Re-exploring Molecular Complexity with ALMA: Insights into chemical differentiation from the molecular composition of hot cores in Sgr B2(N2)

A. Belloche; R. T. Garrod; H. S. P. Müller; N. J. Morin; S. A. Willis; K. M. Menten

doi:10.1051/0004-6361/202554411

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A&A, 698 (2025) A143

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Issue		A&A Volume 698, June 2025


Article Number		A143
Number of page(s)		195
Section		Interstellar and circumstellar matter
DOI		https://doi.org/10.1051/0004-6361/202554411
Published online		19 June 2025

A&A, 698, A143 (2025)

Re-exploring Molecular Complexity with ALMA: Insights into chemical differentiation from the molecular composition of hot cores in Sgr B2(N2)

A. Belloche¹^★, R. T. Garrod², H. S. P. Müller³, N. J. Morin⁴, S. A. Willis⁴ and K. M. Menten¹^,★★

¹ Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, 53121 Bonn, Germany
² Departments of Chemistry and Astronomy, University of Virginia, Charlottesville, VA 22904, USA
³ Astrophysik/I. Physikalisches Institut, Universität zu Köln, Zülpicher Str. 77, 50937 Köln, Germany
⁴ Department of Chemistry, University of Virginia, Charlottesville, VA 22904, USA

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 7 March 2025
Accepted: 29 April 2025

Abstract

Context. Hot molecular cores correspond to the phase of star formation during which many molecules, in particular complex organic molecules (COMs), thermally desorb from the surface of dust grains. Sophisticated kinetic models of interstellar chemistry describe the processes that lead to the formation and subsequent evolution of COMs in star-forming regions.

Aims. Our goal is to derive the chemical composition of hot cores in order to improve our understanding of interstellar chemistry. In particular, we want to test the models by comparing their predictions to the observed composition of the gas phase of hot cores.

Methods. We used the Atacama Large Millimeter/submillimeter Array (ALMA) to perform an imaging spectral line survey of the high mass star-forming region Sagittarius B2(N) at 3 mm, called Re-exploring Molecular Complexity with ALMA (ReMoCA). We modeled under the assumption of local thermodynamic equilibrium the spectra obtained with this survey toward the sources embedded in the secondary hot core Sgr B2(N2). We compared the chemical composition of these sources to that of sources from the literature and to predictions of the chemical kinetics model MAGICKAL.

Results. We detected up to 58 molecules toward Sgr B2(N2)’s hot cores, including up to 24 COMs, as well as many less abundant isotopologs. The compositions of some pairs of sources are well correlated, but differences also exist, in particular for HNCO and NH₂CHO. The abundances of series of homologous molecules drop by about one order of magnitude at each further step in complexity. The nondetection of radicals yields stringent constraints on the models. The comparison to the chemical models confirms previous evidence of a high cosmic-ray ionization rate in Sgr B2(N). The comparison to sources from the literature gives a new insight into chemical differentiation. The composition of most hot cores of Sgr B2(N2) is tightly correlated to that of the hot core G31.41+0.31 and the hot corino IRAS 16293–2422 B after normalizing the abundances by classes of molecules (O-bearing, N-bearing, O+N-bearing, and S-bearing). There is no overall correlation between Sgr B2(N2) and the shocked region G+0.693−0.027 also located in Sgr B2, and even less with the cold starless core TMC-1. The class of N-bearing species reveals the largest variance among the four classes of molecules. The S-bearing class shows in contrast the smallest variance.

Conclusions. These results imply that the class of N-bearing molecules reacts more sensitively to shocks, low-temperature gas phase chemistry after nonthermal desorption, or density. The overall abundance shifts observed between the N-bearing and O-bearing molecules may indicate how violently and completely the ice mantles are desorbed.

Key words: astrochemistry / line: identification / ISM: molecules / ISM: individual objects: Sagittarius B2(N)

^★★

This article is dedicated to the memory of Karl Menten who suddenly passed away shortly before the completion of this work. He originally motivated the Sgr B2 spectral line surveys performed at the MPIfR which started with the IRAM 30 m telescope in 2004. We are deeply grateful for the numerous inspiring discussions that we had the privilege to have with him over the past two decades, in particular about star formation and astrochemistry. We miss his enthusiasm, his scientific curiosity and creativity, as well as his open and generous personality.

© The Authors 2025

Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article is published in open access under the Subscribe to Open model.

Open Access funding provided by Max Planck Society.

1 Introduction

Molecular clouds are the birthplaces of stars and their planetary systems. In these interstellar environments, star and planet formation results from an intricate interplay between physical and chemical processes. This work focuses on the chemistry that occurs in star-forming regions, in particular on the formation and evolution of complex organic molecules (COMs), which are defined as carbon bearing molecules that contain at least six atoms (Herbst & van Dishoeck 2009). Interstellar chemistry proceeds in both the gas phase and the solid phase, the latter corresponding to the surface and icy mantles of dust grains. While both phases are involved in the formation and evolution of COMs (e.g., Herbst & Leung 1986; Charnley et al. 1992; van Dishoeck & Blake 1998; Garrod et al. 2008), the major role of the solid phase in the production of COMs has become more obvious in the past decade, in particular since the detection of gas-phase saturated COMs at low temperatures in prestellar cores and protostellar envelopes (e.g., Öberg et al. 2010; Bacmann et al. 2012; Vastel et al. 2014) and, more recently, with the identification of several COMs in interstellar ices with the James Webb Space Telescope (JWST, e.g., Yang et al. 2022; Rocha et al. 2024; Nazari et al. 2024). While earlier kinetic models of interstellar chemistry relied on the increased mobility of radicals at the surface of dust grains during the warm-up phase of protostellar evolution to explain the production of many COMs (e.g., Garrod & Herbst 2006; Garrod et al. 2008), additional nondiffusive processes efficient at low temperatures on dust grains have been added to these models in order to account for the early formation of COMs in star-forming regions (e.g., Jin & Garrod 2020; Garrod et al. 2022).

One of our goals is to obtain observational constraints on the chemical and physical processes that play a role in the emergence of molecular complexity in star-forming regions. One way to achieve this is to test chemical models such as those mentioned above by comparing their predictions to the post-desorption chemical composition of the gas phase of hot molecular cores in star-forming regions.

Hot cores correspond to the dense and compact regions around nascent high-mass protostars where spectral emission from COMs is detected, which is associated with molecular desorption from dust grains upon heating by the central object (e.g., Walmsley et al. 1995). They have typical kinetic temperatures of 150−200 K. Their lifetime is on the order of 6 × 10⁴ yr (e.g., Bonfand et al. 2017; Nony et al. 2024). In low mass star-forming regions, these objects are called hot corinos (Ceccarelli 2004). The chemical composition of hot cores can be determined by analyzing their spectrum in the millimeter/submillimeter wavelength range where molecules have most of their rotational transitions. With the advent of broadband instrumentation at single-dish telescopes and interferometers over the past two decades, spectral line surveys of multiple sources covering a broad frequency range have become more affordable (see, e.g., the review by Jørgensen et al. 2020). This means that the predictions of chemical models can be tested by determining the abundances of a large number of COMs for sample of sources that are located in different environments, have various masses and luminosities, or are in different evolutionary stages.

This work focuses on hot cores embedded in Sagittarius (Sgr) B2. This molecular cloud complex is located in the central molecular zone of our Galaxy, at a projected distance of about 100 pc from the galactic center, Sgr A*. Sgr B2 consists of several protoclusters, in particular the high mass star-forming region Sgr B2(N) that contains a population of hot cores and more evolved H II regions (e.g., Gaume et al. 1995; Schmiedeke et al. 2016; Bonfand et al. 2017; Sánchez-Monge et al. 2017). Since the 1970s, thanks to its high H₂ column density, Sgr B2 has been a place of choice to search for interstellar molecules, in particular COMs (e.g., Ball et al. 1970; Rubin et al. 1971; Hollis et al. 2000; Belloche et al. 2008, 2022). It has been the target of multiple spectral line surveys, starting with single-dish telescopes in the 1980s and 1990s (e.g., Cummins et al. 1986; Turner 1989; Nummelin et al. 1998). Our own spectral line survey of Sgr B2(N) with the IRAM 30 m telescope at 3 mm, 2 mm, and 1.3 mm led to the detection of several new COMs (Belloche et al. 2008, 2009). Despite the limited angular resolution (halfpower beam width, HPBW, ∼25^′′), we were able to identify the presence of two hot cores in Sgr B2(N) within the single-dish beam thanks to their different systemic velocities (Belloche et al. 2013). Our analysis of the IRAM 30 m spectrum showed that the spectral confusion, exacerbated by the presence of these two velocity components, was much higher at 1.3 mm than at 3 mm and that the opacity of the dust made it more difficult to derive reliable COM column densities at 1.3 mm in this source.

In order to make further progress with the determination of the chemical composition of Sgr B2(N)’s hot cores, we took advantage of the advent of the Atacama Large Millimeter/submillimeter Array (ALMA) in the early 2010s to perform an imaging spectral line survey of Sgr B 2(N) at 3 mm. Thanks to its angular resolution of ∼1.6^′′, this survey, called Exploring Molecular Complexity with ALMA (EMoCA), resolved the two main hot cores into separate sources which we called Sgr B2(N1) and Sgr B2(N2) (Belloche et al. 2016). This gain in angular resolution reduced the spectral confusion considerably. The resolution was however still insufficient to resolve the internal structure of both hot cores, and the high dust optical depth of the main hot core, Sgr B2(N1), still made it difficult to explore its chemical composition even at 3 mm. This motivated a second high-sensitivity, high-angular-resolution (∼0.5^′′) survey with ALMA, called Re-exploring Molecular Complexity with ALMA (ReMoCA) (Belloche et al. 2019). This survey allowed us to resolve the thermal structure of Sgr B2(N1), probe the transition between nonthermal and thermal desorption, and establish that a number of COMs co-desorb thermally with water in this source (Busch et al. 2022). Our series of spectral line surveys of Sgr B2(N) has demonstrated that a high angular resolution is key to reduce the spectral confusion of high mass star-forming regions by probing individual sources and pockets of gas with narrower line widths (Belloche et al. 2022).

In this article, we focus our attention on the secondary hot core Sgr B2(N2), which ReMoCA resolves into several sources, and we derive their individual chemical compositions. Möller et al. (2025) recently used ALMA at 1.2 mm to study the whole population of hot cores in Sgr B2(N) and Sgr B2(M). They derived the chemical composition of several dozen sources, albeit on the basis of a limited number of molecules, and focused their analysis on evaluating the evolutionary stages of the sources. Thanks to the lower degree of spectral confusion at 3 mm, we report here the detection of a much larger set of molecules and investigate the constraints that these observational results impose on chemical models. This article is structured as follows. We describe the observational setup and the method used to identify the molecules and measure their column densities in Sect. 2. Section 3 presents the results of this analysis and a comparison to other sources from the literature. In Sect. 4, we compare the observational results with the predictions of chemical models. We discuss the results obtained in this work in Sect. 5. Our conclusions are reported in Sect. 6.

2 Observations and radiative transfer modeling

2.1 ALMA observations

The imaging spectral line survey ReMoCA was performed with ALMA toward Sgr B2(N) at high angular resolution in the 3 mm atmospheric window between 84.1 and 114.4 GHz. The observations and data reduction were described in detail in Belloche et al. (2019). We summarize here the main features. The survey was performed in five parts that were tuned to different frequencies with a spectral resolution of 488.3 kHz(1.7−1.3 km s⁻¹). We called these five spectral setups S1–S5. Each setup consists of four 1.88−GHz wide spectral windows labeled W0-W3 that together cover both sidebands. The five setups were observed on different days with various antenna configurations (see Table 1 of Belloche et al. 2019), which resulted in different angular resolutions. The spectral coverage, angular resolution, and noise level of each spectral window are listed in Table 1, which is slightly updated compared to the initial table published in Belloche et al. (2019). The survey has a median angular resolution of 0.6^′′, which corresponds to 4900 au at the distance of Sgr B2 (8.2 kpc, Reid et al. 2019). The median rms noise level is 0.8 mJy beam⁻¹, which corresponds to a brightness temperature noise level of 0.27 K for a half-power beam width (HPBW) of 0.6^′′ at a frequency of 100 GHz.

Table 1

Beam sizes and noise levels.

Because of the high spectral line density, which translates into spectra close to the confusion limit, and the presence of several sources with different systemic velocities in the field of view, splitting the line and continuum emissions in the Fourier plane was not possible. This was done instead in the image plane as described in Belloche et al. (2019). This splitting was subsequently slightly improved, as we reported in Melosso et al. (2020a).

The primary beam of the ALMA 12 m antennas has a HPBW that varies between 69^′′ at 84 GHz and 51^′′ at 114 GHz (Remijan et al. 2015). The data cubes of the ReMoCA survey were not corrected for the primary beam response. However, this correction was applied to all spectra used to derive column densities and those displayed in the figures of this article. Figures showing maps are not corrected for the primary beam response. This has the advantage of keeping the noise level uniform. In these maps, the longest (shortest) angular distance to the phase center is 6.8^′′(0.5^′′), which corresponds to a correction factor of 1.05 (1.0003) at 84 GHz and 1.03 (1.0001) at 114 GHz. The distortion of the maps is thus marginal (smaller than 5%).

2.2 Radiative transfer modeling

We modeled the ReMoCA spectra under the assumption of local thermodynamic equilibrium (LTE), which is well justified given the high densities that characterize the hot cores in Sgr B2(N) (>1 × 10⁷ cm⁻³, Bonfand et al. 2019). We used the astronomical software Weeds (Maret et al. 2011), which is part of the CLASS program of the GILDAS package¹, to compute synthetic spectra. Weeds accounts for the finite angular resolution of the observations, the line optical depth, and the contribution of the background continuum emission to the equation of radiative transfer (see the Weeds documentation² for more details). We emphasize that neglecting the strong continuum emission of hot cores in the equation of radiative transfer would lead to underestimating the molecular column densities.

We modeled the contribution of each molecule separately before adding them together. The synthetic spectrum of each species is defined by five parameters: size of the emitting region (θ_s), column density (N), temperature (T_rot), line width (ΔV), and velocity offset (V_off) with respect to the assumed systemic velocity of the source. Many molecules contribute to the ReMoCA spectra with rotational transitions that originate not only from their vibrational ground state but also from various vibrationally excited states. In those cases, we modeled each vibrational state separately in order to be able to account for potential differences between vibrational and rotational temperatures.

When the vibrational and rotational temperatures of a given molecule are equal, the column density parameters N of all vibrational states of that molecule are identical in our Weeds models. This value corresponds to the total column density of the molecule, that is it includes the populations of the ground state and all vibrationally excited states. On the contrary, if the vibrational temperature of a given vibrational state V is higher than the rotational temperature because of, for example, radiative pumping, then, in order to fit the observed spectrum, the column density parameter N_V of that state would have to be artificially increased to account for the overpopulation in that state. In such a case, the calculation of the total column density of the molecule would be tricky. In practice, we did not find any difference between the vibrational and rotational temperatures of the detected molecules.

We decided to keep the original angular resolutions of the 20 ReMoCA spectral windows despite their differences rather than degrade the data to the coarsest angular resolution (0.8^′′). The reason for this is that the shortest separation between the sources studied in this article is 0.55^′′. Such a small angle is resolved by setups S4 and S5 but not completely by the other setups. The LTE parameters derived for each source were optimized as much as possible on S4 and S5. Part of the discrepancies between the synthetic and observed spectra of setups S1–S3 result from their coarser angular resolution that leads to partial contamination between neighboring sources, an effect that cannot be accounted for by our simple modeling framework.

2.3 Spectroscopy

Table C.1 provides the list of the 448 spectroscopic entries that were used in this work to model the observed spectra or derive upper limits to the column densities of nondetected molecules. These spectroscopic entries were downloaded from the Cologne Database for Molecular Spectroscopy (CDMS³, Müller et al. 2001, 2005; Endres et al. 2016), the Lille Spectroscopic Database (LSD⁴), and the database for molecular spectroscopy of the Jet Propulsion Laboratory (JPL⁵, Pickett et al. 1998). They were all inserted into a local database connected to the radiative transfer software Weeds. In addition to these publicly available entries, our database also includes predictions that were provided to us by various collaborators over the past two decades and a few entries that we prepared ourselves on the basis of published studies.

Spectroscopic entries in all three public databases mentioned above have a specific five- or six-digit index called the tag. The third-to-last digit of the tag codes for the database (0 for JPL, 5 for CDMS, and 8 for LSD). The two or three digits that precede the database code represent the molecular weight of the molecule (e.g., 32 for CH₃ OH or 103 for c-C₆ H₅CN). The last two digits distinguish molecules in the same database that have the same molecular weight. For a given molecule, we modeled the emission of vibrational excited states separately in order to account for potential deviations from LTE in the event that the vibrational temperature would be different from the rotational one (see Sect. 2.2). To do that, we had to split the original entries that contain several vibrational states in the public databases into multiple entries containing one state each, and we assigned new tags to the individual entries, with a database code different from 0 or 5. Unfortunately, we had used the database code 8 for some of our local entries before the LSD database was created. We have started to change these tags when new LSD entries are in conflict with our existing local entries. Some CDMS entries are provided with hyperfine structure in the documentation area of the CDMS website with no specific tag. Here again, we had to assign new tags to these CDMS entries with hyperfine structure.

We made a considerable effort to estimate a posteriori corrections to the column densities of the analyzed molecules when their partition functions did not include, or only partially, the contribution of vibrationally excited states or higher energy conformers. In most cases, the vibrational correction (C_vib) was computed in the harmonic approximation using the following equation: $C_{vib} = \prod_{i = 1}^{N} \frac{1}{1 - e^{- E_{i} / k T}},$ $Mathematical equation: $C_{\text{vib}}=\prod_{i=1}^{N} \frac{1}{1-e^{-E_{i} / k T}},$$ (1)

with k the Boltzmann constant, T the temperature, and E_i the energies of the N fundamental modes of vibration. In most cases, the conformational correction (C_conf) was computed assuming that a conformer j of energy E_j with respect to the energy of the lowest-energy conformer contributes to the total partition function of the molecule with a population equal to e^−E_j/kT times the population of the lowest-energy conformer. We also accounted for the degeneracies of the conformers when they differed. For each spectroscopic entry, Table C.1 provides the chemical formula of the molecule, along with the vibrational state or name of the conformer when relevant, the tag, the database where it was downloaded from, and a list of relevant references (“Spectro” for the frequencies and dipole moment, “C_vib” for the vibrational energies, and “C_conf” for the energies of the conformers).

3 Results

3.1 Source selection and description

In this work, we focus our analysis on four positions located in Sgr B2(N2), the secondary hot core of Sgr B2(N). The first three positions correspond to the continuum sources AN06, AN03, and AN02 extracted by Sánchez-Monge et al. (2017) from their observations of Sgr B2(N) performed with ALMA in the 1.2 mm atmospheric window with an angular resolution of 0.4^′′. These three sources represent all the continuum sources within Sgr B2(N2) in the list of Sánchez-Monge et al. (2017). Their spectral index between 211 and 275 GHz was found to be 2.4, 3.2, and 3.1, respectively, suggesting that their 1.2 mm ALMA continuum emission is dominated by dust emission. In addition to these three sources, we also analyzed the position called Sgr B2(N2b) where we recently detected normalpropanol and iso-propanol (Belloche et al. 2022). The spectral index of this position in the 1.2 mm spectral window is approximately 3 according to Fig. 7 of Sánchez-Monge et al. (2017). This suggests that the 1.2 mm ALMA continuum emission of N2b is dominated by dust as well. The coordinates and 1.2 mm spectral indices of all selected positions are listed in Table 2. We describe below the additional information that we derived from the ReMoCA survey on the physical structure or stage of evolution of these four sources.

Table 2

Properties of the positions analyzed within Sgr B2(N2).

3.1.1 AN03, a HCH II region

AN03 coincides with the hypercompact (HC) H II region K7 that was identified by De Pree et al. (2015) in Very Large Array (VLA) continuum observations at 7 mm. K7 is located at a J2000 equatorial position of 17^h47^m 19^s.895, −28^∘22^′ 13^′′.47, with uncertainties of 0.01 s and 0.1^′′, and has a deconvolved full-width-at-half-maximum (FWHM) size of ∼0.08^′′ at 7 mm (660 au at the distance of Sgr B2). Several recombination lines of hydrogen were covered by the ReMoCA survey and detected toward Sgr B2(N2). Figure 1 shows the ReMoCA spectrum at the frequencies of the H40α and H41α recombination lines toward the four positions of Sgr B2(N2) analyzed in this article. We selected these lines because they suffer little from contamination by rotational emission of molecules (see the molecular contribution predicted by our LTE model overlaid in blue in Fig. 1). In order to investigate the spatial morphology of the emission of these recombination lines in Sgr B2(N2), we integrated each line over a fixed velocity range, as highlighted in dark gray in Fig. 1. The resulting integrated intensity maps are shown in Fig. 2. The H40α line was independently covered by two setups with differing angular resolutions (S1W3 and S5W0). Figures 1 and 2 show the spectra and maps for both setups in order to illustrate the impact of the angular resolution.

The maps of Fig. 2 reveal a clear peak of H α emission at a J2000 equatorial position of 17^h47^m 199^s.899, −28^∘22^′ 13^′′.59 with an uncertainty of ∼0.1^′′, as measured in the S5W0 map which has the highest angular resolution (HPBW 0.43^′′ × 0.30^′′). This H α emission peak coincides within the uncertainties with the positions of the 1.2 mm continuum source AN03 and the 7 mm HCH II region K7. Figure 2 also displays the positions of the water and methanol masers observed with the VLA by McGrath et al. (2004) and Lu et al. (2019), respectively. AN03 seems to be associated with the easternmost water maser which has a systemic velocity of 75.3 km s⁻¹. Their angular separation is on the order of 0.15′′ (∼1200 au).

The recombination line maps of Fig. 2 also show an arc of extended emission that corresponds to K5, a large shell of ionized gas seen in the VLA continuum maps of Gaume et al. (1995) at 1.3 cm. The fact that the morphology of this extended structure is very similar in all three maps of Fig. 2 gives us confidence that the maps suffer little from contamination by molecular emission.

Fig. 1

ALMA continuum-subtracted spectra toward the hot core positions AN06, AN03, AN02, and N2b (from left to right) at the frequencies of the hydrogen recombination lines H40α (top and middle rows, for two setups with different angular resolutions) and H41α (bottom row). In each panel, the vertical dashed line marks the systemic velocity of the source adopted for the LTE modeling of the molecular emission. The velocity axis refers to the rest frequency of the recombination line. The horizontal dashed line indicates the 3σ noise level. The blue spectrum represents the LTE model that includes the contribution of all molecules identified so far. The velocity range highlighted in dark gray is specific to each recombination line and represents the range of channels selected to compute the integrated intensity map of each recombination line shown in Fig. 2. The setup and spectral window are indicated in the left panel of each row along with the corresponding HPBW.

Fig. 2

ALMA integrated intensity maps of the hydrogen recombination lines H41α (panel a) and H40α (panels b and c, for two setups with different angular resolutions). The intensity was integrated over the velocity range highlighted in dark gray in Fig. 1 in order to avoid contamination by molecular lines. The violet plus symbols mark the hot core positions AN06, AN03, AN02, and N2b. The green cross indicates the VLA position of the HCH II region K7 at 7 mm from De Pree et al. (2015). The blue triangles and violet square indicate the VLA positions of the water and Class II methanol masers reported by McGrath et al. (2004) and Lu et al. (2019), respectively. The setup and spectral window numbers are given in the bottom right corner of each panel along with the associated beam size (HPBW). The values of the noise level, σ, are 18, 21, and 15 mJy beam ⁻¹ km s⁻¹, respectively. The contours start at 3σ (brown contour) and then increase by a factor of two at each step (black contours). The dotted blue contour, when present, shows the −3σ level.

Fig. 3

ALMA continuum emission maps at 92.3, 98.9, and 99.6 GHz. The setup and spectral window numbers are given in the bottom right corner of each panel along with the associated beam size (HPBW). The symbols are the same as in Fig. 2. The values of the noise level, σ, are 0.73, 0.97, and 0.42 mJy beam ⁻¹, respectively. The contours start at 5σ (brown contour) and then increase by a factor of two at each step (black contours). The additional violet contour in panel c is at 60σ. It was added to emphasize the emission peak on AN02. Dotted blue contours, when present, indicate the −5σ and −10σ levels.

3.1.2 AN06, an isolated dust continuum source

Figure 3 shows the maps of continuum emission measured in the same ReMoCA spectral windows (S3W0, S1W3, and S5W0) as the recombination lines displayed in Fig. 2. These continuum emission maps trace a similar extended structure as the Hα maps of Fig. 2, indicating that the extended continuum emission detected at 3 mm with ReMoCA contains a significant contribution from thermal free-free emission of ionized gas. However, the contours of the highest angular resolution map (S5W0, Fig. 3c) clearly reveal 3 mm continuum peaks associated with the 1.2 mm continuum sources AN02 and AN06. This indicates that the compact 3 mm continuum emission around each source has a significant contribution from dust emission. AN06 is thus a bona fide dust continuum source that is not associated with any HCH II region because it has no compact counterpart in the ReMoCA maps of Hα emission and in the VLA maps of thermal free-free emission of De Pree et al. (2015).

The ReMoCA spectra of AN06 reveal for most molecules that we analyzed two velocity components that are clearly distinct. This is illustrated in Fig. 4 with two rotational transitions of ethanol that were carefully selected for their absence of contamination by other molecules. We divided the ethanol emission into three velocity ranges highlighted with the three darkest shades of gray in Fig. 4. The integrated intensity maps corresponding to these three velocity ranges are shown in Fig. 5. While the maps in the velocity ranges 71−77 km s⁻¹ and 77−81 km s⁻¹ do not show any particular structure toward AN06 (Figs. 5b, c, e, and f), this source is clearly associated with a compact emission peak in the ethanol maps integrated from 65 to 71 km s⁻¹ (Figs. 5a and d). Therefore, we think that the dust continuum emission of AN06 is associated with the lower-velocity molecular component and we adopted a systemic velocity of 68.3 km s⁻¹ for this source. The higher velocity component detected toward AN06, at a systemic velocity of about 73.5 km s⁻¹ and with lower peak intensities than the lower velocity component, traces the edge of the Sgr B2(N2) dense core and is likely not related to AN06 itself (Figs. 5b and e). Both components have intrinsic line widths of about 3.5 km s⁻¹. We conclude that AN06 is a bona fide continuum source that is likely not embedded in Sgr B2(N2) but rather lies in either the foreground or background of Sgr B2(N2).

3.1.3 AN02, a dust continuum source with two velocity components, possibly driving an outflow

AN02 is associated with a 3 mm continuum peak in Fig. 3c and has no compact counterpart in Hα emission at 3 mm (Fig. 2) or thermal free-free emission at 7 mm (De Pree et al. 2015). It thus represents a bona fide dust continuum source. Like for AN06, our LTE modeling of the molecular emission of AN02 required two velocity components to fit the asymmetric shape of the lines (see, e.g., Figs. 4c and g). However, with a velocity difference of only ∼4 km s⁻¹ and intrinsic line widths of about 4 and 3 km s⁻¹, these two components are not as well separated as in the case of AN06. They have similar peak temperatures and both have a local maximum at the position of AN02 in the integrated intensity maps shown in Fig. 5. Therefore, the data do not tell us whether the continuum source AN02 is associated with both components or only one of them. We assumed a systemic velocity of 72.9 km s⁻¹ for AN02, which corresponds to the lower velocity component, but modeled both components together with velocity offsets of ∼0 and ∼4 km s⁻¹.

Four water masers lie in the vicinity of AN02 toward the northwest, at angular distances of about 0.25^′′ and 0.6–0.7^′′. They are roughly aligned with AN02, suggesting that AN02 may drive an outflow that produces these masers. The easternmost maser, which we associated with AN03 in Sect. 3.1.1, is located at a position that is roughly symmetric to the position of the westernmost masers with respect to AN02. Therefore, the easternmost maser may equally well be associated with AN02 rather than AN03. The three westernmost masers have velocities of 75.3, 74.0, and 21.2 km s⁻¹ (McGrath et al. 2004). The former velocities are both in between the velocities of the two molecular components associated with AN02 while the latter velocity is blueshifted by about −50 km s⁻¹. The closest maser to AN02 has a velocity of 56.5 km s⁻¹ that is blueshifted by about −16 km s⁻¹.

A Class II methanol maser was detected by Lu et al. (2019) with the VLA in the vicinity of Sgr B2(N2) (see violet square in Fig. 3). It is located about 1^′′ to the northwest of AN02. Mills et al. (2018) reported an astrometry offset affecting their VLA A-configuration data due to an error in the position of their phase calibrator in the VLA catalog. Such an offset may also affect the data set of Lu et al. (2019) which was calibrated with the same phase calibrator (X. Lu, priv. comm.), but the offset does not seem to be along the northwest direction (A. Ginsburg, priv. comm.). Therefore the source associated with the methanol maser is currently unknown.

We conclude that AN02 is a dust continuum source with two velocity components of molecular emission. This source is possibly driving an outflow along the northwest-southeast direction.

Fig. 4

ALMA continuum-subtracted spectra toward the hot core positions AN06, AN03, AN02, and N2b (from left to right) at the frequencies of two contamination-free rotational transitions of ethanol indicated on the right along with the energy of the upper level in temperature unit. In each panel, the vertical dashed line marks the systemic velocity of the source adopted for the LTE modeling of the molecular emission. The velocity axis refers to the rest frequency of the ethanol transition. The horizontal dashed line indicates the 3σ noise level. The blue spectrum represents the LTE model that includes the contribution of all molecules identified so far. The red spectrum represents the LTE model of ethanol only. In the case of AN06, the synthetic ethanol spectra of the first and second velocity components are displayed in orange and red, respectively. The velocity ranges highlighted in the three darkest shades of gray are specific to each ethanol transition (but common to all sources, albeit with slight differences due to the finite spectral sampling) and represent the ranges of channels selected to compute the integrated intensity maps of each transition shown in Fig. 5. The setup and spectral window are indicated in the left panel of each row along with the corresponding HPBW.

Fig. 5

ALMA integrated intensity maps of two contamination-free rotational transitions of ethanol with rest frequency and upper level energy indicated on the right of each row. The intensities were integrated over the velocity ranges highlighted in the three darkest shades of gray in Fig. 4. The integration range is indicated in the top left corner of each panel. The setup and spectral window numbers are given in the bottom right corner along with the associated beam size (HPBW). The symbols are the same as in Fig. 2. The noise level, σ, is indicated below the velocity range. The black contours start at 3σ and then increase by a factor of two at each step, except for the last contour of panel e which is at 320σ. The dotted blue contour, when present, indicates the −3σ level.

Fig. 6

Maps of centroid velocity (left column) and line width (right column) of two contamination-free rotational transitions of ethanol with rest frequency and upper level energy indicated in the top left corner of each row. The kinematic information is plotted only for pixels with a signal-to-noise ratio in peak intensity higher than 10. The setup and window numbers are given in the bottom right corner of each panel along with the associated beam size (HPBW). The symbols are the same as in Fig. 2. These two transitions of ethanol have opacities on the order of 2 in N2b, which means that the intrinsic line widths around N2b are somewhat smaller than the plotted values.

3.1.4 N2b, a dust continuum source with narrow lines

Figure 6 shows maps of centroid velocity and line width obtained by fitting a Gaussian function to the spectra of both transitions of ethanol used in Figs. 4 and 5. Figures 6b and d reveal that the line width decreases toward the southwest of Sgr B2(N2), reaching values as low as ∼2.7 km s⁻¹. Such narrow lines are advantageous for the search for new molecules but the narrowest line widths are reached at the edge of the detected molecular emission where the emission becomes faint (see Fig. 5). As a compromise between having narrow line widths and keeping high intensities, we selected position N2b for our previous work on propanol (Belloche et al. 2022). This position has intrinsic line widths on the order of 3.5 km s⁻¹ and we adopted a systemic velocity of 74.2 km s⁻¹. Figures 5b and e show that N2b is associated with a peak of integrated intensity of ethanol. It also corresponds to an extension of 3 mm continuum emission in Fig. 3c, which shows the map at highest angular resolution, while no such clear extension is visible in the H40α map of Fig. 2. This suggests that N2b may be associated with a compact dust core like the other three sources, even if it was not extracted as such by Sánchez-Monge et al. (2017). Their ALMA dust continuum map at 242 GHz also shows an extension at the position of N2b (see their Fig. 4).

Figure 6 shows other features that are consistent with the source descriptions provided in the previous sections. The blue area toward AN06 in panels a and c reveals the low velocity component that we exclusively assigned to AN06 and that dominates its emission. The overlap along the line of sight between this velocity component and the dense core of Sgr B2(N2) results in the sharp line width increase (∼8 km s⁻¹) at the location of the red band that touches AN06 in panels b and d. The existence of two velocity components toward AN02 also results in a local increase in the line width (∼7 km s⁻¹) and a slightly higher average centroid velocity (∼75 km s⁻¹) than the rest of Sgr B2(N2), which has a systemic velocity of ∼74 km s⁻¹. Finally, an unresolved pocket of gas with a systemic velocity of ∼75 km s⁻¹ stands out at a bit less than 1^′′ to the south of AN03. This region is not associated with any specific structure in the Hα and continuum maps shown in Figs. 2 and 3, therefore it is difficult to assess its nature, but it seems to be associated with a filamentary structure that is connected to AN02 in the ethanol maps integrated over the 77−81 km s⁻¹ velocity range (Figs. 5c and f).

3.2 Chemical composition

The LTE parameters of all the molecules included in the model of at least one of the four positions presented in Sect. 3.1, obtained following the method described in Sect. 2.2, are listed in Tables E.1–E.5. For a given catalog entry, each table indicates the number of spectral lines that we considered as detected, namely lines with a peak temperature above the 3σ noise level and for which the LTE model of this entry accounts for at least about two thirds of the detected signal. Exceptions to the latter criterion occur for the setups with the largest beams (e.g., S1) for which the LTE models, which were optimized for the setups with the highest angular resolution (S4 and S5), sometimes clearly underestimate the emission in the larger beams due to the nonuniformity of the molecular emission across the Sgr B2(N2) region. The excellent match between the LTE models and the ReMoCA spectra of N2b, AN02, AN03, and AN06 is illustrated in Fig. D.1 that also displays the ReMoCA spectrum of the ultracompact H II region K4 for comparison. K4 is located about 9^′′ to the north of Sgr B2(N2).

Species that are considered as securely detected are labeled with a “d” in the third column of Tables E.1–E.5. The number of lines required to qualify a catalog entry as detected depends on the signal-to-noise ratio of the lines and whether or not other related entries (e.g., other isotopologs or vibrational states) are detected with consistent parameters. Entries that are considered as tentatively detected with only a few lines are labeled with a “t.” Entries that are included in the LTE model because their predicted lines have peak temperatures close to or above the 3σ level but are heavily contaminated by emission or absorption of other species, and therefore cannot be considered as securely or tentatively identified, are labeled with a “c” to indicate that they merely contribute to the detected signal. They correspond typically to higher-energy vibrational states that are modeled with the same parameters as the lower-energy ones, or rarer isotopologs that are modeled with the parameters of the main species assuming a typical isotopic ratio. Species that are not detected are labeled with a “n” and the tables provide upper limits to their column densities.

Table 3 provides a concise overview of the detections obtained with the ReMoCA survey toward N2b, AN02, AN03, AN06, and AN06c2. This table contains 61 molecules. Thanks to the sensitivity of ALMA and the fact that the spectral confusion at 3 mm is not too severe, we were able to identify between 42 and 58 molecules (counting only the main isotopologs). Among these molecules, between 3 and 10 are detected only tentatively and between 11 and 14 are seen only in absorption. Furthermore, many COMs are detected: between 22 and 24 toward N2b, AN02, and AN03, and about 15 toward AN06 and AN06c2.

In addition to the molecules included in the LTE model of at least one of the four positions, we provide upper limits to the column density of 146 relevant molecules that were searched for in the ReMoCA survey but not detected (Tables E.6–E.10). The upper limits were obtained assuming the same LTE parameters as related molecules that were detected or typical LTE parameters of the respective source, keeping only the column density as a free parameter. The molecules are grouped by classes (O-bearing, O+N-bearing, N-bearing, S-bearing, S+O-bearing, S+N-bearing, Cl-bearing, P-bearing, and hydrocarbons) and, within each class, by increasing entry index (tag) in our local Weeds database.

The column densities of 46 molecules extracted from Tables E.1–E.5 and normalized to the column density of methanol are displayed in Fig. 7. We normalized to methanol because it is the most abundant detected COM and because deriving reliable H₂ column densities is challenging. In order to compare the chemical compositions of the selected positions more easily, we show their abundances (relative to methanol) normalized to the abundances of N2b in Fig. F.1.

Table 3

List of molecules detected toward N2b, AN02, AN03, AN06, and AN06c2 with the ReMoCA survey.

Fig. 7

Column densities derived toward N2b, AN02, AN03, and AN06 with our LTE modeling, normalized to the column density of methanol (see Tables E.1–E.5). The column density of methanol is indicated in the top left corner of each panel. The chemical compositions of the two velocity components detected toward AN06 are displayed separately (AN06 in the top panel and the second velocity component in the panel labeled AN06c2). The panel of AN02 reports the sum of the column densities of its two velocity components. Hatched bars show tentative detections while empty bars with downward arrows indicate upper limits. Molecules with status “c” in Tables E.1–E.5 are here represented as upper limits. The dashed lines indicate levels of 1% and 0.01% with respect to methanol.

3.3 Correlations of chemical composition between positions

Figure 8 shows correlation plots of the column densities normalized to methanol for various pairs of positions. The best correlation occurs between the chemical compositions of AN02 and AN03 (Fig. 8e). Most data points deviate by much less than a factor of 2 from the 1:1 relation. The only data points that deviate by slightly more than a factor of 2 are HC₃N, HC₅N (upper limit toward AN02), and SO₂. C₂H₃CN, SO, and HNCS deviate by slightly less than a factor of 2. All six molecules are less abundant toward AN02 than toward AN03. The upper limits derived for HCCNC, HSCN, and E-HNCHCN toward AN02 and for n- and i−C₃H₇OH, and C₂H₅OCHO, toward both AN02 and AN03 are not stringent enough to tell whether or not these molecules deviate from the overall correlation.

The next pair of positions that show a high degree of correlation are the two velocity components of AN06 (Fig. 8h). In this case, however, the abundances (relative to methanol) of the second velocity component (AN06c2), which traces the gas at the edge of the dense core that contains AN03 and AN02, are systematically higher than the abundances of the main component (AN06), by roughly a factor of 2. There is one notable exception to the overall correlation: NH₂CHO is remarkably underabundant toward AN06, with an abundance upper limit that is nearly two orders of magnitude below the abundance measured toward AN06c2. HNCO is offset on the same side of the overall correlation, but only by a factor of about 4, while HC₃N, SO₂, and CH₃CCH are offset by a factor slightly larger than 2.

In contrast to the previous two pairs, the chemical composition of N2b correlates poorly with that of the other positions (Figs. F.1 and 8a−d). Most molecules have abundances relative to methanol distributed between the 1:1 and 10:1 lines in Figs. 8a and b, the abundances toward AN02 and AN03 being higher than toward N2b. The most extreme deviations from the 1:1 relation, by more than a factor of 20, occur for t-HCOOH (upper limit toward N2b) and NH₂CN.

The correlation between N2b and AN06 is even poorer, with a spread of nearly two orders of magnitude (Figs. F.1a and 8d). Most species are less abundant (relative to methanol) in AN06 than in N2b. The most extreme cases are HNCO and NH₂CHO which are almost two orders of magnitude (or more) less abundant, followed by the cyanides CH₃CN, C₂H₅CN, C₂H₃CN, nand i-C₃H₇CN, and HC₃N (about one order of magnitude). The only molecules that are more abundant in AN06 than in N2b by at least a factor of two are C₂H₅OCHO and CH₃NH₂.

AN06c2 correlates a bit better with N2b than AN06 (Figs. F.1b and 8c). The only species that deviate from the 1:1 relation by significantly more than a factor of 2 are CH₃ NH₂ (more abundant in AN06c2) as well as HNCO and C₂ H₅CN (more abundant in N2b).

Finally the chemical composition of both components of AN06 is poorly correlated with that of AN03 (Figs. 8f and g). All molecules are less abundant in AN06 than in AN03, by up to 2–3 orders of magnitude for HC₃N, C₂H₃CN, HNCO, and NH₂CHO. The situation is less extreme for AN06c2, with a handful of molecules close to the 1:1 relation, most molecules between the 1:1 and 1:10 relations, and only HC₃N, C₂H₃CN, and HNCO being 15–30 times less abundant than in AN03.

In order to investigate if the lack of correlation between certain sources is related to systematic differences between classes of molecules, we show in Fig. F.2 the same correlation plots as in Fig. 8 but with colors coding for the atomic composition of the molecules: O-bearing in black, O+N-bearing in blue, N-bearing in red, pure hydrocarbon in green, and S-bearing in yellow. We notice that the correlations of the pairs N2b/AN02, N2b/AN06c2, N2b/AN06, and AN06/AN06c2 (Figs. F.2a, c, d, and h) are much tighter for the S-bearing molecules than for the full sample of molecules. This is not the case for the pairs of sources that involve AN03 (Figs. F.2b, e, f, and g). Furthermore, the class of O+N-bearing molecules stands out in Figs. F.2d and g: these molecules are located in the lower part of the correlation plot in both cases, revealing that they are underabundant in AN06 compared to N2b and AN03 (and AN02, given that it correlates well with AN03). In contrast to the S-bearing and O+N-bearing species, the O-bearing and N-bearing molecules do not show any obvious systematic differences between the five sources.

To summarize, the pairs of positions in Sgr B2(N2) that show a good correlation of their chemical composition (relative to methanol) are AN02/AN03 and AN06/AN06c2. The molecules that stand out in at least one panel of Fig. 8 are HC₃N, HC₅N, C₂H₃CN, C₂H₅CN, NH₂CN, CH₃NH₂, NH₂CHO, HNCO, t-HCOOH, C₂ H₅OCHO, and SO₂. Finally, AN03 stands out for its S-bearing molecular content, which poorly correlates with the other sources, and AN06 for its underabundant O+N-bearing species compared to N2b, AN03, and AN02 (Fig. F.2).

Fig. 8

Correlation plots of column densities normalized to methanol for various pairs of positions. The x- and y-axes of each panel correspond to the positions written in the bottom right and top left corners, respectively. The color coding of the molecules is the same as in Figs. 7 and F.1 and is indicated on the right. Latine and Greek letters, reported at the same abscissa as the corresponding molecule, were added to facilitate the identification of the data points. Filled data points represent firm and tentative detections while empty circles with arrows indicate upper limits toward at least one position. The dashed and dotted lines indicate deviations by a factor 10 and 2, respectively.

Fig. 9

Abundance of protonated molecules (red) relative to their neutral form (black). Each panel corresponds to one position as labeled in the bottom right corner. Empty squares and downward arrows represent tentative detections and upper limits, respectively. Crosses of the same color represent predictions of the chemical model described in Sect. 4. The molecules are listed along the x axis below and above the bottom and top panels, respectively. The list of protonated molecules is indicated at the bottom right.

Fig. 10

Same as Fig. 9, but for radicals (red) with respect to the hydrogenated form (black).

3.4 Correlations between families of molecules

Figures 9–15 explore the chemical composition of each selected position by plotting abundance ratios of specific pairs of species for different families of molecules.

Fig. 11

Same as Fig. 9, but for molecules with a double (red) or triple (green) bond with respect to the saturated form with a single bond (black).

3.4.1 Protonated molecules

We compare in Fig. 9 six protonated molecules to their neutral form. None of these cations are detected toward any position. The most stringent constraint is obtained for formaldehyde toward N2b, with [H₂COH⁺]/[H₂CO] < 10⁻³.

3.4.2 Radicals

Radicals related to formaldehyde, methanol, acetaldehyde, methyl cyanide, cyanoacetylene, and formamide are not detected either (Fig. 10). The most stringent constraint is obtained for methyl cyanide with [CH₂CN]/[CH₃CN] < 10⁻³, again toward N2b.

3.4.3 Degree of bond saturation

Figure 11 shows the abundance ratios of saturated (single bond) and unsaturated (double or triple bond) molecules for nine families of molecules. In nearly all cases, the double-bond form is much less abundant than the single-bond one, by up to at least two orders of magnitude for the pairs C₂ H₅OH/C₂H₃ OH and n-C₃H₇CN/E-CH₃CHCHCN in N2b. The only clear exception is the pair CH₃NH₂/CH₂NH which has an abundance ratio of about 1 toward all positions, the unsaturated form even slightly dominating over the saturated one in AN02 and, marginally, N2b.

The three molecules C₂ H₅CN/C₂H₃CN/HC₃N are detected toward all five positions, with the saturated form being the most abundant one by factors of 5 (in AN03) to 50 (in N2b). The relative ratios of the double-bond and triple-bond molecules depend on the position: they have a similar abundance in AN03 but the triple-bond one dominates over the double-bond one by a factor ∼2 in N2b, AN06, and AN06c2, while the opposite is true in AN02, with C₂H₃CN dominating over HC₃N by a factor of 2. At the next stage in complexity in the alkyl cyanide family, the unsaturated forms of n- C₃H₇CN are less abundant than the saturated one by at least one order of magnitude in N2b, AN02, and AN03. The triple-bond species CH₃ C₃N is (tentatively) more abundant than the double-bond one, E-CH₃CHCHCN, in AN02, which is opposite to what we found for the pair HC₃N/C₂H₃CN in the same source.

Fig. 12

Same as Fig. 9, but for molecules with O-bearing functional groups -CHO and -COOH with respect to molecules with a -CN functional group.

3.4.4 CN, CHO, and COOH functional groups

We examine in Fig. 12 how the abundances of five groups of molecules behave for molecules that share the same backbone but terminate with a nitrile functional group (-CN), an aldehyde group (-CHO), or a carboxylic group (-COOH). No obvious pattern is visible. Overall, the CN-bearing molecules are more abundant than their CHO- or COOH-bearing counterparts, with the notable exception of NH₂CN which is about two orders of magnitude less abundant than NH₂CHO in all three positions where it is detected (N2b, AN02, AN03). While acetaldehyde is at most a factor of 10 less abundant than methyl cyanide, we find that propanal is at least two orders of magnitude less abundant than ethyl cyanide in nearly all positions (the upper limit is inconclusive for AN06c2). In addition to the simple carboxylic acid HCOOH, only one complex carboxylic acid, CH₃COOH, is (tentatively) detected toward AN02 and AN03, with an abundance about one order of magnitude lower than CH₃CN. If this ratio also holds for the pair NH₂ CH₂CN/NH₂CH₂COOH, then glycine would have an abundance at least one order of magnitude lower than its current upper limit.

Fig. 13

Same as Fig. 9, but for the reduced form (-CH₃. functional group) with respect to the aldehyde form (-CHO functional group).

3.4.5 Oxydized and reduced forms

We show in Fig. 13 how the abundances behave when we go from an oxydized form (aldehyde functional group) to a reduced form (CH₃ group). Here again, no obvious pattern is seen. While CH₃OH and C₂H₅OH largely dominate over t-HCOOH and CH₂(OH)CHO, respectively, in the sources where t-HCOOH or CH₂(OH)CHO is detected, it is the opposite for CH₃NH₂ which is less abundant than NH₂CHO in N2b, AN02, and AN03 (but maybe not in AN06c2). The pair CH₃OCH₃/CH₃OCHO lies in between these two cases, with an abundance ratio close to 1 in all five sources. The upper limits obtained for CH₃NHCH₃ and C₂H₅OCH₃ suggest that they behave like one of the latter two cases.

3.4.6 Size of carbon backbone

We investigate in Fig. 14 how the abundances vary with the size of the carbon backbone for 20 series of homologous molecules. H₂CO, CH₃OH, and NH₂CHO show a similar pattern: an abundance drop of about one order of magnitude when a molecule has an additional CH₂ group at one end of its heavy-atom backbone (CH₃CHO, C₂H₅OH, and CH₃NHCHO), and a further drop of about one order of magnitude for a second additional CH₂ group (s-C₂H₅CHO, tentatively detected in ANO3, n-C₃H₇OH, detected in N2b, and C₂H₅NHCHO, with a stringent upper limit in AN02). This behavior is also seen for CH₃OCHO for the case of one additional CH₂ group, either with (tentative) detections or constraining upper limits of C₂H₅OCHO. We do not have results for the next degree of complexity in this case.

The same behavior is observed for the series of alkyl cyanides C₂H₅CN, n−C₃H₇CN (detection), and n-C₄H₉CN (upper limit), at least in N2b, but CH₃CN does not fit into this pattern because it has a somewhat lower abundance than C₂H₅CN in nearly all five sources. Toward N2b, in addition to the previous cases, the derived upper limits shown in red also indicate an abundance drop of about one order of magnitude (at least) for one additional CH₂ group for the following species: CH₃OCH₃, a-(CH₂OH)₂, CH₂(OH)CHO, CH₂CO, CH₃NCO, CH₃NC, C₂H₃CN, CH₂NH, CH₃SH, and CH₃CCH. The upper limits are inconclusive for CH₃C(O)NH₂ and CH₃NH₂ in N2b, but they do imply an abundance drop by nearly an order of magnitude (at least) in AN02. The case of HC₃N is more extreme, with an abundance drop of 2–3 orders of magnitude for CH₃C₃N in N2b, AN02, and AN03. For the two positions were HCCNC is tentatively detected, the upper limits derived for CH₃CCNC are inconclusive.

The only clear exception to the general behavior noted in the previous paragraph is NH₂CN, for which the next stage in backbone length, NH₂CH₂CN, is one order of magnitude more abundant in N2b. However, the latter molecule corresponds to an additional CH₂ group between the nitrogen and carbon atoms of NH₂CN. We do not have results for the molecule with an additional CH₂ group at the end of the backbone (CH₃NHCN).

Fig. 14

Same as Fig. 9, but for series of molecules with an additional CH₂ group in their backbone at each further step in complexity.

3.4.7 Structural isomers

Finally, we examine the relative abundances of structural isomers, that is molecules with the same elemental composition but a different arrangement of their constituent atoms. The results are displayed in Fig. 15 for 17 groups of species with a molecular size ranging from 4 to 12 atoms. Several pairs of isomers stand out for having similar abundances: C₂H₅OH/CH₃OCH₃, CH₃C(O)NH₂/CH₃NHCHO, CH₃COOH/CH₂(OH)CHO (tentatively), n/i-C₃H₇OH, and n/i-C₃H₇CN. On the contrary, one isomer (given in parentheses) dominates by more than two orders of magnitude in the following groups: CHNO (HNCO), C₂H₃N(CH₃CN), C₃HN(HC₃N), and C₃H₅N(C₂H₅CN). We note that methyl acetate (CH₃C(O)OCH₃), which was detected in Orion KL (Tercero et al. 2013, 2018), is missing in the C₃H₆O₂ family shown in Fig. 15. We have a preliminary spectroscopic entry for this molecule but it suffers from several issues that currently prevent us from deriving reliable column densities or upper limits for the Sgr B2(N2) sources.

Focusing only on the detections and tentative detections displayed in Fig. 15, we do not find any significant difference in the isomer ratios between the five positions. These isomer ratios thus appear to be relatively robust with respect to the potential range of physical conditions or age spanned by this small sample of sources.

Fig. 15

Same as Fig. 9, but for groups of structural isomers. The elemental composition of the groups is labeled along the x axis. The list of molecules belonging to each group is provided at the bottom of the figure with their respective colors as used in the plots.

3.5 Comparison of chemical composition of Sgr B2(N2) to other sources

Figures 16–20 compare the column densities relative to methanol derived in Sect. 3.2 toward the sources of Sgr B2(N2) to the composition of sources that have been studied in detail in the literature: the hot core G31.41+0.31, hereafter G31.41 (GUAPOS interferometric survey, e.g., Mininni et al. 2020; López-Gallifa et al. 2024), the hot corino IRAS 16293–2422 B, hereafter IRAS 16293B (PILS interferometric survey, e.g., Jørgensen et al. 2016; Drozdovskaya et al. 2019), the (possibly shocked) molecular cloud region G+0.693–0.027, hereafter G+0.693 (Yebes and IRAM 30 m single-dish survey, e.g., Rivilla et al. 2022c; Jiménez-Serra et al. 2022)⁶, and the starless core TMC-1 in the Taurus molecular cloud (mainly the QUIJOTE single-dish survey, e.g., Cernicharo et al. 2021; Agúndez et al. 2025). The column densities of these sources compiled from numerous articles published by the teams of these surveys, as well as a few others in the case of TMC-1 (e.g., Gratier et al. 2016; Tennis et al. 2023), are listed in Table A.1. In the cases where this had not been done, we applied a posteriori vibrational or conformational corrections to the published column densities. We report in Table 4 the Pearson correlation coefficients of the abundance distributions shown inFigs. 16–20, for all molecules and per class of molecules. The calculation does not take the upper limits into account and it was done only for samples with at least four items.

Jørgensen et al. (2020) reported a good correlation between the chemical compositions of IRAS 16293B (derived from the PILS survey) and Sgr B2(N2) (obtained from the EMoCA survey at an angular resolution of ∼1.6^′′) after normalizing the column densities of the O-bearing and N-bearing molecules to CH₃OH and HNCO (or CH₃CN), respectively. Given that Sgr B2(N2) consists of several sources that were not resolved by the EMoCA survey, we revisit this correlation by taking advantage of the higher angular resolution of the ReMoCA survey. Figures 16b–18b show a clear differentiation of the four main classes of molecules (O-bearing, O+N-bearing, N-bearing, and S-bearing) between IRAS 16293B and N2b/AN02/AN03. For each class of molecules taken separately, there is a good correlation between IRAS 16293B and N2b/AN02/AN03, but the four groups are shifted with respect to each other: the O-bearing molecules occupy the top part of the distribution, the N-bearing species the bottom part, and the O+N-bearing and S-bearing molecules lie in between. The correlation coefficients listed in Table 4 confirm this visual impression: the coefficients of IRAS 16293B for the individual classes of molecules are systematically larger than for the whole sample of molecules. The tightest correlation is obtained for the S-bearing molecules, followed by the O-bearing ones. The N-bearing molecules are the ones that correlate the least, with correlation coefficients between 0.66 and 0.77. The O+N-bearing species have a coefficient that lies in between, except for N2b where it is slightly smaller than for the N-bearing molecules. With a coefficient larger than 0.8, the overall correlation seems to be stronger for AN06c2 and AN06 than for N2b/AN02/AN03 but this may be biased by the smaller sample of molecules (21 versus 25–28) and the upper limits of several O-bearing, N-bearing, and O+N-bearing species in Figs. 19b and 20b suggest significant deviations from the overall correlation.

Table 4

Correlations between the chemical composition of Sgr B2(N2) sources and sources from the literature.

Among the four sources from the literature displayed inFigs. 16–18, the one that shows the best overall correlation with N2b, AN02, and AN03 is the hot core G31.41 (Table 4). One reason for this better overall correlation compared to IRAS 16293B is that the differentiation between the different classes of molecules is less pronounced. Still, like in the case of IRAS 16293B, the N-bearing species occupy the lower part of the distribution in Figs. 16a–18a: they are underabundant by about one order of magnitude in G31.41, especially compared to AN02 and AN03. However, in contrast to IRAS 16293B, the O-bearing molecules are well mixed with the O+N-bearing species and the S-bearing molecules in the overall distribution. Among the four classes of molecules, the tightest correlation occurs for the N-bearing molecules with a coefficient larger than 0.9. The O-bearing molecules are the ones that correlate the least. This is even more true for N2b when we consider the upper limits obtained for t-HCOOH and CH₃COOH which are not taken into account in the calculation of the correlation coefficients (labels q and i in Fig. 16a). However, these two molecules do not stand out in the O-bearing class in the cases of ANO2 and AN03 where they are detected. There is also a strong overall correlation between G31.41 and AN06 (Fig. 20a), except for the class of O+N-bearing molecules, with HNCO, NH₂CHO, and CH₃C(O)NH₂ being overabundant in G31.41 by about or more than one order of magnitude.

In contrast to G31.41 and IRAS 16293B, there is no overall correlation between G+0.693 and the Sgr B2(N2) sources, although they are all located in the same molecular cloud complex. The lack of correlation results mainly from the distribution of the N-bearing species which have correlation coefficients between 0.08 and 0.44 (Table 4). For each other class of molecules, we find a much higher degree of correlation, with all correlation coefficients being above 0.75. The S-bearing species are located above the O+N-bearing molecules and O-bearing species in Figs. 16c–20c, meaning that, relative to O+N-bearing molecules and O-bearing species, the S-bearing molecules are more prominent in G+0.693 than in the Sgr B2(N2) sources.

Finally, the situation is even more extreme for the starless core TMC-1. There is absolutely no overall correlation between this source and the Sgr B2(N2) sources, even for the O- or N-bearing molecules taken separately. The only class of molecules that shows some (low) degree of correlation is the S-bearing class but, as indicated by the smaller correlation coefficients (0.6–0.7), the dispersion of the data points in Figs. 16d–20d is larger than for the other three sources from the literature.

To summarize, the class of N-bearing species is the one that reveals the largest variance compared to the other classes of molecules: with respect to the Sgr B2(N2) sources, it shows the tightest correlation for G31.41, a poorer correlation for IRAS 16293B, and no correlation at all for G+0.693 and TMC-1. The N-bearing species are underabundant by 1−2 orders of magnitude in G31.41 and IRAS 16293B with respect to N2b, AN02, and AN03. This is also the case, albeit in a less pronounced manner, for the S-bearing and O+N-bearing molecules. In contrast to the N-bearing species, the class of S-bearing molecules has the smallest variance: it shows a high degree of correlation for G31.41, IRAS 16293B, and G+0.693 with respect to the Sgr B2(N2) sources (and a moderate degree of correlation for TMC-1). This is also the case, albeit to a lesser degree, for the class of O+N-bearing molecules. Finally, the O-bearing species stand out in G31.41, with a poorer correlation to the Sgr B2(N2) sources. Overall, the O-bearing species are the ones that are the closest to the 1:1 relation but this is biased by the fact that we analyzed the abundances relative to methanol.

Fig. 16

Same as Fig. F.2 but comparing sources from the literature to N2b.

Fig. 17

Same as Fig. F.2 but comparing sources from the literature to AN02.

Fig. 18

Same as Fig. F.2 but comparing sources from the literature to AN03.

Fig. 19

Same as Fig. F.2 but comparing sources from the literature to AN06c2.

Fig. 20

Same as Fig. F.2 but comparing sources from the literature to AN06.

4 Comparison with chemical models

To aid in our interpretation of the varied molecular column densities determined toward each source, we made use of a preexisting grid of gas-grain chemical models. Shope et al. (2024) used the three-phase (gas/surface/bulk-ice) chemical kinetics model MAGICKAL to simulate hot-core chemistry under a range of physical parameters. Their chemical model and network were identical to the “final” model used by Garrod et al. (2022). Their physical treatment, consisting of a cold, isothermal collapse (stage 1) followed by a dynamically static warm-up phase (stage 2), was the same as that used by Garrod (2013), and similar to various related implementations. The Shope et al. grid varied several key parameters within their generic model: (i) the cosmic-ray ionization rate (CRIR) used throughout stages 1 and 2 of the model; (ii) the final gas density, n_H, reached during stage 1, which carries through to stage 2; (iii) the warm-up timescale, t_wu, parameterized as the time taken to reach 200 K during stage 2; and (iv) the visual extinction, A_V, used at the beginning of stage 1, which in turn scales the evolving A_V value as collapse occurs. Parameters (i)–(iii) were varied logarithmically (n_H = 2 × 10⁶−2 × 10¹⁰ cm⁻³; ζ = 1.30 × 10⁻¹⁸−1.30 × 10⁻¹⁵ s ⁻¹; t_wu = 2 × 10⁴−2 × 10⁶ yr; see Table 1 of Shope et al. 2024). The initial visual extinction was tested with two values (2 mag and 3 mag). Due to the warm-up timescale only being relevant to stage 2, this resulted in a total of 84 stage-1 model runs, and 756 stage-2 runs, i.e. 9 stage-2 models for each preceding stage-1 run. Each stage-1/stage-2 combination is a single-point (0-D) representation of a hot core through its chemical evolution from cold, diffuse conditions to a dense, hot core that reaches a final temperature of 400 K.

Although the main focus of the Shope et al. (2024) models was a comparison with NGC 6334I, they also compared their model grid with the EMoCA-derived column densities for Sgr B2(N2) presented by Jørgensen et al. (2020) (see Belloche et al. 2016). This comparison involved finding the grid model with the best collective match to the observational column densities, each taken as a ratio with the column density of methanol, i.e. R_obs,i = N(i)/N(CH₃OH). Since the models produce only single-point fractional abundances, the peak value for each molecule (during stage 2) was taken in ratio with the peak methanol abundance, i.e. R_{mod, i} = X_peak(i)/X_peak(CH₃OH), to represent the column density ratio (see Shope et al. 2024 and Belloche et al. 2019, for more technical discussions of the nuances of such comparisons). The logarithm of the quotient of the two ratios, i.e. log(R_mod,i/R_obs,i) = m_i, indicates the number of orders of magnitude that a modeled species diverges from its observed value. For species for which only an upper limit is determined, a modeled value that exceeds the observational value is treated in the same way, while a model value below the observed upper limit is treated as a perfect match, i.e. m_i = 0. To determine the overall quality of match for a particular model, the root mean square of m_i over all species i is calculated, with the best matching model having the lowest value. We note that this choice of matching parameter tends to favor models in which multiple species show a moderately good match, over models in which the match is very good for some species and very poor for others. In other words, species for which the model results are very divergent from the observations have a strong negative impact on the overall matching parameter.

Particularly noteworthy in the analysis of Shope et al. (2024) is their determination of the best-matching model for Sgr B2(N2) to have a warm-up timescale 2 × 10⁶ yr and a cosmic-ray ionization rate of 1.3 × 10⁻¹⁷ s⁻¹. The characteristic warm-up timescale should be considered simply a parameter, which is most important in determining how rapidly an already hot core becomes yet hotter; however, this timescale value is the longest of those tested in the grid. Meanwhile, the best-matching CRIR corresponds to the canonical interstellar value, ζ₀, which is rather lower than might be expected for Sgr B2(N2), based on other recent studies (e.g., Bonfand et al. 2017). However, the determination of this match was based on the column densities of 11 chemical species, plus two upper limits, with several species omitted. Our present ReMoCA observations provide both a larger selection of molecules with which to compare, all of which are included in the model chemical network, as well as a sample of cores that the higher angular resolution of ReMoCA allowed us to distinguish. Collective fitting of this dataset may allow for a more accurate determination of the likely physical conditions in each core.

Because of these differences in the observational dataset, our method diverges slightly from that used by Shope et al. (2024). Crucially, we assumed that all five cores must experience the same CRIR. To impose this constraint, we determined firstly the best-matching model for each core within each fixed CRIR value; this produced a total of five match parameters for each CRIR value. We then took the root mean square of these five, so that each CRIR has an associated matching parameter that identifies the best collective value for the entire dataset (these values are shown in Fig. 21). We consider the best model for each source to be the one with the lowest matching parameter within the subset for which the CRIR has been collectively determined as the best match.

Some of the detected species are not included in our match calculations, as they are not present in the chemical network used in the model grid. However, following Shope et al. (2024), we also elected not to include certain species in the calculations even though they are present in the models. This includes species such as acetone (CH₃C(O)CH₃) and propanal (C₂H₅CHO), for which the network is sparse (although it has since been improved by Belloche et al. 2022). Others, like CH₃NCO, have an uncertain chemistry that is not well reproduced by the models. Some omitted species, such as HC₃N and HC₅N, are not uniquely associated with the hot stage of the core, and therefore could be unduly influenced by chemistry in the lower density gas (either in the observations or during the early stages of the models). We also excluded the sulfur-bearing species OCS, H₂CS, and CH₃SH, as the dominant form of sulfur in dense clouds is uncertain and the chemistry is thus poorly defined. We retained SO and SO₂ in the fit, as they are mainly produced in the gas phase in hot cores, following ice desorption, and they have been well reproduced by chemical models for a long time, given an appropriate initial sulfur abundance (here, X_init(S) = 8 × 10⁻⁸). We note that the initial elemental abundances used in the models were not varied in the grid; Shope et al. (2024) used the values reported in Table 1 of Garrod (2013).

Table 5

Range of final (i.e. stage-2) gas densities from the model grid used in the comparison with each source, in the density-restricted setup.

There is a total of 37 species that could have in principle been included in the matching routine. Twelve were omitted (as described above), leaving 25, of which between 0 and 8 are observational upper limits. In contrast to Shope et al. (2024), for species bearing a nitrile (−CN) group the matching parameter is based on a ratio with CH₃CN instead of CH₃OH, in keeping with the comparison methods of Jørgensen et al. (2020). For methyl cyanide (CH₃CN) itself, the ratio with methanol was retained.

Using this updated matching parameter method, we compared the observational data with the model grid in two ways. Firstly, we determined the best matching models based on an unrestricted comparison with all models; secondly, we restricted the comparison to a narrower range of gas densities that are reflective of observational values or estimates for each source (see Table 5). Values for AN02, AN03, and AN06 are based on determinations of n(H₂) by Sánchez-Monge et al. (2017). The ReMoCA continuum emission is a factor of ∼3 fainter toward N2b than toward AN02 (see Fig. 3). Therefore we extended the restricted density range of this source to lower values. As we have seen in Sect. 3.1.2, two velocity components overlap along the line of sight to AN06. The secondary component, AN06c2, traces the edge of the dense core that contains AN02, AN03, and N2b, while the main component, AN06, is likely in the foreground or background. The continuum emission analyzed by Sánchez-Monge et al. (2017) contains the contribution of both components, yet we think that it is dominated by AN06. This motivated our decision to restrict the density range explored for AN06c2 to lower values.

Figure 21 indicates the collective matching parameter over all five sources, along with the individual subvalues for each source, at each CRIR value; panel (a) shows the results for the unrestricted setup, while panel (b) shows those of the density-restricted comparison. In both setups, CRIR values below the canonical interstellar value, ζ₀, are disfavored. In the unrestricted setup, the best overall match occurs with ζ=10 ζ₀. In the density-restricted setup, the best match is obtained at the maximum CRIR value tested, ζ=100 ζ₀ = 1.3 × 10⁻¹⁵ s⁻¹.

It is notable that the preference for the most extreme CRIR value is strongly associated with the variation in the ANO2 and AN03 matching parameters; indeed, the overall matching parameter is worse for all CRIR values in the restricted setup as a result of this dependence. In the unrestricted case, the best-matching models for both of these sources (with ζ = 10 ζ₀) take stage-2 gas densities of n_H = 2 × 10⁶ cm⁻³, which are much lower than the observational estimates. Values of 2 × 10⁸ cm⁻³ (with ζ = 100 ζ₀) provide the best match in the restricted setup. Thus, at these higher densities for AN02 and AN03, a further elevated CRIR is required for the best match with observations, even though the other sources are provided a somewhat worse match under those conditions.

Our analysis continues with the exclusive use of the densityrestricted comparisons. Figure F.3 plots the matching parameter of each model for each observed source, ordered in bands corresponding to a fixed CRIR value. The bands are ordered from the best to the worst overall matching parameter. Within each band, and for each individual source, the matching parameter is plotted from best to worst. We note that there is no correspondence between model numbers among curves in the same band, as the results for each source-model combination are individually sorted. Furthermore, due to the density restrictions shown in Table 5, different sources have different numbers of grid models available to them. The plots indicate simply the degree of variation in matching parameter between different models for each source. Source AN06c2 universally shows the best matching parameter, comparing the curves at fixed sorted-model values. Matching the other sources is generally harder, i.e. there are fewer models that reproduce them well. This may be related to the fact that AN06c2 has the most upper limits, making a match easier. However, AN06 has the same number of upper limits, while being a worse match in all cases, when comparing best with best. The matching parameters for AN02 and AN03 generally appear to become the most rapidly divergent with increasing sorted-model value, i.e. they have a narrow range of physical conditions that produce a “good” match with observations.

Figure 22 shows the comparison between models and observations for each source; the best-matching model is shown in each case, for the best-matching CRIR of ζ = 1.3 × 10⁻¹⁵ s⁻¹. Unfilled blue bars with an arrow indicate observational upper limits, while black bars indicate species that are omitted from the matching procedure, but which nevertheless exist in the models. With the CRIR value fixed, the key remaining parameters distinguishing the best-match models for each source are the gas density and the warm-up timescale. Particularly notable is the fact that, with the exception of AN06, the best match for each source is provided by the same model, taking values ζ = 100 ζ₀, n_H,final = 2 × 10⁸ cm⁻³, t_wu = 2 × 10⁴ yr, and A_V,init = 3 mag. The best-matching model for AN06 has a factor 10 higher gas density, but is otherwise the same. We note that the best warm-up timescale for all sources is the shortest of those tested, and 2.5 × shorter than the “fast” warm-up timescale used in various past models by Garrod (2013) and others.

Many of the species omitted from the matching procedure are, unsurprisingly, not well reproduced. But in general, most common hot-core species included in the matching routine are reasonably well reproduced by the models, i.e. are within one order of magnitude (oom) of the observed values (as marked by the shaded region). For example, the structural isomers methyl formate and glycolaldehyde are both generally somewhat overproduced, although they are mostly within 1 oom of the observed values, and they also tend to scale with each other among the plots shown. However, due to the fact that four of the sources share a single best-matching model, any apparent variations in the quality of match between that (or any) pair of species among those four sources is solely due to variations in the observed values. In the two best-match models representative of either AN06 or collectively of AN02, AN03, AN06c2, and N2b, glycolaldehyde is overabundant compared with methyl formate.

H₂CO, C₂H₅OH, CH₃CHO, and CH₂CO are consistently, if only modestly, overproduced for all five source-model matches. Species that appear consistently underproduced include CH₃CN and C₂H₅CN, although this is not the case for the AN06 model. HNCO and NH₂CHO are in some cases underproduced and in some cases overproduced, although they are mostly discrepant in the same sense; again, this apparent variation is essentially caused by matching a single model to a selection of sources that behave somewhat differently.

Figures. 9–15 show not only the observational ratios but the equivalent values from the best-matching chemical models for each source. Chemical model results are indicated by crosses. Although in our matching-parameter analysis we have only used molecules that are detected in at least one of our source datasets, it is valuable to compare the best-matching models with the observations for a much broader range of species, including those that have been omitted from the matching parameter.

The model abundances for protonated molecules with respect to their unprotonated forms (Fig. 9) are comfortably below all of the observational upper limits. Ion abundances in the models (during the hot stage, and under otherwise fixed CRIR conditions) tend to scale inversely with gas density, which influences collisional lifetimes. Within the range of densities tested in the restricted setup, the models would be unlikely to reach the observed upper limits. The model-source comparisons are therefore not a strong constraint on ion abundances.

Gas-phase destruction rates for radicals also tend to scale with gas density. The modeled values for radicals HCO, CH₃O, and CH₂OH with respect to H₂CO and CH₃OH (shown in Fig. 10) are substantially below the observational upper limits. The other four radicals plotted (CH₂CHO, CH₂CN, C₃N, and NH₂CO) are in some cases far above the upper limits. This may be caused by a lack of appropriate destruction mechanisms for these radicals; in particular, reactions with (abundant) atomic hydrogen could lead to their rapid destruction, as is the case for HCO, CH₃O, and CH₂OH (see, e.g., Tsang & Hampson 1986), leading to the production of H₂ and another stable species. Other disproportionation and radical-pair production reactions are also possible, where energetically favorable. In the models, reactions with atomic H are frequently the most important destruction mechanisms for radicals that have them; more important even than sequential protonation and recombination, or CR-driven photodissociation. Atomic-H reactions for CH₂CHO, CH₂CN, NH₂CO, and C₃N are not currently present in the network (although see Bonfand et al., in prep.). In the latter case, however, such a pathway is unlikely, due to the absence of energetically favorable products. As such, the fact that the modeled C₃N/HC₃N ratio exceeds the observational upper limit for sources N2b, AN02, and AN03 indicates that a model with higher gas density could remedy this. However, the fact that HC₃N was not included in the matching parameter could also affect the ratio.

Similarly, the ratios between low- and high-saturation species, C₂H₅CN/HC₃CN and the equivalent larger nitriles (see Fig. 11) may also be affected by the difficulties of including those species in the matching parameters. The abundances of C₂H₃CN and C₂H₃OH are somewhat dependent on the branching ratio of the recombination of protonated ethyl cyanide and protonated ethanol, respectively, which could explain their overabundances for most model-source combinations. In Fig.12, -CHO and -COOH group-bearing species are somewhat overproduced relative to -CN, although relative to each other they are much closer to the observations. The NH₂CHO/NH₂CN ratio is higher than expected.

Modeled ratios of -CHO versus -CH₃ group-bearing species shown in Fig. 13 are generally not far off the observations, although the CH₂(OH)CHO/C₂H₅OH ratio is elevated in the case of N2b. As described by Shope et al. (2024), this may indicate that a higher gas density model is required.

As may be seen in Fig. 14, modeled ratios between the different nitrile homologs (C_nH_2n+1CN, n = 1−4) show a somewhat varied match with observations, with C₂H₅CN somewhat underproduced in all cases, while normal butyl cyanide is generally overproduced. The general underproduction of C₂H₅CN may be related to an overconversion to the larger cyanides in the ice mantles. For the CH₃OCHO series, in cases where C₂H₅OCHO is detected, the ethyl to methyl formate abundance appears low in the models. As noted by Belloche et al. (2009), production of ethyl formate in the models is dominated by a methyl-radical addition to another radical whose precursor is methyl formate. In the present model grid, this occurs largely within the bulk ice, driven by CR-induced UV photoproduction of radicals. A number of different factors determining the availability of the CH₃ radical (such as CH₄ abundance and photodissociation efficiency) could be at play in determining the eventual modeled abundance of ethyl formate. Again, four of the sources are being matched with the same single model, so when they vary observationally, disagreements should become apparent. A higher resolution grid, with differentiated source-model matches for all sources, might lead to better individual matches for the molecules mentioned here.

Among the structural isomers shown in Fig. 15, CH₃CN/CH₃NC is not a good match for source AN06, although it is not bad for the other sources. In the present chemical network, which includes reactions used by Willis et al. (2020), the abundance of CH₃NC is particularly sensitive to the reaction H+CH₃NC → HCN+CH₃, whose activation-energy barrier is highly uncertain. At the higher density experienced in the AN06 best-match model, this reaction would have a more pronounced effect. CH₃NC was already omitted from the matching parameter due to the uncertainties in its chemistry. The poor match for AN06 could indicate that the barrier is too low in the network.

Figure 15 also shows the ratios of C₂H₄O₂ species. CH₃OCHO (methyl formate, MF) and CH₂(OH)CHO (glycolaldehyde, GA) generally show roughly appropriate ratios with each other, although they are both very moderately overproduced in comparison with CH₃COOH (acetic acid, AA) for sources where it is detected (AN02 and AN03). The production of acetic acid in the models occurs through somewhat different chemical pathways that involve acetaldehyde, CH₃CHO (see Garrod et al. 2008, 2022). In the model representing N2b, AN02, AN03, and AN06c2, the MF/GA/AA ratio is 18/5.8/1. In the model representing AN06, a similar ratio 17/4.0/1 is achieved. These may be compared with the observed ratios of 29/1.6/<1 (N2b), 12/1.8/1 (AN02), and 14/1.5/1 (AN03). The modeled abundance of glycolaldehyde in particular, especially in comparison with source N2b, could be considered notably in excess of the observational value, when considering MF:GA ratios. Shope et al. (2024) considered the abundance of glycolaldehyde in particular and the possible explanations for extremely low ratios with methyl formate toward some sources. Those authors noted that a combination of high gas density and long timescale in the models was capable of reducing GA abundances during the warm-up period. Chemically, this occurred through the brief adsorption of gas-phase atomic H onto the grain surfaces, followed by reaction with a glycolaldehyde molecule that had recently become exposed on the surface but had not yet desorbed. This process was found to be effective under conditions where H adsorption would remain rapid for a long period of time.

The best-match models for the five sources studied in the present work achieve fairly high gas densities, but do not experience long enough periods for the effect to take hold strongly. In a less constrained physical model, or a more precise treatment of the warm-up process, GA destruction might be more efficient. As noted by Shope et al., the key period for GA destruction is not the entire warm-up period from ∼10−400 K, but the much shorter period between the start and end of major water-ice desorption. A somewhat longer period in this more limited temperature range would still be consistent with hot-core lifetimes on the order of a few 10⁴ yr. More dynamically accurate simulations of hot-core chemical evolution are necessary to test these possibilities.

Fig. 21

Overall matching parameter obtained for the comparison of the grid of chemical model results with observational abundances of all species across all sources, assuming a uniform CRIR. A lower matching parameter indicates a closer match between models and observations. Panel a: unrestricted density. Panel b: density restricted to the ranges indicated in Table 5.

Fig. 22

Comparison of best-fit model abundances to observational results, using the density-restricted setup. Bars indicate the number of orders of magnitude by which each abundance ratio in the best-matching model exceeds (or otherwise) the observational value. Data correspond to the best-matching model for each individual source, within the subset of grid models with ζ = 100 ζ₀, which is found to produce the best overall match across all sources. The shaded area represents values where the models and observations vary by 1 oom or less. Unfilled blue bars with an arrow indicate that the comparison is based on an observational upper limit. Black bars indicate observed species that were not included in the matching parameter analysis.

5 Discussion

5.1 Chemical evolution within Sgr B2(N2)

The ReMoCA survey has allowed us to derive the detailed chemical composition of four hot cores embedded in Sgr B2(N2) and a position, AN06c2, located at the edge of the dense core (Sect. 3.2). As demonstrated in Sect. 3.3, there is a tight correlation between the chemical compositions of AN02 and AN03, despite the fact that AN03 contains a HCH II region while AN02 does not (Sects. 3.1.1 and 3.1.3). While we could worry that the small angular separation between AN02 and AN03 (∼0.64^′′) may have resulted in a mutual contamination of their observationally derived chemical compositions, we can exclude this interpretation because the correlation between N2b and AN02 is looser (Fig. 8a) despite their even smaller separation (∼0.55^′′). The correlation between AN02 and AN03 is thus genuine. The presence of the HCH II region indicates that AN03 is more evolved than AN02 or more massive (hence more luminous). The similarity of the masses derived from dust emission by Sánchez-Monge et al. (2017) for AN02 and AN03 tends to discard the latter interpretation. According to the time sequence derived by Nony et al. (2024) for the high mass star-forming protocluster W49N, the lifetime of hot cores is 6 × 10⁴ yr, including the phase when a hypercompact or ultracompact (UC) H II region coexists with the hot core emission. W49N contains a bit less than half as many hot cores with H/UCH II regions as hot cores without, which suggests a statistical lifetime of ∼2 × 10⁴ yr for the former. Hot core lifetimes are similar in Sgr B2(N) and W49N (Bonfand et al. 2017) and so we conclude that the chemistry has not evolved significantly during a timescale of ∼2 × 10⁴ yr around AN03 on a scale of 0.7^′′(∼5700 au). With a size of ∼0.08^′′, the HCH II region is one order of magnitude smaller than the molecular emission probed with ReMoCA toward AN03 (∼0.7^′′−1^′′). This is likely the reason why it has not yet had time to alter the chemistry of the hot core. As a matter of fact, there is no significant difference between AN02 and AN03 in terms of rotational temperature of their molecular emission (see Fig. F.4e).

The hot core N2b is most likely in an earlier evolutionary stage than AN02 and AN03 because, unlike these sources, it is neither associated with a HCH II region nor with masers. The rotational temperatures of its molecular emission are on average a bit lower than the temperatures of AN02 and AN03 by about 20 K (Fig. F.4a–b), which may support an earlier evolutionary stage. As reported in Sect. 3.1.4, N2b is associated with a compact dust core and its elevated rotational temperatures of ∼150 K suggest that it has already formed a protostar, unless its temperature structure is imposed by its neighbor AN02. If N2b contains a nascent protostar, then it is at most a few 10⁴ yr younger than AN02. Therefore, it is remarkable that its chemical composition shows significant differences compared to that of AN02 and AN03. Overall, N2b’s molecular abundances relative to methanol are a factor ∼2−4 lower than those of AN02 and AN03, and their dispersion is larger than in the correlation plot of AN02 versus AN03 (compare panels a–b and e of Fig. 8). Two organic molecules stand out with abundances that are even lower by more than one order of magnitude in N2b : NH₂CN and t-HCOOH. It is tempting to conclude that the chemistry evolves significantly during the initial phase (a few 10⁴ yr) of thermal desorption at the hot core stage, which may suggest that gas-phase processes play an important role in reshaping the chemical composition after desorption of the molecules from the grains. In this context, it would be interesting to understand why NH₂CN and t-HCOOH in particular appear to be so sensitive to this phase.

We proposed in Sect. 3.1.2 that AN06 is a hot core that is not embedded in the dense region that contains AN02, AN03, and N2b. Like N2b, it is not associated with a HCH if region or any masers. AN06 may thus be in an early evolutionary stage, maybe even earlier than N2b given its slightly lower rotational temperatures (see Fig. F.4d). Its chemical composition correlates with that of N2b, albeit with a much larger dispersion than AN02 versus AN03 (compare panels d and e of Fig. 8). If AN06 and N2b are at a similar stage of evolution, then the differences in their chemical compositions suggest that they reside in different environments. Alternatively, AN06 may be less massive than the other hot cores of Sgr B2(N2) and the chemical differences may result from a difference in evolutionary stage. Sánchez-Monge et al. (2017) derived a mass of AN06 that is 30% smaller than the mass of AN03. However, their dust continuum observations cannot disentangle the two velocity components that overlap along the line of sight. The mass calculated for AN06 may thus be largely overestimated. The most striking chemical difference between AN 06 and N2b is that HNCO and NH₂CHO are nearly two orders of magnitude less abundant in the former. Understanding the cause of this huge deviation from the overall correlation may tell us what controls the larger dispersion of the correlation between AN06 and N2b. Finally, while the chemical compositions of AN06 and AN06c2 correlate well apart from an overall shift by a factor ∼2 (Fig. 8h), the abundance of formamide stands out by nearly two orders of magnitude. In this respect, the composition of AN06c2 is much more similar to that of the dense region that contains AN02, AN03, and N2b. This supports the idea that AN06c2 traces the edge of the region and AN06 overlaps along the line of sight but is physically unrelated to this region.

5.2 Tight correlation between Sgr B2(N2) and G31.41

Located in the Scutum spiral arm at a distance of 3.75 kpc (Immer et al. 2019), the hot core G31.41 is a small protocluster of luminosity ∼4.5 × 10⁴ L_⊙ that consists of four high-mass young stellar objects (YSOs) with gas masses of 15−26 M_⊙ within a radius of 400-500 au (Beltrán et al. 2021). Each of them drives an outflow (Beltrán et al. 2022) but none of them harbors a HCH II region (Cesaroni et al. 2010; Beltrán et al. 2021). AN03 and AN02 have gas masses of about 500 M_⊙ within a radius of about 2800 au (Sánchez-Monge et al. 2017). Assuming powerlaw density profiles with a power-law index in the range from −1.5 to −2, then the mass enclosed in a radius of 500 au is 40−90 M_⊙, implying that AN03 and AN02 may form highermass stars than G31.41’s YSOs by a factor 2–3, or even higher given that AN03 already contains a HCH II region. Among the four sources from the literature investigated in Sect. 3.5, G31.41 is the one with the tightest correlation of its overall chemical composition with that of N2b, AN02, and AN03. The overall chemical composition of hot cores thus seems to be relatively insensitive to the environmental features that distinguish the Galactic center region from the Galactic disk (e.g., level of turbulence, CRIR, gas temperature). The O-bearing species show a poorer correlation between G31.41 and N2b, AN02, and AN03 than the N-bearing ones. This suggests that the former may be more sensitive to the environmental conditions than the latter.

5.3 Chemical segregation between classes of molecules

Chemical segregation between O-bearing and N-bearing molecules has been reported in various star-forming regions since the 1990s (see, e.g., Qin et al. 2022 and short reviews on this topic in Sect. 4.3 of Jørgensen et al. 2020 and Sect. 4.7 of Busch et al. 2024). Among the four classes of molecules investigated in Sect. 3 (O-, N-, N+O-, and S-bearing), the N-bearing class stands out as the one with the largest variance across the sample of sources investigated in Sect. 3.5. For this class of molecules, the composition of Sgr B2(N2)’s hot cores correlates well with that of the other two hot cores/corinos G31.41 and IRAS 16293B but does not correlate at all with that of G+0.693 and TMC-1. This is in stark contrast to the class of S-bearing molecules which always shows a correlation with Sgr B2(N2)’s hot cores, strong in the case of G31.41, IRAS 16293B, and G+0.693, and moderate in the case of TMC-1. The O-bearing molecules lie in between, with a strong correlation in the case of G31.41, IRAS 16293B, and G+0.693, but a poor one in the case of TMC-1.

TMC-1 is a cold (∼10 K) starless core of moderate density (3−8 × 10⁴ cm⁻³, see, e.g., Pratap et al. 1997; Lique et al. 2006) located in the low mass star-forming molecular cloud Taurus. Part of its molecular composition may be controlled by gasphase chemistry (e.g., Cernicharo et al. 2021), but pure gas-phase chemical models have difficulties in reproducing the abundances of certain molecules like acetaldehyde (e.g., Agúndez et al. 2025), suggesting that nonthermal desorption of molecules formed on the grains, triggered by, for instance, cosmic rays or the release of chemical energy, likely contributes to the composition of the gas phase as well (see, e.g., the case of CH₃OH, C₂H₅OH, and C₂H₅CHO in Agúndez et al. 2023).

G+0.693 is also a starless region of moderate density (10⁴− 10⁵ cm⁻³, Zeng et al. 2020; Colzi et al. 2024) and low dust temperature (∼20 K, Etxaluze et al. 2013), but it is located in the Sgr B2 molecular cloud complex, about 54^′′ away from Sgr B2(N2), which means that they both have been subject to similar levels of turbulence, cosmic rays, and radiation (UV photons, X-rays) during their evolution. The chemistry of G+0.693 is thought to be dominated by low-velocity shocks that eject molecules from the grain mantles through sputtering (e.g., Requena-Torres et al. 2006; Zeng et al. 2018). If we consider that most molecules are formed on the grains and that the molecular composition of Sgr B2(N2)’s hot cores was in large part inherited from the composition of the grain mantles formed during the prestellar phase, then differences in the compositions of G+0.693 and Sgr B2(N2)’s hot cores could result from the shocks that affect G+0.693. Alternatively, the composition of the hot cores may have been altered by processes on the grains during the warm-up phase. However, it is also likely that the large difference in gas density between G+0.693 and Sgr B2(N2)’s hot cores has a major effect on the post-desorption behavior of gas-phase molecules. Lower gas densities in G+0.693 should result in both a slower rate of destruction and a different balance of destructive ions, depending on how complete is the loss of ice due to sputtering during the passage of a shock (Willis et al., in prep.). The amount of ammonia in the gas can assist in the destruction of some molecules (especially amine group-bearing species) while enhancing the survival of others (Taquet et al. 2016; Garrod & Herbst 2023).

The CRIR in the Galactic center region is higher than in the Galactic disk by 1–2 orders of magnitude (e.g., Indriolo et al. 2015; Le Petit et al. 2016). The fact that N-bearing species of Sgr B2(N2)’s hot cores correlate well with hot cores/corinos in the Galactic disk (G31.41 and IRAS 16293B) but neither with G+0.693 nor with TMC-1 implies that the CRIR is not the dominant factor producing the large variance of the N-bearing class. We conclude that the class of N-bearing molecules reacts more sensitively to shocks (G+0.693), low-temperature gasphase chemistry subsequent to nonthermal desorption (TMC-1), or density (G+0.693 and TMC-1) than O-bearing and S-bearing species. This is in line with the findings of Busch et al. (2024) who reported an abundance enhancement of cyanopolyynes relative to methanol in post-shock gas in the outflow of Sgr B2(N)’s main hot core. In contrast, the strong correlation of the O-bearing content of Sgr B2(N2)’s hot cores with G+0.693 and the poor one with TMC-1 suggest that, in comparison to the processes that control the O-bearing content of hot cores, shocks do not greatly modify the relative abundances of the class of O-bearing species while low-temperature gas-phase chemistry does to some extent.

We showed in Sect. 3.5 that there is a tight correlation between the chemical compositions of the low-mass protostar IRAS 16293B and that of N2b, AN02, and AN03 after normalizing the column densities by class of molecules, but the classes are globally shifted in abundance with respect to each other: N-bearing molecules are underabundant by about 2 oom in IRAS 16293B, O+N-bearing and S-bearing species by about 1 oom. Similar shifts between classes of molecules also exist in the case of G31.41, but they are less pronounced: 1−1.5 oom for the N-bearing and <1 oom for the O+N-bearing and S-bearing species. G31.41 seems to be forming stars of lower mass than Sgr B2(N2)’s sources (see Sect. 5.2), but the difference may only be a factor of a few while there is a much larger gap in mass between G31.41 and IRAS 16293B (>1 oom). Therefore it seems unlikely that the abundance shifts between classes of molecules result primarily from physical differences between regions forming low-mass and high-mass stars, such as the temperature during the prestellar phase, the duration of the prestellar phase, or the level of UV radiation during the warm-up phase.

Shocks may play a role in hot cores and corinos, via, for example, accretion shocks of infalling material onto a circumstellar disk. This could in turn lead to somewhat different desorption behavior, including only partial desorption of ices, given a sufficiently weak shock. This could lead to only the outer layers being desorbed. These outer layers are often considered to be relatively water-poor, and have been associated with observed apolar spectral signatures of CO and CO₂ (Garrod & Pauly 2011), while being richer in CO and its products such as methanol. It is likely that these layers would also be rich in O-bearing COMs (which are also ultimately related to CO), if they exist in the ice. On the other hand, the deeper ice layers are shown by models to have a much greater NH₃ fraction. One might expect that amine-group bearing molecules would also be more abundant in those deeper ice layers, while their binding energies with water also tend to be greater than many of the CO-related species. Thus if only weak accretion shocks are active, only the weakly bound upper layers of O-bearing molecule-rich ice might be released into the gas. In hot cores, the accretion shocks may be stronger (leading to more complete ice desorption, either by sputtering or thermal desorption), and the heating of the grains by the protostar is likely to lead to a more global, sustained, and vigorous heating of the grains than in a hot corino. Hot cores may therefore undergo a more complete desorption of the ices than hot corinos, in general, leading to greater N-bearing species being released.

Other mechanisms may also lead to sporadic heating of the ices, such as accretion outbursts. Low-mass protostars may flashheat their surroundings multiple times (e.g. Vorobyov & Basu 2015). This could again lead to the desorption of only the outer and more weakly bound ice layers, which would be O-rich and N-poor. It is also suggested, based on simulations (Meyer et al. 2021), that outbursts are also common for high-mass protostars.

In principle, any of these processes that may lead to only partial ice desorption might well encourage the preferential release of O-rich material, while leaving deeper, N-rich material in place. Differences in molecular O/N ratios between sources would then be an indicator of how violently and completely the ice mantles are desorbed.

Unfortunately, while current chemical models can operationalize differences in binding energies between species, hot core models do not currently distinguish between ice layers beyond the typical bulk+surface-layer paradigm, and they are also not yet capable of taking local ice surface composition into account in the determination of binding energies. Testing the above idea will require substantial upgrades in model capabilities.

5.4 Chemical model behavior

The chemical model grid is capable of reproducing the observed abundances to a reasonable degree of accuracy (within 1 oom) in most cases. However, the rather coarse resolution of the grid means that we are unable to determine clearly the possible variations in physical conditions that would lead to the observed chemical distinctions between the five sources. The method imposes the requirement that a single value of CRIR be used in the matching routine, under the assumption that the same rate should be applicable to the entire region. A narrow range of gas density values is also imposed, based on observational constraints. Under these conditions, a single set of model parameters is found to be the best match to four of the sources. It is plausible that with a much higher resolution grid, better individual matches could be obtained for each source that would still be consistent with the imposed restrictions.

The matching method is nevertheless valuable in identifying two key parameter values: firstly, that the CRIR must necessarily be “high” to best match all five sources; and secondly, that the warm-up timescale for all best-matching models is 2 × 10⁴ yr, the shortest value tested. Although the warm-up timescale in these models is a parameterized value that need not necessarily match the true dynamical timescale, the best-match value is both consistent across sources and in keeping with expected lifetimes for hot cores, on the order 6 × 10⁴ yr (Nony et al. 2024; Bonfand et al. 2017). We note that our grid also includes values 3.56 and 6.32 × 10⁴ yr, which did not produce the best match for any of the sources.

Again, with four out of five sources being reproduced by the same chemical model, the modeling comparison itself has limited value in determining the origins of the chemical differences between sources. However, the models can help to indicate what conditions could be adjusted to produce a closer match.

For example, the lower observed values of HNCO and NH₂CHO toward AN06, as opposed to N2b, might indicate that different CRIR values are required between sources. The models indicate that the extreme ζ = 100 ζ₀ value suppresses the abundances of HNCO and NH₂CHO in the ices during the cold collapse stage, due to the enhanced UV field. Imposition of such a strict agreement in CRIR between Sgr B2(N2) sources might therefore be inappropriate; however, to account for a factor of nearly two orders of magnitude would require substantial variation, if the differences in these molecular abundances were caused by this alone.

Alternatively, other UV ionization sources could be active, requiring a slightly lower overall CRIR for all sources, and allowing more influence for variations in visual extinction between sources. Moreover, a more careful treatment of the 2-D/3-D structure during stages 1 and 2 would help to determine how important external UV may be to the eventual detected abundances of certain COMs, by allowing regions of the core with different initial visual extinctions to contribute to the molecular abundances that are ultimately observed. Using the simple physical model that we have employed here, it is not possible to determine which regions of a natal cold core may later contribute most strongly to the release of icy COMs into the gas phase. Also, as noted above, shocks or luminosity outbursts could be important to the release of the ice mantles, determining both the location of COM desorption and the degree of ice loss, which could in turn lead to variations in which molecules are released. Testing such ideas requires dedicated coupled chemical-dynamical modeling that cannot currently be carried out in high enough volumes to allow for a model-grid comparison similar to what is used here.

5.5 Comparison to ALMA 1.2 mm survey of Sgr B2(N)

Möller et al. (2025) derived the chemical composition of several dozen hot cores identified by Sánchez-Monge et al. (2017) on the basis of an imaging spectral line survey performed with ALMA between 211 and 275 GHz with a spectral resolution of 490 kHz (0.7−0.5 km s⁻¹) and an angular resolution ranging from 0.4^′′ to 0.7^′′. They used super-resolution data cubes restored with a beam of 0.4^′′. For each source, they modeled a spectrum averaged over a polygon (see the shaded blue polygons in their Fig. 2). Following Sánchez-Monge et al. (2017), they divided Sgr B2(N2) into three sources (AN02, AN03, and AN06) with polygon sizes on the order of 2^′′ × 1^′′. Their polygon around AN02 also includes the position that we named N2b.

Figure F.5 compares the column densities that we derived toward AN02, AN03, AN06, and AN06c2 to the column densities of the “Core Components” reported for AN02, AN03, and AN06 in Tables L.57, L.59, and L. 65 of Möller et al. (2025), respectively. In the case of AN02, most molecules were modeled by these authors with only one velocity component with a larger line width and thus we compared their 1.2 mm column densities to the sum of the column densities that we derived for the two velocity components that we identified in this source with ReMoCA. For both C₂H₅CN and CH₃CN, we used only the main component reported by Möller et al. (2025) and ignored the other component that has a six orders of magnitude lower column density⁷. For CH₃OH, we added the column densities of the two components modeled by Möller et al. (2025). In the case of AN03 (Table L.59 of Möller et al. 2025), we selected only the high-column-density components derived for CH₃CN and CH₃OH, and we ignored the HC₃N component that has a line width smaller than 0.5 km s⁻¹. Finally, Möller et al. (2025) modeled the spectrum of AN06 with two velocity components similar to ours only for C₂H₅CN and OCS. Most other molecules were modeled with a single velocity component with a larger line width. Therefore, apart from C₂H₅CN and OCS, Figs. F.5c and d display the same 1.2 mm column densities.

Given that Möller et al. (2025) used spectra averaged over large polygons while we analyzed spectra from a single pixel, we do not expect the ReMoCA column densities to match exactly the 1.2 mm column densities. While there is a decent overall correlation between the column densities of both studies, Fig. F.5a shows that the ReMoCA column densities of AN02 are generally higher (by a factor of ∼2) than the 1.2 mm ones. This must result in part from the fact that the 1.2 mm column densities were derived from spectra that were averaged over typical sizes of 2^′′ × 1^′′ while the ReMoCA ones are peak column densities. In addition to this, the 1.2 mm column densities of AN02 were derived assuming an emission size of 1.7^′′ while, guided by their integrated intensity maps, we modeled most molecules with emission sizes (FWHM) of 0.7–1.0′′ Furthermore, we do not know if the column densities reported by Möller et al. (2025) account for vibrational and conformational corrections. These corrections can be as high as a factor 1.5–2 for some of the molecules displayed in Fig. F.5 (for instance C₂H₃CN, C₂H₅CN, HC₃N, CH₃OCHO). The situation is similar for AN 03, which was modeled with an emission size of 1.6^′′ by Möller et al. (2025) versus 0.7−1.0^′′ in our study, with a slightly better match between the two studies compared to AN02 (Fig. F.5b).

The case of AN06 looks different (Figs. F.5c and d). The 1.2 mm column densities are in many cases higher than the ReMoCA ones (by more than a factor of two). We think that this is due to the fact that the aperture used by Möller et al. (2025) includes more emission from the Sgr B2(N2) dense core, meaning more contamination from the AN03 region, than our single-pixel spectra. As a matter of fact, our Weeds models for AN06 were as far as possible optimized based on setups S4 and S5, which have the smallest beams in our survey (0.3–0.5′′). The line emission of AN06 in the ReMoCA setups with larger beams (S1–S3) is generally underestimated by our Weeds models, due to contamination by the AN03 environment, while it is well fitted for S4 and S5.

One molecule stands out in Fig. F.5. The column densities reported for NH₂D by Möller et al. (2025) are much higher (up to two orders of magnitude for AN03) than the column densities derived from the ReMoCA survey. The ReMoCA column density toward AN03 was derived from four detected transitions while Möller et al. (2025) detected only one transition in some of their sources. In their Fig. F.20, they show the spectra toward AN05 and AN08 only, and so it is unclear to us if the NH₂D column densities reported for AN02, AN03, and AN06 are reliable. Their Fig. F.20 indicates that there is significant contamination by other molecules at the frequency of the NH₂D transition toward AN08. Therefore, it could well be that the emission detected toward AN02, AN03, and AN06 at the frequency of this NH₂D transition is dominated by these contaminating molecules and does not trace NH₂D.

The comparison presented in this section strengthens our confidence in the reliability of the column densities derived from the ReMoCA survey. They faithfully represent the genuine chemical composition of the four hot cores AN02, AN03, AN06, and N2b. Thanks to the lower degree of spectral confusion at 3 mm compared to the 1.2 mm range⁸, the ReMoCA survey has allowed us to detect many more molecules (up to 58 toward AN03), in particular many more COMs: while Möller et al. (2025) reported the detection of ten COMs toward AN02, AN03, and AN06, we managed to identify 22,24, and 17 COMs toward these sources, respectively (see Table 3).

5.6 Unidentified lines

There are still many unidentified lines in the ReMoCA spectra of the sources embedded in Sgr B2(N2) (see Fig. D.1). Table B.1 provides the list of unidentified lines that are brighter than 10 K (signal-to-noise ratio higher than 10–30 depending on the setup) in the spectrum of N2b. There are 255 such lines, which translates into about eight bright unidentified lines per GHz on average. As was already the case for our earlier singledish survey of Sgr B2(N) (Belloche et al. 2013), we think that most of these bright unidentified lines correspond to rotational transitions from within vibrationally excited states of molecules which have their vibrational ground state and in some cases a few vibrational states already included in our model but for which spectroscopic predictions of higher vibrational states are still missing in our database, for instance ethyl cyanide or ethanol.

6 Conclusions

We used the imaging spectral line survey ReMoCA performed with ALMA to probe the chemical composition of Sgr B2(N2), the secondary hot molecular core of the high mass star-forming protocluster Sgr B2(N). At the angular resolution of the survey, Sgr B2(N2) consists of four hot cores (N2b, AN02, AN03, and AN06). Two velocity components were detected toward AN06. The main component traces a hot core associated with a compact dust continuum source. It is likely in the foreground or background of the dense core Sgr B2(N2). The second velocity component, which we called AN06c2, traces the edge of Sgr B2(N2). AN02 is associated with a dust continuum source and possibly drives an outflow. It is likely younger than AN03 which is associated with a HCH II region. N2b is associated with a fainter dust continuum source, has narrow line widths, and is possibly younger than AN02 and AN03. All four sources have line-rich spectra. We identified up to 58 molecules, including up to 24 COMs. In addition to these molecules, many less abundant isotopologs were also detected. We derived the molecular composition of the four hot cores as well as AN06c2 under the assumption of LTE. The main results of our analysis are the following:

The pairs of sources that show the best correlations of their chemical compositions are AN02/AN03 and AN06/AN06c2. AN03 stands out for its S-bearing molecular content that poorly correlates with that of the other sources, and AN06 for its underabundant O+N-bearing molecules.
AN06 may be in a similar evolutionary stage as N2b. Their compositions correlate with each other, albeit with a larger dispersion than AN02 versus AN03. The most striking difference between AN06 and N2b is that HNCO and NH2CHO are almost two orders of magnitude less abundant in the former.
Protonated molecules XH⁺and radicals X^• were not detected. The most stringent constraint was obtained in both cases for N2b, with abundance ratios XH⁺/X and X^•/XH lower than 10⁻³.
Molecules with a double bond are in nearly all cases much less abundant than the corresponding single-bond species, by up to at least two orders of magnitude. The only clear exception is the pair CH₂NH/CH₃NH₂ with a ratio close to unity.
Only one complex carboxylic acid was (tentatively) detected. If the abundance ratio NH₂CH₂COOH/NH₂CH₂CN is similar to the ratio CH₃COOH/CH₃CN then the abundance of glycine must be at least one order of magnitude below its current ReMoCA upper limit.
The abundances of series of homologous molecules drop by about one order of magnitude at each further step in complexity, except for NH₂CN, which is less abundant than NH₂CH₂CN by one order of magnitude, and CH₃CN, which has a similar abundance as C₂H₅CN.

To gain more insights into the workings of interstellar chemistry, we compared the chemical composition of Sgr B2(N2)’s hot cores to that of other sources that have been studied in detail in the literature: the lower-density Sgr B2 source G+0.693, thought to be affected by shocks, the hot core G31.41 in the Galactic disk, the hot corino IRAS 16293B, and the cold starless core TMC-1, both located in the Solar neighborhood. We focused in particular on the behavior of four classes of molecules (O-bearing, N-bearing, O+N-bearing, and S-bearing). The main results of this comparison are the following:
The source that shows the tightest correlation with N2b, AN02, and AN03 is G31.41. This implies that the overall chemical composition of hot cores is relatively insensitive to the environmental features that distinguish the Galactic center region from the Galactic disk.
For each class of molecules taken separately, there is also a good correlation between IRAS 16293B and Sgr B2(N2)’s hot cores, but the four classes are shifted with respect to each other. Such a segregation also exists for G31.41 relative to Sgr B2(N2)’s sources but it is less pronounced.
There is no overall correlation between G+0.693 and Sgr B2(N2)’s hot cores. The lack of correlation results mainly from the N-bearing species, while the other classes correlate better. The S-bearing species are more prominent in G+0.693.
There is absolutely no correlation between the compositions of TMC-1 and Sgr B2(N2)’s hot cores.
Among the four classes of molecules, the class of N-bearing species is the one that reveals the largest variance. Firstly, its abundance distribution with respect to Sgr B2(N2)’s hot cores goes from a tight correlation (G31.41) to no correlation (G+0.693 and TMC-1). Secondly, this class shows overall shifts with respect to the other classes that can be large, up to two orders of magnitude (IRAS 16293B).
In contrast, the class of S-bearing molecules has the smallest variance, with a high degree of correlation for G31.41, IRAS 16293B, and G+0.693 with respect to Sgr B2(N2).

We also compared the molecular composition of Sgr B2(N2)’s sources to a grid of generic hot-core models computed with the chemical kinetics code MAGICKAL. The main results of this comparison are the following:
We confirm previous evidence for a strongly elevated CRIR for Sgr B2(N2). The models produce their best collective match with observational abundances for the five sources adopting a CRIR value ζ=1.3 × 10⁻¹⁵ s⁻¹, which is the highest value tested. The shortest warm-up timescale (2 × 10⁴ yr) is found to produce the optimum match with all sources.
The chemical model grid is not able to distinguish adequately the chemical differences between sources. Four of the sources are best matched by a single model, under conditions where the gas density is constrained to an observational range of values. The models therefore provide little direct indication of the origins of chemical differences between the sources.

The model grid might provide more information given higher resolution in physical conditions. However, the match with observations may be much further improved by the use of an explicit treatment of gas dynamics in tandem with the chemistry.

We conclude from the observed behavior of the class of N-bearing molecules that this class reacts more sensitively to shocks (G+0.693), low-temperature gas phase chemistry after nonthermal desorption (TMC-1), or density (G+0.693 and TMC-1) than the classes of O-bearing and S-bearing species. A possible interpretation of the segregation between N-bearing and O-bearing molecules is that only partial ice desorption might encourage the preferential release of the outer ice layers that are rich in CO and related O-bearing species, while leaving deeper, N-rich material in place. The overall abundance shifts observed in the gas phase between the classes of N-bearing and O-bearing molecules may thus indicate how violently and completely the ice mantles are desorbed. Testing this idea will require substantial improvements of chemical models that do not currently distinguish between ice layers beyond the typical bulk+surface-layer paradigm.

Acknowledgements

AB thanks all the physicists who sent him spectroscopic predictions over the past two decades, in particular E. Alonso, J. L. Alonso, B. Arenas, L. Bizzocchi, L. Bonah, S. Brünken, C. Cabezas, Ning Chen, L. Coudert, C. Endres, Z. Fried, E. Gougoula, S. Gruet, B. Heyne, V. Ilyushin, Z. Kisiel, I. Kleiner, K. Kobayashi, L. Kolesniková, J. Koucký, L. Margulès, A. Maris, M.-A. Martin-Drumel, B. McGuire, C. Medcraft, I. Medvedev, M. Melosso, P. Misra, R. Motiyenko, M. Ordu, M. Sanz-Novo, M. Schnell, P. Stahl, O. Zingsheim, and Luyao Zou. We value the long-term efforts of the spectroscopic community that crucially feed the databases used by astrophysicists. RTG thanks the National Science Foundation for funding through the Astronomy & Astrophysics program (grant number 2206516). We thank B. Shope for advice on the chemical model grid. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2016.1.00074.S. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. The interferometric data are available in the ALMA archive at https://almascience.eso.org/aq/. Part of this work has been carried out within the Collaborative Research Center 956, subproject B3, funded by the Deutsche Forschungsgemeinschaft (DFG, the German research foundation) – project ID 184018867. HSPM acknowledges support by the DFG through the Collaborative Research Center 1601, sub-projects A4 and Inf – project ID 500700252.

Appendix A Column densities from the literature

Table A.1 reports column densities and rotational temperatures of G31.41, IRAS 16293B, G+0.693, and TMC-1 that we collected from the literature. These column density values were used inFigs. 16–20.

Table A.1

Column densities of G31.41, IRAS 16293B, G+0.693, and TMC-1 collected from the literature.

Appendix B Bright unidentified lines

Table B.1 provides the list of unidentified lines that are brighter than 10 K in the ReMoCA spectrum of N2b.

Table B.1

List of unidentified lines brighter than 10 K in the ReMoCA spectrum of N2b.

Appendix C Spectroscopic references

Table C.1 provides the list of relevant references for the 448 spectroscopic entries that were used in this work to model the observed spectra or derive upper limits to the column densities of nondetected molecules.

Table C.1

Spectroscopic references of molecules analyzed in this work.

Appendix D Spectra

Figure D.1 shows the complete ReMoCA spectral survey toward five positions in Sgr B2(N): the four hot-core positions N2b, AN02, AN03, and AN06, and the ultracompact (UC) H II region K4. The continuum-subtracted spectra were shifted vertically by 0, 200,400,600, and 900 K, respectively. The continuum level is indicated in blue on the right below each spectrum. LTE synthetic spectra computed with Weeds and containing the contribution of all identified molecules are overlaid in red on the hot-core spectra. The parameters of these LTE models are listed in Tables E.1–E.5. The molecular identification of a selection of spectral lines is indicated in various colors: magenta for rotational lines in the vibrational ground state, teal for rotational lines in vibrationally excited states (with the suffix v added to the label), and orange for lines seen in absorption. Green labels above the K4 spectrum mark the frequencies of hydrogen and helium recombination lines. To facilitate the visual matching between the spectral lines and their magenta or teal labels, short vertical bars are displayed in the same color below each hot-core spectrum. Each panel corresponds to half a spectral window (W) of a single spectral setup (S). The setup index (from 1 to 5) and the spectral window index (from 0 to 3) of each panel are indicated in the bottom left corner along with the HPBW. The spectra are displayed in brightness temperature scale and were corrected for primary beam attenuation. The panels are ordered in increasing frequency of the spectral windows. There is a substantial frequency overlap between the upper sideband of setups 1 and 2 and the lower sideband of setups 4 and 5. The angular resolution of these pairs of setups differs by a factor ∼1.5, which motivates our decision to display all spectral windows in Fig. D.1, despite their frequency overlap.

Fig. D.1

ALMA continuum-subtracted spectra of the hot core positions N2b, AN02, AN03, AN06 and the UCH II region K4 (black). LTE synthetic spectra are overlaid in red. See Appendix D for a detailed description of the figure.

Appendix E LTE model parameters and column density upper limits

Tables E.1–E.5 list the parameters of our best-fit LTE models of N2b, AN02, AN03, the second velocity component toward AN06 (called AN06c2), and the first velocity component toward AN06, respectively. Tables E.6–E.10 provide the column density upper limits derived for 146 molecules that were not detected in any of the investigated positions.

Table E.1

Parameters of our best-fit LTE model of N2b.

Table E.2

Parameters of our best-fit LTE model of AN02.

Table E.3

Parameters of our best-fit LTE model of AN03

Table E.4

Parameters of our best-fit LTE model of the second velocity component toward AN06 (AN06c2).

Table E.5

Parameters of our best-fit LTE model of AN06.

Table E.6

Column density upper limits toward N2b.

Table E.7

Column density upper limits toward N2b.

Table E.8

Column density upper limits toward AN03.

Table E.9

Column density upper limits toward the second velocity component toward AN06 (AN06c2).

Table E.10

Column density upper limits toward AN06.

Appendix F Additional figures

Figure F.1 shows the chemical composition (relative to methanol) of AN06, AN06c2, AN03, and AN02, normalized to the composition of N2b. Figure F.2 shows the same as Fig. 8 but with a different color scheme. Figure F.3 shows the matching parameters for all tested chemical models. Figure F.4 displays correlation plots of rotational temperatures for various pairs of positions in Sgr B2(N2). Figure F.5 compares the ReMoCA column densities to the column densities derived by Möller et al. (2025).

Fig. F.1

Same as for Fig. 7 but normalized to N2b. The dashed lines indicate values of 0.1, 1, and 10.

Fig. F.2

Same as Fig. 8 but with colors coding for the class of molecules (black for O-bearing, blue for O+N-bearing, red for N-bearing, green for pure hydrocarbon, and yellow for S-bearing).

Fig. F.3

Matching parameters for all tested models, using the density-restricted setup. Each curve shows the matching parameter for a single source as a function of all the models in the grid with which it is compared, using the same fixed CRIR. The results are arranged in bands, one for each CRIR. The bands are arranged left to right in order of best to worst overall matching parameter. Within each band, the models contributing to each curve are arranged in order of increasing (i.e. worse) matching parameter, as determined independently for each source comparison.

Fig. F.4

Correlation plots of rotational temperatures for various pairs of positions. The x- and y-axes of each panel correspond to the positions written in the bottom right and top left corners, respectively. The color coding of the molecules is the same as in Fig. 8, plus black for methanol, and is indicated on the right. The plain line indicates the one-to-one relation.

Fig. F.5

Comparison of the column densities obtained from the ReMoCA survey toward AN02, AN03, AN06, and AN06c2 to those derived from an ALMA survey of Sgr B2(N) at 1.2 mm by Möller et al. (2025). The ReMoCA column densities correspond to peak column densities while the 1.2 mm column densities were derived from spectra averaged over the shaded blue polygons shown in Fig. 2 of Möller et al. (2025). Panels c and d show the same 1.2 mm column densities except for C₂H₅CN and OCS which were modeled with two velocity components similar to the components that we extracted from the ReMoCA survey. The dashed, dotted, and plain lines and the color coding and labels of the molecules are the same as in Fig. 8.

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See http://www.iram.fr/IRAMFR/GILDAS

https://www.iram.fr/IRAMFR/GILDAS/doc/html/weeds-html/weeds.html

https://cdms.astro.uni-koeln.de/

⁴

https://lsd.univ-lille.fr/

⁵

https://spec.jpl.nasa.gov/

⁶

A comparison between G+0.693 and Sgr B(N2) on the basis of our previous survey EMoCA, which had a lower angular resolution (1.6^′′) and could not disentangle the four sources embedded in Sgr B2(N2), was presented by Jiménez-Serra et al. (2025).

⁷

On top of its extremely low column density, the weak component of C₂H₅CN has a puzzling velocity offset of −159 km s⁻¹ in Table L.57 of Möller et al. (2025). We do not know what this component represents.

⁸

As explained in Sect. 1, our previous single-dish survey of Sgr B2(N) at 3, 2, and 1.3 mm already showed that spectral confusion and optical depth of the continuum emission are severe issues at 1.3 mm for this source (Belloche et al. 2013).

All Tables

Table 1

Beam sizes and noise levels.

In the text

Table 2

Properties of the positions analyzed within Sgr B2(N2).

In the text

Table 3

List of molecules detected toward N2b, AN02, AN03, AN06, and AN06c2 with the ReMoCA survey.

In the text

Table 4

Correlations between the chemical composition of Sgr B2(N2) sources and sources from the literature.

In the text

Table 5

Range of final (i.e. stage-2) gas densities from the model grid used in the comparison with each source, in the density-restricted setup.

In the text

Table A.1

Column densities of G31.41, IRAS 16293B, G+0.693, and TMC-1 collected from the literature.

In the text

Table B.1

List of unidentified lines brighter than 10 K in the ReMoCA spectrum of N2b.

In the text

Table C.1

Spectroscopic references of molecules analyzed in this work.

In the text

Table E.1

Parameters of our best-fit LTE model of N2b.

In the text

Table E.2

Parameters of our best-fit LTE model of AN02.

In the text

Table E.3

Parameters of our best-fit LTE model of AN03

In the text

Table E.4

Parameters of our best-fit LTE model of the second velocity component toward AN06 (AN06c2).

In the text

Table E.5

Parameters of our best-fit LTE model of AN06.

In the text

Table E.6

Column density upper limits toward N2b.

In the text

Table E.7

Column density upper limits toward N2b.

In the text

Table E.8

Column density upper limits toward AN03.

In the text

Table E.9

Column density upper limits toward the second velocity component toward AN06 (AN06c2).

In the text

Table E.10

Column density upper limits toward AN06.

In the text

All Figures

	Fig. 10 Same as Fig. 9, but for radicals (red) with respect to the hydrogenated form (black).
In the text

	Fig. 11 Same as Fig. 9, but for molecules with a double (red) or triple (green) bond with respect to the saturated form with a single bond (black).
In the text

	Fig. 12 Same as Fig. 9, but for molecules with O-bearing functional groups -CHO and -COOH with respect to molecules with a -CN functional group.
In the text

	Fig. 13 Same as Fig. 9, but for the reduced form (-CH₃. functional group) with respect to the aldehyde form (-CHO functional group).
In the text

	Fig. 14 Same as Fig. 9, but for series of molecules with an additional CH₂ group in their backbone at each further step in complexity.
In the text

	Fig. 15 Same as Fig. 9, but for groups of structural isomers. The elemental composition of the groups is labeled along the x axis. The list of molecules belonging to each group is provided at the bottom of the figure with their respective colors as used in the plots.
In the text

	Fig. 16 Same as Fig. F.2 but comparing sources from the literature to N2b.
In the text

	Fig. 17 Same as Fig. F.2 but comparing sources from the literature to AN02.
In the text

	Fig. 18 Same as Fig. F.2 but comparing sources from the literature to AN03.
In the text

	Fig. 19 Same as Fig. F.2 but comparing sources from the literature to AN06c2.
In the text

	Fig. 20 Same as Fig. F.2 but comparing sources from the literature to AN06.
In the text

	Fig. 21 Overall matching parameter obtained for the comparison of the grid of chemical model results with observational abundances of all species across all sources, assuming a uniform CRIR. A lower matching parameter indicates a closer match between models and observations. Panel a: unrestricted density. Panel b: density restricted to the ranges indicated in Table 5.
In the text

Fig. 22

In the text

	Fig. D.1 ALMA continuum-subtracted spectra of the hot core positions N2b, AN02, AN03, AN06 and the UCH II region K4 (black). LTE synthetic spectra are overlaid in red. See Appendix D for a detailed description of the figure.
In the text

	Fig. F.1 Same as for Fig. 7 but normalized to N2b. The dashed lines indicate values of 0.1, 1, and 10.
In the text

	Fig. F.2 Same as Fig. 8 but with colors coding for the class of molecules (black for O-bearing, blue for O+N-bearing, red for N-bearing, green for pure hydrocarbon, and yellow for S-bearing).
In the text

Fig. F.3

In the text

	Fig. F.4 Correlation plots of rotational temperatures for various pairs of positions. The x- and y-axes of each panel correspond to the positions written in the bottom right and top left corners, respectively. The color coding of the molecules is the same as in Fig. 8, plus black for methanol, and is indicated on the right. The plain line indicates the one-to-one relation.
In the text

Fig. F.5

In the text

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