Free Access
Issue
A&A
Volume 628, August 2019
Article Number A2
Number of page(s) 25
Section Interstellar and circumstellar matter
DOI https://doi.org/10.1051/0004-6361/201834527
Published online 26 July 2019

© ESO 2019

1 Introduction

The molecular composition of a star-forming region can be used to probe the physical conditions of its environment, define its evolutionary stage, identify chemical processes, and, in addition, sets the stage and starting conditions for chemistry in discs and eventually planetary systems. A large number of molecular species ranging from simple to complex (molecules consisting of six or more atoms) have been identified in various interstellar environments, from giant molecular clouds to dense cores, protostars, and protoplanetary discs (see reviews by Herbst & van Dishoeck 2009; Caselli & Ceccarelli 2012; Tielens 2013; Sakai & Yamamoto 2013). In the context of the formation of high- and low-mass stars, the hot core or hot corino stage displays a particularly rich chemistry. At this stage, the young protostar heats its surroundings and creates a bubble of warm (~200 K) gas, enriched in complex molecules. This complexity is a result of chemistry in the warm gas combined with thermal desorption of the icy mantles of dust grains (e.g. Charnley et al. 1992).

Over the last decades, many surveys, mostly using single-dish telescopes, have been undertaken to investigate the chemical complexity of high-mass hot cores (e.g. Blake et al. 1987; Hatchell et al. 1998; van der Tak et al. 2000; Ikeda et al. 2001; Bisschop et al. 2007; Kalenskii & Johansson 2010; Isokoski et al. 2013; Rivilla et al. 2017; McGuire et al. 2017; Suzuki et al. 2018; McGuire et al. 2018). Much focus has been on the hot cores associated with Orion and Sagittarius B2 (hereafter Sgr B2), famous for their high abundances of complex molecules (see e.g. Neill et al. 2014; Crockett et al. 2014, and references therein), although recently, the low-mass counterparts of these sources have also been under investigations (e.g. Schöier et al. 2002; Cazaux et al. 2003; Bottinelli et al. 2004). A wealth of information on the chemistry associated with hot cores has been provided by these observations, although most are limited by the generally large beam sizes of single-dish telescopes. The consequence of this is that observations do not only sample the hot core, but also the surrounding environments associated with the protostar, such as the large-scale envelope or outflows (e.g. Fayolle et al. 2015). Generally, this results in multi-component molecular emission, where each component may be characterised by a different line width, velocity, excitation temperature, and column density. Furthermore, beam dilution effects may result in large uncertainties on derived molecular column densities if not accounted for correctly.

The emergence of interferometers such as the Submillimeter Array (SMA), the NOrthern Extended Millimeter Array (NOEMA) and, in particular, the Atacama Large Millimeter/ submillimeter Array (ALMA), which provide much higher spatial resolutions than single-dish telescopes, has made it possible to study the molecular emission associated with hot cores on much smaller scales than were previously accessible. This means that, for the first time, an opportunity to “look into” the hot cores themselves is provided whereby the challenges of many single-dish studies can be overcome. In addition, the unprecedented sensitivity of ALMA has made possible the detection of a wealth of weak lines ensuring a more accurate characterisation of the chemistry associated with the cores.

To date,the chemical inventory of only a handful of sources has been extensively studied with interferometers. These include the low-mass protobinary system IRAS 16293–2422 (hereafter IRAS 16293, Jørgensen et al. 2016) and the high-mass, star-formingregions associated with Sgr B2(N) (Belloche et al. 2016) and Orion KL (Brouillet et al. 2013; Pagani et al. 2017; Favre et al. 2017; Tercero et al. 2018; Peng et al. 2019). Therefore, there is a substantial need for the continued investigation of additional hot cores in order to build up a database of the molecular inventories and temperatures characterising these sources. Such a database will provide the statistics needed for new insights into the physical and chemical processes at play during the formation of hot cores and will help the classification of sources according to evolutionary stage.

To this end, the high-mass hot core of AFGL 4176 has been investigated with ALMA and for the first time a comprehensive study of the chemical inventory of the source is presented. The results of this work are compared with other high- and low-mass sources, in addition to the predictions of hot core chemical models.

AFGL 4176, located in the southern hemisphere at 13h 43m01.704s, − 62°08′51.23′′ (ICRS J2000), was first identified by Henning et al. (1984) through its bright infrared spectrum as a young and massive star embedded in a thick dusty envelope. The source has been further characterised by Beltrán et al. (2006), who used large-scale millimetre continuum observations carried out with the Swedish-ESO Submillimetre Telescope (SEST) to identify a compact core of approximately 1120 M with a diameter of 1 pc and luminosity of 2 × 105 L (assuming a distance of 5.3 kpc). It should be noted, however, that the distance to AFGL 4176 is not well constrained and cited values range from 3.5 to 5.3 kpc (see Boley et al. 2012, and references therein), with the most frequently cited distance being 4.2 kpc, based on observations of CH3 OH masers (Green & McClure-Griffiths 2011). However, in this work we will assume a distance of 3.7 kpc, based on the recent second release of Gaia data, which places the source at a distance of 3.7 kpc (Bailer-Jones et al. 2018).

In addition to the large-scale envelope, strong evidence that the system contains a Keplerian-like disc is presented by Johnston et al. (2015) who use observations of CH3 CN obtained with ALMA to trace the disc kinematics on scales of ~1200 au. A disc-like structure is consistent with the models reported by Boley et al. (2012) who combine interferometric and photometricobservations of AFGL 4176 and use radiative transfer and geometric models to characterise the source. Although the observationsare generally well described by one-dimensional models, Boley et al. (2012) find substantial deviations from spherical symmetry at scales of tens to hundreds of astronomical units. On these scales, the observations are better described by a multi-component model consisting of a Gaussian halo and an inclined circumstellar disc. Knots of shocked H2 emission have also been identified around AFGL 4176, potentially indicating an outflow, though no preferred spatial direction was revealed (De Buizer 2003).

A limited number of detections of molecular species have been reported towards AFGL 4176. CO2 and H2 O have been identified in observations carried out with the Infrared Space Observatory (van Dishoeck et al. 1996; van Dishoeck & Helmich 1996; Boonman et al. 2003) and detections of CO, NH3, and CH3 CN by the Atacama Pathfinder Experiment (APEX) and ALMA have been reported by Johnston et al. (2014, 2015). In addition, a number of CH3OH masers are reported in the vicinity of the source (Phillips et al. 1998; Green & McClure-Griffiths 2011). However, so far no reports of a more comprehensive chemical inventory of the source exist.

This paper presents an extensive study of the molecular species detected towards AFGL 4176, in addition to those previously reported. The work is based on the same set of high sensitivity, high resolution ALMA observations as analysed by Johnston et al. (2015), but focuses on identifying and characterising all molecular species with transitions in the observed frequency range associated with the source, rather than the disc kinematics. The high sensitivity of ALMA makes it possible to identify weak and optically thin lines while the unique spatial resolving power ensures that the analysed emission stems from the disc around the central forming star rather than the large-scale surrounding envelope.

The structure of the paper is as follows. Section 2 introduces the observations, calibration process, and methods used for identifying molecular species. Section 3 lists all detected species, our derived molecular column densities, and excitation temperatures and discusses the spatial distribution of selected molecules. Section 4 compares the results for AFGL 4176 with observations of other objects and with model predictions. Finally, Sect. 5 summaries the results and conclusions.

2 Observations and methods

2.1 Observations

Observations of AFGL 4176 were carried out with ALMA during Cycle 1 (program 2012.1.00469.S, see Johnston et al. 2015, for first results) with 39 antennas in the array, between August 16, 2014 and August 17, 2014 using the Band 6 receivers, covering the frequency range of 211–275 GHz. Four spectral windows were obtained covering a total bandwidth of ~4.7 GHz. These consist of two wide windows of 1875 MHz centred at 240.5 and 254.0 GHz, and two narrow windows of 468.75 MHz centred at 239.0 and 256.3 GHz. The spectral resolution of the observations is 976.6 kHz (~1.2 km s−1) and 244 kHz (~0.3 km s−1) for the wide and narrow windows, respectively. The angular resolution is ~0.35′′, equivalent to ~1285 au at the distance of AFGL 4176.

The data were downloaded from the ALMA archive and reduced via the delivered pipeline script using the Common Astronomy Software Applications (CASA) version 4.2.1. Bandpass and absolute flux calibration was carried out, respectively, using J1617-5848 and Titan, on August 16 and J1427-4206 and Ceres on August 17. The phase and gain calibration was carried out, respectively, using J1308-6707 and J1329-5608 on both days. A conservative flux calibration accuracy of 20% has been adopted. This uncertainty only contributes moderately to the total uncertainty of the presented results. The data were continuum subtracted using the most line-free channels and corrected for primary beam attenuation.

The continuum and line data were imaged separately in CASA version 5.1.1-5 using a pixel size of 0.04′′, a velocity resolution for the spectral cubes of 1.2 km s−1, and Briggs weighting with a robust parameter of 1.5. The peak continuum emission is 29 mJy beam−1 (5.7 K at 247 GHz) with an rms noise of approximately 0.5 mJy beam−1 (0.1 K at 247 GHz) in a beam of 0′′.33 × 0. ′′31. The coordinates of the continuum peak were determined by 2D Gaussian fitting in the image plane to be 13h 43m01.699s± 0.003s, −62°08′51.25′′ ± 0.02′′ (IRCS J2000). Table 1 lists the frequencies covered and the rms noise per 1.2 km s−1 channel derived for each of the spectral windows.

For each of the imaged cubes, a spectrum is extracted at the location of the peak continuum emission. Each spectrum represents the average over an area equivalent to the size of the synthesised beam (~0. ′′3). As a consequence, all derived molecular column densities are thus synthesised beam averaged and probe the warmest, inner regions of the hot core. Figure 1 shows the continuum and the location at which the spectra were extracted. In addition to the main continuum peak, labelled mm1 by Johnston et al. (2015), a secondary peak is observed ~1′′ north-west of the primary peak; this peak is labelled mm2. A counterpart to this secondary peak is observed to the south-east of mm1 (outside the plotted region). These two peaks are located perpendicular to the major axis of mm1 and may indicate a large-scale outflow, consistent with the CO observations presented by Johnston et al. (2015). For this work, however, the focus is on the main continuum peak and all subsequent discussion refers to this source only.

Table 1

Overview of spectral cubes.

thumbnail Fig. 1

Continuum image of AFGL 4176 at 1.2 mm. Contours are [5, 10, 15, 25, 35, 45, 55]σ, with σ = 0.5 mJy beam−1. The peak continuum location at which the spectra have been extracted is marked by the black cross. The synthesised beam (0′′.33 × 0′′.31 ~1210 × 1140 au) is shown in the bottom left corner.

2.2 Methods for line identification and modelling

For the identification of spectral lines, catalogued transition frequencies from the JPL (Jet Propulsion Laboratory1, Pickett et al. 1998) and CDMS (Cologne Database forMolecular Spectroscopy2, Müller et al. 2001, 2005; Endres et al. 2016) molecular databases are compared with the extracted spectra. Observed lines are considered detected if the peak signal-to-noise ratio (S/N) is three or higher. Species with fewer than five detected lines are considered tentative detections. This criterion may be too strict for some of the simpler molecules with sparse, but strong rotational spectra (e.g. SO) and these can likely be considered detections. Using the CASSIS3 line analysissoftware and assuming local thermodynamic equilibrium (LTE) and optically thin lines, a synthetic spectrum is produced for each identified species. This is done by providing CASSIS with the following parameters: excitation temperature, Tex (K), column density of the species, Ns (cm−2), source velocity, vLSR (km s−1), line width at half maximum (km s−1), and angularsize of the emitting region (assumed to be equal to the area of the synthesised beam), θs (′′). We note that Ns is a synthesised-beam-averaged column density, not a source-averaged column density.

For two species, CH3CN and HC3N, vibrationally excited transitions are detected (see Sect. 3). For vibrationally excited CH3CN the JPL database is used. This entry utilises a partition function in which vibrational contributions are taken into account. In contrast, the CDMS entries for vibrationally excited HC3N, and isotopologues thereof, do not include vibrational contributions to the partition function but list these separately. Therefore, vibrational correction factors have been applied to all listed values of vibrationally excited HC3N. These vibrational correction factors are retrieved from the CDMS site4. At 225 K, the vibrational correction factor to the partition function of HC3N and its isotopologues are 1.17 and 1.48, respectively.

Excitation temperatures and column densities are determined for species that have three or more unblended lines detected, that is, lines with a S/N of three or higher, whose emission can mainly be attributed to one molecule, and these span upper state energies of at least 100 K. This is done by creating grids of models varying Tex and Ns and identifying the model with the minimal χ2 as the best fit. The CASSIS software computes the χ2 value for each synthetic spectrum in the model grid, taking into account the channels within a range of ± 10 km s−1 of the catalogue frequency of all unblended lines detected for each species. Table A.1 lists the model grids for each of the fitted species. The uncertainty on Tex and Ns is listed as the standard deviation of models within the 95% confidence level. For most species these are about 20%, though for CH3CHO and (CH2OH)2 the uncertainty on Tex is up to 85%. In both cases, the larger uncertainty on Tex is likely due to the relatively low S/N ~ 4) of a number of the unblended lines detected for these species. The uncertainty on column density ratios with respect to methanol is calculated through the propagation of errors. For species where less than three unblended lines are detected, or species where upper state energies of the detected lines do not span more than 100 K, the column density is derived assuming a fixed excitation temperature. In the case of CH3OH and the isotopologues of SO2, the excitation temperature is assumed to be 120 and 150 K, based on the 13C-methanol isotopologue and main isotopologue of SO2, respectively. For all other species, the excitation temperature is assumed to be 200 K. This value is the average of the best-fit excitation temperatures derived for the 12 species listed at the top of Tables 2 and 3. However, since the spread in best-fit excitation temperatures is fairly large, with a standard deviation of ~70 K, column densities are also derived assuming excitation temperatures of 130 K and 270 K. For most species, the column densities derived at these temperatures are within 50% of the value derived assuming Tex = 200 K. For CH3COCH3, column densities at 130 K and 270 K are within a factor of three of the value derived assuming Tex = 200 K, while for vibrationally excited HC3N, the column density derived at 130 K is a factor of five higher than the value derived at Tex = 200 K.

To ensure that no lines are incorrectly assigned, three checks are conducted. First, that the best-fit model for each species does not predict lines at frequencies, covered by the observations, where no emission is detected. Second, that no other species for which the spectroscopy is known and listed in either of the databases mentioned above can reproduce the line without predicting lines at frequencies where no emission is detected. Finally, that isotopically rare species do not predict lines of the main isotopologuewhere no emission is detected.

The excitation temperatures and column densities for 13CH3OH and CH3CN are derived first becausetheir lines are very numerous, bright, and span a large range of upper state energies. Based on fits to these species, a source velocity of −53.5 km s−1 and full width at half maximum (FWHM) line widths of 6 km s−1 are found. These values are kept fixed for the subsequent fitting of other species to minimise the number of free parameters. However, it should be noted that different molecules may trace different gas components and that the fixed source velocity and line width represent the average conditions of the sources. For example, a slight velocity shift is observed for the transitions of SO2. Leaving thesource velocity as a free parameter for this species results in a best-fit value of −52.0 km s−1. The best-fit column density and excitation temperature at this source velocity are within the uncertainty of the values derived assuming the source velocity to be −53.5 km s−1.

Finally, in order to compare the derived molecular column densities across different objects, CH3OH and H2 are used as references. Methanol is used as a reference because it is usually one of the most abundant species in hot cores and is thought to be the parent molecule for most complex organics. However, because this species is very abundant, many of its lines are optically thick and therefore its column density cannot be derived directly. The column density of CH3OH is instead estimated based on the best-fit value for its 13C-isotopologue, adopting a 12C/13C value of 60, derived assuming a galactocentric distance (dGC) of 6.64 kpc and the relation for 12C/13C reported by Milam et al. (2005). One has to keep in mind that 12C/13C may still deviate from the galactrocentric trend (for example, HC3N/HCC13CN is tentatively found to be ~16 for AFGL 4176, see Sect. 3.3) and therefore can cause an uncertainty in the CH3OH column density.

The H2 column density is determined from the dust continuum according to Eq. (1), (1)

where Iν is the continuum intensity, Ωbeam is the solid angle covered by the beam, = 2.33 is the mean molecular mass per H2 molecule, mH is the mass of the hydrogen atom, κν is the dust opacity, Bν is the Planck function at T = 200 K, and the factor 100 accounts for the gas-to-dust ratio. For our data, Iν = 29 mJy beam−1 and κν = 1.0 cm2 g−1 (Ossenkopf & Henning 1994). The resulting H2 column density is found to be = 4 × 1023 cm−2. This equation does not assume the Rayleigh–Jeans limit, but includes the Planck correction.

Table 2

Summary of detected lines.

Table 3

Summary of derived values of Ns and Tex.

thumbnail Fig. 2

Full model (red), i.e., the sum of synthetic spectra, for all species detected towards AFGL 4176 in the spectral window centred at 256.3 GHz. Frequencies are shifted to the systemic velocity of the region. The data are shown in black. Bottom panel: zoom-in of the top panel to highlight weak lines.

3 Results

A total of 354 lines are identified towards AFGL 4176 with a S/N of three or above. Of these, 324 lines can be assigned to a total of 23 different molecular species or their isotopologues. Fifteen species have five or more detected transitions, while eight species have fewer than five detected transitions and are therefore considered tentative detections. For the remaining 30 lines no match to known transitions was found. A list of frequencies and peak intensities for these unidentified lines is given in Appendix B. With a total covered bandwidth of ~4.7 GHz, the line density is roughly 75 lines per GHz or one line per 13.3 MHz (ALMA Band 6). For comparison, the ALMA Protostellar Interferometric Line Survey (PILS) towards the low-mass protobinary system IRAS 16293B found one line per 3.4 MHz (ALMA Band 7, Jørgensen et al. 2016). The high line density in AFGL 4176 means that detected lines are often blended with emission from other species. Therefore, as noted above, great caution is exercised when lines are assigned to species.

Table 2 lists all identified species, isotopologues, and isomers. The table also summarises the number of identified lines, both unblended and blended, the range of upper state energies and Einstein A coefficients covered by these lines, as well as the derived excitation temperature and column density for each species. It should be noted that hyperfine transitions with the same catalogued frequency are counted as a single line since these are indistinguishable in the data. After the identification and modelling of individual species the synthetic spectra are summed to obtain a full model for AFGL 4176. Figure 2 shows the full model for the spectral window centred at 239.1 GHz (see Appendix C for a full model of other spectral windows).

The derived excitation temperatures range between 120 and 320 K with an average of 200 K, consistent with the range of temperatures derived for CH3CN by Johnston et al. (2015). The highest column density is 5.5 × 1018 cm−2, derived for CH3OH (based on 13CH3OH and corrected with the 12C/13C ratio), while the lowest value is 1.8 × 1014 cm−2, derived for HCN. While these column densities span more than four orders of magnitude, the majority of species have column densities between 1016 and 1017 cm−2. The species with the highest number of detected lines, 41 in total, is CH3OH. These lines also span the largest range of upper state energies ranging from 20 to 950 K. Similarly, the range of upper state energies of the 13C-isotopologue of methanol span 60–640 K, though only twelve lines associated with this isotopologue are detected. For the remaining species, the number of detected lines ranges from a single line up to 30 lines in the case of aGg’(CH2OH)2, though it should be noted that more than half of these are blended with emission from other species. On average the upper state energies of species span a range of 300 K.

The column density calculation above assumes that the dust is optically thin. We can test if this is indeed the case for AFGL 4176 by converting the continuum intensity into a brightness temperature TB = 5.7 K. If we compare this to the excitation temperature of the gas (~200 K), we find that TB is much less than the physical temperature of the material. Therefore, the optical depth is likely to be low. We therefore conclude that, averaged over the beam, continuum opacity is negligible. Only if the emitting material is distributed over a region with a size six times smaller than the beam would dust opacity become a factor. The effect would be that part of the gas is “hidden” by the dust, and real column densities are higher; however, since all lines would be equally affected, the ratios of species are unaffected.

By far the highest column density ratios with respect to methanol are derived for SO2 and SO with values of 14.7 and 12.7%, respectively.The remaining species have column density ratios with respect to methanol ranging between 0.003 and 3%, with most species showing ratios on theorder of 0.1%. On average, the column density ratios derived for O-bearing species are a factor of three higher than those derived for N-bearing species. This trend will be further discussed in Sect. 4.

Vibrationally excited transitions are detected for two species, CH3CN and HC3N. In both cases, the column density ratio derived from the vibrationally excited transitions are higher than those derived for the ground-state vibrational transitions; 0.8 versus 0.6% for CH3CN/CH3OH, and 0.5 versus 0.1% for HC3N/CH3OH. For the latter species only one and three lines are detected for the ground and vibrationally excited states, respectively. That the column densities derived based on the vibrationally excited states are higher than those derived from the ground-state vibrational transitions is most likely not representative of the actual distribution of molecules but rather due to the fact that the vibrationally excited transitions are excited via shocks or infrared pumping and therefore not in LTE. The column densities derived from these transitions can therefore not be trusted.

3.1 Upper limit on the column density of glycolaldehyde

Glycolaldehyde (CH2(OH)CHO) is of prebiotic interest because of its structural similarities with sugars and the fact that it therefore could be at the basis of the formation of more complex sugar compounds, such as ribose. It was first detected in the interstellar medium towards Sgr B2(N) (Hollis et al. 2000) and subsequently towards various high- and low-mass hot cores (e.g. Beltrán et al. 2009; Jørgensen et al. 2012, 2016; Coutens et al. 2015; Taquet et al. 2015).

A number of transitions of glycolaldehyde are covered by the data although only three of these lines, one of which is blended, are considered detected. These lines have S/Ns of approximately four. The remaining lines identified as likely to be due to glycolaldehyde, five in total, are detected with S/Ns of between two and three and are therefore not included in the line list in Table 2. Due to the generally low signal-to-noise of the glycolaldehyde lines, we report an upper limit column density for this species. The upper limit is derived based on the two unblended lines and assumes an excitation temperature of 200 K. The column density upper limit is ≤1.1 × 1016 cm−2, equivalent to ≤0.2% with respect to methanol.

The formation of glycolaldehyde has been investigated both in the laboratory and with chemical models (Bennett & Kaiser 2007; Woods et al. 2012, 2013). Recently, a laboratory study by Chuang et al. (2017) found that the relative abundance of this and other complex species can be used as a diagnostic tool to derive the processing history of the ice in which the species formed. In particular, the ratio of glycolaldehyde to ethylene glycol provides a useful tool to distinguish ices processed purely by atom-addition (hydrogenation), ices processed purely by UV irradiation, or ice processed by both. In the case of AFGL 4176, the ratio of glycolaldehyde to ethylene glycol is ≤0.4. This ratio is consistent with the relative abundance of the species formed in experiments where ice analogues are exposed to both UV irradiation and hydrogenation.

At the same time, observational studies have shown a trend in the glycolaldehyde to ethylene glycol ratio based on source luminosities. Rivilla et al. (2017) found that this ratio decreases with source luminosity, with ratios ≾0.1 for high-mass sources with a luminosity similar to AFGL 4176. Of course, the ratio found in AFGL 4176 is an upper limit and the actual ratio can thus either follow this trend or deviate from it. Apart from the parameters listed above, density can also affect the glycolaldehyde to ethylene glycol ratio (Coutens et al. 2018).

3.2 Isotopologues with only blended lines

Three isotopologues of HC3N and one of CH3CN are detectedtowards AFGL 4176, although only through blended lines. For completeness, these isotopologues are includedin the full model. Since no column density and excitation temperature can be derived from the blended lines, these are adopted from the isotopologues for which unblended lines are detected. That is, for HC13CCN the column density derived for HCC13CN is adopted, while for vibrationally excited H13CCCN and HC13CCN the column density derived for vibrationally excited HCC13CN is adopted. In the case of CHCN, the column density derived for the main isotopologue has been corrected by the 12C/13C ratio of 60. The isotopologues and the adopted column densities and excitation temperatures are listed in Table 4. It should be noted that no lines of either H13CCCN, 13CH3CN, or CH3C15N were covered by the data. A couple of transitions of vibrationally excited CHCN are within the data range but since these are all weak and highly blended they could not be modelled.

3.3 Isotope ratios

From the three detected isotopologues of HC3N, it is possible to derive 12C/13C and 14N/15N isotope ratios. These are found to be ~16 and ~34, respectively. At the galactocentric distance of AFGL 4176 (dGC = 6.64 kpc), 12C/13C = 60 and 14N/15N = 335–389 according to Milam et al. (2005) and Colzi et al. (2018), respectively. Therefore both isotope ratios found in AFGL 4176 are significantly lower. However, it should be noted that all three HC3N isotopologues are tentative detections and more importantly that the detected line belonging to the main HC3N isotopologue is optically thick; both issues could cause a severe isotope ratio deviation. Furthermore, isotope fractionation could be the result of specific reactions in which HC3N is involved. Clearly, isotope ratios need to be determined from addition molecules (or other lines of HC3N and its isotopologues) in order to verify or disprove the deviation from the galactocentric trends found in this work.

Table 4

Summary of isotopologues with only blended lines.

3.4 Spatial distribution of selected species

Two line maps are produced for each of the species for which five or more unblended lines are detected. The imaged lines are chosen so that both high and low upper state energy transitions are represented, in order to investigate whether these occupy different spatial regions. Also, only lines that are relatively isolated, that is to say whose peak frequency is separated by at least 3 km s−1 from neighbouring peaks, are imaged. This is done in order to minimise line confusion. After suitable lines have been identified for each species, the zero- and first-moment maps, that is the velocity integrated intensity map and intensity-weighted velocity map, respectively,are produced. The spatial extent of each species is determined by fitting a 2D Gaussian to the zero-moment maps (fit parameters are listed in Table D.1). As a representative sample of these maps, the lines of CH3OH, NH2CHO, and CH3C2H are shown in Fig. 3; maps for the remaining species are presented in Appendix D.

There are no large differences between the spatial distribution of O- and N-bearing species. Except for CH3C2H, all species have emission peaks near the position of the continuum peak. The N-bearing species (CH3CN, C2 H3CN, and C2 H5CN) peak very close to the continuum peak, while some O-bearing species (e.g. CH3OH and CH3OCH3) peak up to 0. ′′2 away from the continuum peak. Although this scale is of the same order as the size of the synthesised beam, the signal-to-noise of these maps (30 – 90) is large enough to make these spatial differences significant.

Noticeable differences in spatial distributions exist between transitions of the same species with low and high upper state energies. These differences are illustrated in Fig. 4 where the ratio between the spatial extent (as measured by the fitted FWHM) of the low and high upper state energy transitions are plotted. In this figure, a ratio above 1 indicates that the spatial extent of the low Eup transition is larger than that of the high Eup transition, while a ratio below 1 indicates that the spatial extent of the high Eup transition is larger than that of the low Eup transition. For the majority of the imaged species, the spatial extent of the low upper state energy transition is larger than that of the high upper state transition. This trend is especially clear in the case of the S-bearing species H2 CS and SO2, the O-bearing species CH3OCHO, and the N-bearing species CH3CN. For these species, the spatial extent of the low upper state energy transition is ~30% larger than that of the high Eup transition. The larger spatial extent of the low Eup transitions indicates that these species are present in colder gas. This is consistent with the relatively low excitation temperatures derived for SO2 and H2CS of 150 and 160 K, respectively, but contradicts the high excitation temperatures derived for CH3CN and CH3OCHO of 270 and 310 K, respectively. For C2 H3CN and C2 H5CN the trend is reversed, with the spatial extent of low Eup transitions being smaller than that of high Eup transitions by up to 40% (as measured by the fitted FWHM). The large differences between the spatial extent of low and high Eup transitions seen in the case of H2CS, SO2, CH3OCHO, and CH3CN indicate that these species trace both the warm central region and the cooler outer region of the hot core, while the majority of the remaining complex organic molecules (e.g. CH3OH, CH3OCH3, C2 H5OH, CH3COCH3, and NH2CHO) are likely only excited in the central parts of the core since these species show only limited differences between the low and high Eup transitions. Finally, as noted above, CH3C2H is the only species whose emission is not concentrated at the location of the continuum peak emission. Instead this species shows two peaks of approximately similar intensity, with one roughly coinciding with that of the continuum and a second at the location of the secondary continuum peak, mm2. The spatially more diffuse emission of this species is consistent with trends observed towards other hot cores (see e.g. Fayolle et al. 2015). This indicates that a cold, gas-phase formation mechanism likely dominates the formation of CH3C2H.

For a number of species a velocity gradient is detected across the source. The gradient is most pronounced in the case of NH2CHO but also visible in the high upper state energy lines of CH3CN, CH3OCHO, C2 H3CN, and C2 H5CN. The presence of a velocity gradient across the sources is consistent with the result of Johnston et al. (2015) who model the emission of CH3CN and find this to be consistent with a Keplerian-like disc. A few species, namely CH3OH, C2 H5OH, CH3OCH3, and H2CS, seem to have a velocity gradient that differs from CH3CN.

4 Discussion

Table 5 presents an overview of all detected O-, N-, and S-bearing species (isotopologues not included) towards AFGL 4176. These detected species are common in regions of star formation (see review by Herbst & van Dishoeck 2009, and references therein).

On average, the column density ratios with respect to methanol derived for the O-bearing species are a factor of three higher than the ratios derived for the N-bearing species. Also, the excitation temperatures derived for the O-bearing species are generally low, 120–160 K, while the excitation temperatures derived for the N-bearing species are generally high, 190–240 K. This differentiation of species with excitation temperature is similar to trends observed in the Orion molecular cloud (Blake et al. 1987; Crockett et al. 2015) and in the high-mass, star-forming complex G19.62-0.23 (Qin et al. 2010). Similar trends are also reported by Suzuki et al. (2018) who carry out a survey of N- and O-bearing species towards eight high-mass, star-forming regions including Orion KL and G19.62-0.23, using the 45 m radio telescope at the Nobeyama Radio Observatory. When comparing these observations with chemical models, Suzuki et al. (2018) conclude that the correlations between fractional abundances of different groups of species can be explained by a combination of different temperature structures inside the cores and different evolutionary phases of the studied regions. As examples of a younger, less evolved source and an older, more evolved source, Suzuki et al. (2018) discuss NGC 6334F (also known as NGC 6334I) and G10.47+0.03, respectively. While G10.47+0.03 displays a relatively high fractional abundance of N-bearing species, ~2–10% with respectto methanol, the fractional abundances of the same species detected towards NGC 6334F are only ~0.1%. By assuming a more dominant high-temperature region (~200 K) and laterevolutionary stage for G10.47+0.03 with respect to NGC 6334F, Suzuki et al. (2018) reproduce the observed trends. The trend of younger regions being characterised by lower abundances of N-bearing species with respect to O-bearing species may be a consequence of gas-phase nitrogen chemistry taking longer to initiate compared with the chemistry of O-bearing species (Charnley et al. 1992).

The studies discussed above are primarily based on single-dish observations and therefore, mostly, are spatially unresolved. Whereas single-dish telescopes often cover both the inner hot envelope around the centrally forming star and the emission of its cooler outer envelope, interferometric observations are able to filter out the extended emission and focus solely on the hot core. Also, since single-dish telescopes are generally less sensitive when compared with interferometric observations and especially ALMA, it may not be possible to identify enough optically thin lines from which excitation conditions can be derived. The generally larger beam sizes of single-dish telescopes may also result in underestimated column densities of molecular species if the effects of beam dilution are not accounted for correctly.

In the following, we compare our ALMA results for AFGL 4176 with a selection of ALMA studies, which suffer less from sensitivity and resolution limitations but do cover sources located at a range of different distances, therefore sampling different spatial scales ranging from ~70 to ~13300 au. These studies focus on Sgr B2(N), Orion KL, and the low-mass protostellar binary IRAS 16293. We conclude the section with a comparison of the ratios of the detected species to the ratios predicted by chemical models. Table 6 and Fig. 5 summarise the comparisons.

thumbnail Fig. 3

First-moment (intensity-weighted velocity) map of CH3OH (top panels) lines at 254.0153 GHz (left) and 241.2679 GHz (right). Pixels with S/Ns of less than three are masked out. The zero-moment (integrated intensity) map for each line is overlaid in grey contours. Contours start at 9σ and are in steps of 12σ, with σ = 1.34 × 10−2 and 7.16 × 10−3 Jy beam−1 km s−1, for the left and right panel, respectively. The black and green crosses mark the locations of the peak continuum emission and peak integrated line intensity, respectively. Middle panel: same as top panels but for NH2 CHO lines at 239.952 GHz (left) and 254.727 GHz (right). Contours start at 9σ and are in steps of 12σ, with σ = 6.17 × 10−3 and 6.25 × 10−3 Jy beam−1 km s−1, for the left and right panel, respectively. Bottom panels: same as top panels but for CH3 C2H lines at 239.2523 GHz (left) and 239.2112 GHz (right). Contours start at 6σ and are in steps of 3σ, with σ = 7.29 × 10−3 and 6.35 × 10−3 Jy beam−1 km s−1, for the left and right panel, respectively.

thumbnail Fig. 4

Ratio between the fitted FWHM of the low and high upper state energy transitions listed in Table D.1. A ratio lager than 1 indicates that the spatial extent of the low upper state energy transition is larger than that of the high upper state energy transition.

Table 5

Overview of oxygen-, nitrogen-, and sulphur-bearing species detected towards AFGL 4176.

4.1 Comparison with the high-mass, star-forming regions in Sgr B2(N) and Orion KL

Sgr B2(N)

Locatedin the Galactic central region, the Sgr B2 molecular cloud hosts some of the most active sites of high-mass star formation in the galaxy. One of these sites, Sgr B2 (N), is the subject of the ALMA line survey EMoCA (Exploring Molecular Complexity with ALMA, Belloche et al. 2016) aimed at characterising the molecular content of the region. Due to the high spatial resolution of the observations, ~1.6′′, probing scales down to 0.06 pc (~13300 au assuminga distance of 8.34 kpc), Bonfand et al. (2017) were able to identify three new hot cores towards Sgr B2, labelled N3, N4, and N5,in addition to the previously identified cores N1 and N2. Bonfand et al. (2017) find that the chemical compositions of these new cores are very similar to each other and very different from that of the N2 core. Derived C2 H3CN/C2H5CN and CH3CN/C2H5CN ratios suggest that the N2 core is chemically less evolved than the three new cores.

For the hot cores in Sgr B2, the column density ratios with respect to methanol of N-bearing species (CH3CN, NH2CHO, C2 H3CN, and C2 H5CN) are higher than those derived towards AFGL 4176 by up to two orders of magnitude. For the O-bearing species (C2 H5OH and CH3OCHO), the variations are smaller, though still up to an order of magnitude higher in Sgr B2 compared with AFGL 4176.

In addition to the main isotopologues, a number of 13C- and 15N-isotopologues, as well as deuterated and vibrationally excited species, have been detected towards Sgr B2(N2) (Belloche et al. 2016). No deuterated species are detected towards AFGL 4176, despite a number of strong transitions belonging to deuterated molecules, primarily DC3N and deuterated NH2CHO, being covered by the spectra. A number of lines of 13C-cyanoacetylene are detected in addition to 13CH3OH and some blended lines of CHCN. Doubly substituted 13C-cyanoacetylene isotopologues are also detected towards Sgr B2(N2). Towards AFGL 4176, two transitions of 13C doubly substituted cyanoacetylene are covered but both are weak and highly blended. The tentative detection of HCN towards Sgr B2(N2) results in a ratio with respect to methanol identical to the value derived for AFGL 4176. It should be noted, however, that only a very limited number of lines of these isotopologues are detected and therefore their detection is considered tentative and their ratios should be seen as indicative of trends rather than definite values.

Table 6

Summary of column density ratios with respect to methanol predicted by models and derived towards AFGL 4176, Sgr B2(N), Orion KL, and IRAS 16293B.

Orion KL

Due to its proximity, ~414 pc from the Sun (Menten et al. 2007), the high-mass, star-forming regions associated with the Orion molecular clouds are some of the most studied. Using ALMA observations, the morphology and molecular composition of the region was recently studied by Pagani et al. (2017). Thanks to the high angular resolution of their data of 1.7′′ that probed scales of ~700 au, they were able to separate the region into a number of components including the hot core, plateau, and extended ridge, but also into a variety of molecular clumps. This enabled them to report a complex velocity structure. However, due to the lack of zero-spacing data to recover the extended emission of many species, Pagani et al. (2017) do not derive column densities or excitation temperatures for the detected species and they limit their analysis to line identification and determination of line velocity and line widths. Orion KL may therefore be qualitatively compared with AFGL 4176 though no quantitative comparison is possible.

With the exception of CH3C2H and NS, all species detected towards AFGL 4176 are also detected towards Orion KL by Pagani et al. (2017), including vibrationally excited HC3N (also investigated by Peng et al. 2017) and its 13C singly substituted isotopologues. The highest energy vibrationally excited state is the v6 = v7 = 1 state, detected towards the Orion KL hot core region. In contrast, only the first two vibration states (HC3N, v7 = 2 and H13 CCCN, v7 = 1, HC13CCN, v7 = 1 and HCC13CN, v7 = 1) were tentatively detected towards AFGL 4176.

Tercero et al. (2018) also investigate the inventory of complex molecules towards Orion KL, this timed focussing on O-bearing species. With a resolution of ~1.5′′, they probe spatial scales of ~620 au. Tercero et al. (2018) derive molecular abundances at three locations. These are selected based on where each of the species methylformate, ethylene glycol, and ethanol peak. When comparing the relative abundances with respect to CH3OH for C2 H5OH, CH3OCH3, CH3COCH3, CH3OCHO, and (CH2OH)2, we find that the relative abundances of CH3OCH3 and CH3OCHO are consistently lower in AFGL 4176 (1. –3.9 and 1.5–7.2 times lower, respectively) than in Orion KL. However, the relative abundances of CH3COCH3 and (CH2OH)2 are consistently higher in AFGL 4176 compared with Orion KL (6.7–20 and 1.2–6 times higher, respectively), while C2 H5OH has a roughly equal relative abundance in the two sources. What causes the enhancement of certain species remains unclear.

As for AFGL 4176, both conformers of ethylene glycol are detected towards Orion KL (Favre et al. 2017). This is interesting since previously only the more stable of the two, aGg’(CH2OH)2, was detected towards high-mass, star-forming regions (see e.g. Lykke et al. 2015; Brouillet et al. 2015; Rivilla et al. 2017, and references therein), while the gGg’(CH2OH)2 conformer was only detected towards the low-mass system IRAS 16293 (Jørgensen et al. 2016). The ratio between the aGg’ and gGg’ ethylene glycol conformers is 1.2 in AFGL 4176, within the errors of the value of 1.1 derived for IRAS 16293, and half the values of 2.3 and 2.5 derived for the 5 and 8 km s−1 components of Orion KL, respectively (Favre et al. 2017).

4.2 Comparison with the low-mass protobinary IRAS 16293–2422

The low-mass protobinary system IRAS 16293, located at a distance of 141 pc (Dzib et al. 2018), was observed in the ALMA Protostellar Interferometric Line Survey (PILS, see Jørgensen et al. 2016, for overview and first results). The survey covers a total of 33.7 GHz between 329 and 363 GHz, with spectral and angular resolutions of 0.2 km s−1 and 0′′.5 (~70 au), respectively. The IRAS 16293 system is composed of two main components, IRAS 16293A and IRAS 16293B, with the narrow lines associated with the B source, ~1 km s−1, making it ideal for line identification.

Of the species for which five or more lines are detected towards AFGL 4176, all have also been identified towards IRAS 16293, although CH3C2H and NS are not reported in the PILS survey (van Dishoeck et al. 1995; Caux et al. 2011; Coutens et al. 2016; Jørgensen et al. 2016, 2018; Lykke et al. 2017; Calcutt et al. 2018; Drozdovskaya et al. 2018). In addition, eight species with fewer than five transitions detected towards AFGL 4176 (CS and OCS only via their isotopologues) are also common between the sources. Figure 6 presents an overview of the relative abundances of all species detected towards AFGL 4176 and IRAS 16293B.

Overall,the composition of AFGL 4176 is more similar to that of IRAS 16293B than to the high-mass, star-forming regions in the Galactic centre. Specifically, the relative column densities derived for the O-bearing species C2 H5OH, CH3OCH3, CH3OCHO, and (CH2OH)2 towards IRAS 16293B are within a factor of two of the values derived for AFGL 4176. The remaining species show slightly larger variations with the ratio of CH3CHO to CH3OH being a factor of four higher in IRAS 16293B compared with AFGL 4176, and the ratio of CH3COCH3 to CH3OH a factor of six lower. For N-bearing species, similar abundances are derived for CH3CN and NH2CHO, with variations within a factor of two between the sources. In contrast, lower ratios of both C2 H3CN and C2 H5CN are reported towards IRAS 16293B compared to AFGL 4176, by factors of 16 and three, respectively. By far the largest variations between the sources are seen in the ratios of the S-bearing species, with SO2 being close to three orders of magnitude higher in AFGL 4176 compared to IRAS 16293B, and H2 CS higher by afactor of 39. However, Drozdovskaya et al. (2018) note that the SO2 emission detected towards IRAS 16293 is likely not homogeneously distributed within the 0′′.5 PILS beam, which also misses a large extended component, and therefore the large difference between the sources could in part be explained by local variations in the distribution of the species towards IRAS 16293.

thumbnail Fig. 5

Relative abundances of N-bearing species (top panel) and O-bearing species (bottom panel) predicted by models and detected towards AFGL 4176, Sgr B2(N), and IRAS 16293B. For AFGL 4176, Sgr B2(N), and IRAS 16293B the colours of bars indicate the excitation temperature derived for each species.

4.3 Comparison with chemical models

The chemistry of hot cores is commonly divided into three main phases: (1) a cold collapse phase dominated by reactions on grain surfaces involving the diffusion of light species (i.e. H); (2) a warm-up phase where relatively complex species can be formed in the ice and subsequently released into the gas phase; (3) a hot core phase dominated by gas-phase reactions triggered by the evaporation of the icy content. The models presented by Garrod (2013) couple all of these phases to trace the gas phase, grain surface, and bulk ice chemistry throughout the evolution of the core. The physical model adopted by Garrod (2013) consists of a collapse phase followed by a gradual warm up of the gas and dust. Three warm-up timescales are adopted: a “fast” scale with warm up to 200 K in 5 × 104 yr, a “medium” scale reaching 200 K in 2 × 105 yr, and a “slow” scale taking 1 × 106 yr to reach 200 K. Listed in Table 6 are the predicted peak gas-phase abundance ratios for each of these models.

In the models presented by Garrod (2013), the formation of the complex O-bearing species detected in this study (i.e. CH3CHO, C2 H5OH, CH3OCH3, CH3COCH3, CH3OCHO, and (CH2OH)2) is closely related to CH3OH. Within the framework of the models, O-bearing species are formed primarily in warm (i.e. 30 K < T < 80 K) ices from the recombination of radicals formed through the UV-photodissociation of CH3OH along with other simple solid species such as H2CO and H2O (see also Garrod & Herbst 2006; Garrod et al. 2008). Here, the UV field is internally generated by cosmic ray collisions with H2. While the models match the abundances of CH3CHO, C2 H5OH, and CH3OCH3 in AFGL 4176 within a factor of three, the abundances of CH3COCH3 and (CH2OH)2 are underpredicted by at least an order of magnitude. For CH3OCHO, the most abundant O-bearing species after CH3OH, with abundances of ~2–3% in both AFGL 4176 and IRAS 16293B, the models predict values that are lower than the observed ones by a factor of four in the case of the fast model and a factor of 39 in the case of the slow model. A possible explanation for the mismatch between models and derived abundances of this species is poorly understood ice chemistry, as well as missing gas-phase routes or included gas-phase routes that are more efficient than presumed. Indeed, a number of studies suggest that gas-phase chemistry could provide an important contribution to the formation of the molecule, either through ion-neutral or neutral-neutral reactions, following the sublimation of precursors from the ice (see e.g. Neill et al. 2011; Cole et al. 2012; Vasyunin & Herbst 2013; Balucani et al. 2015; Taquet et al. 2016). A significant contribution from gas-phase reactions to the formation of CH3COCH3, as suggestedby Charnley (2001), is also possible.

The abundances of complex N-bearing species detected in this study (i.e. CH3CN, NH2CHO, C2 H3CN, and C2 H5CN) are likely the result of a combination of grain-surface and gas-phase reactions. For instance, CH3CN can be formed on ices from the radical recombination of CH3 and CN but also in the gas-phase via ion reactions between HCN and CH, followed by recombination with an electron or a proton transfer reaction. In the models by Garrod (2013), CH3CN is formed primarily in ices via CH3 + CN → CH3CN, though a formation channel via hydrogenation of C2 N on grains at early times is also included. C2 H3CN and C2 H5CN are formed mainly during the warm-up stage as a result of a series of both gas-phase and grain-surface reactions including the formation, recombination, and hydrogenation of the CN radical as well as C2 H2, C2 H4, and HC3N (Garrod et al. 2017). The formation of NH2CHO is likely ice-dominated and proceeds via radical recombination reactions between NH2 and HCO (e.g. Jones et al. 2011; Fedoseev et al. 2016). Alternatively, hydrogenation of HNCO may also lead to NH2CHO. This idea is based on the strong correlation between the abundances of HNCO and NH2CHO reported by Bisschop et al. (2007) and López-Sepulcre et al. (2015) for samples of pre-stellar and protostellar objects, indicating a connection between these species. However, such a connection contradicts the results of experimental works, which show that solid-state HNCO hydrogenation does not produce NH2CHO (Noble et al. 2015). A similar conclusion is reached by Ligterink et al. (2018) who find that HNCO is likely not at the basis of the formation of NH2CHO but rather formed simultaneously. Finally, a gas-phase formation route for NH2CHO via reactions between NH2 and H2CO has also been proposed (Barone et al. 2015; Codella et al. 2017). Chemical models by Quénard et al. (2018) show that both solid-state and gas-phase reactions are important for the formation of NH2CHO and they claim its correlation with HNCO is the result of chemical reactions leading to both species responding in very similar ways to physical parameters, such as temperature.

For thedetected N-bearing species, only the abundance ratio of C2 H3CN is well matched by the models. In contrast, the abundance ratios predicted by the models for CH3CN, NH2CHO, and C2 H5CN are all about an order of magnitude off with respect to the values derived for AFGL 4176. The abundances in AFGL 4176 do not favour either of the fast, medium, or slow models over the other.

In summary, the presence of most of the detected complex molecules could be explained by warm ice chemistry triggered by cosmic-ray UV photons. However, for some species, O- as well as N-bearing, formation routes via gas-phase reactions cannot be neglected. The chemical similarity between IRAS 16293B and AFGL 4176 hints that the chemical composition of complex species may alreadybe set in the cold cloud stage. In this context, the lack of chemical differences between the two sources, despite the large difference in luminosity, implies that AFGL 4176 is a very young source where little processing of the chemical inventory by the protostar has occurred.

thumbnail Fig. 6

Relative abundances of species detected towards AFGL 4176 and IRAS 16293B. Labels in bold refer to species with five or more detected transitions towards AFGL 4176. Oxygen-bearing species are labelled by numbers 1–9 in red, N-bearing species are labelled by numbers 20–25 in blue, and S-bearing species are labelled by numbers 30–34 in yellow. Black arrows indicate upper limits on the relative abundance of CH2 (OH)CHO in AFGL 4176 and of SO in IRAS 16293B. The dashed grey line indicates the 1:1 ratio of relative abundances in AFGL 4176and IRAS 16293B.

5 Summary

This paper presents a comprehensive study of the chemical inventory of the high-mass protostar AFGL 4176. Due to the exceptional resolving power offered by ALMA, the source is analysed on disc scales, making it possible to probe only the emission coming from the inner hot core region around the forming star while avoiding that of its large-scale cooler envelope. The source displays a rich chemistry consisting of 23 different molecular species, of which more than half, fourteen in total, are defined as complex, that is, consisting of six or more atoms. Of the detected species, the majority are oxygen-bearing while fewer contain nitrogen, sulphur, or a combination thereof.

Assuming LTE, the column density is derived for all species detected towards AFGL 4176. With respect to methanol, the O-bearing species are approximately a factor of three more abundant than N-bearing species. This may indicate that AFGL 4176 is a relatively young source since nitrogen chemistry generally takes longer to evolve in the gas phase compared with the chemistry of O-bearing species. Alternatively, if the composition of complex species is already set in the cold cloud stage, this gives an indication that the formation of O-bearing species is favoured over that of N-bearing molecules. This may be due to inefficient reactions pathways (i.e. high diffusion energies and reactions barriers) for the formation of N-bearing species, or due to a dominant nitrogen loss channel, for example into N2.

With the exception of CH3C2H, which shows two emission peaks, the spatial distribution of N- and O-bearing species is roughly similar. Generally, all species show emission peaks near the position of the continuum peak though some O-bearing species peak up to 0′′.2 away. Differences in the spatial extent of transitions of the same species with low and high Eup are seen for the majority of species. For a number of species a velocity gradient is detected across the source consistent with the Keplerian-like disc reported by Johnston et al. (2015).

Overall, the chemical composition of AFGL 4176 is more similar to that of the low-mass protostar IRAS 16293B than to that of the high-mass, star-forming region Sgr B2(N). Taking methanol as a reference, the abundances of C2 H5OH, CH3OCH3, CH3OCHO, and (CH2OH)2 in IRAS 16293B are within a factor of two of the abundances in AFGL 4176. In contrast, the C2 H5OH/CH3OH and CH3OCHO/CH3OH values derived for Sgr B2(N) are higher than those in AFGL 4176 by up to an order of magnitude. For the N-bearing species, the abundances of CH3CN, C2 H5CN and NH2CHO are similar between AFGL4176 and IRAS 16293B, while C2 H3CN/CH3OH is lower by a factor of 16 in IRAS 16293B. For Sgr B2(N) the abundances of all N-bearing species are significantly higher than in AFGL 4176. The similarity between abundances in AFGL 4176 and IRAS 16293B indicates that the production of complex species does not depend strongly on the luminosity of sources, but may be universal despite differences in physical conditions; or it may indicate that the composition of species is already set in the ice during the cold cloud stage.

Acknowledgements

We thank the anonymous referee for the thorough review and for contributing many clarifications that helped us improve our manuscript. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2012.1.00469.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada) and 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.

Appendix A Model grids

Table A.1

Overview of model grids.

Appendix B Unidentified lines

Table B.1

Unidentified lines.

Appendix C Full model

thumbnail Fig. C.1

Same as Fig. 2 for species detected towards AFGL 4176 in the spectral window centred at 254.0 GHz.

thumbnail Fig. C.1

continued.

thumbnail Fig. C.2

Same as Fig. 2 for species detected towards AFGL 4176 in the spectral window centred at 240.5 GHz.

thumbnail Fig. C.2

continued.

thumbnail Fig. C.3

Same as Fig. 2 for species detected towards AFGL 4176 in the spectral window centred at 239.0 GHz.

Appendix D Line maps

thumbnail Fig. D.1

Same as Fig. 3 for 13CH3OH lines at 256.1716 GHz (left) and 253.6895 GHz (right). Contours start at 9σ and are in steps of 6σ and 12σ, with σ = 6.27 × 10−3 and 6.87 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

thumbnail Fig. D.2

Same as Fig. 3 for CH3OCH3 lines at 240.9851 GHz (left) and 253.9075 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 4.95 × 10−3 and 6.08 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

thumbnail Fig. D.3

Same as Fig. 3 for C2H5OH lines at 254.3841 GHz (left) and 253.3274 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 5.58 × 10−3 and 5.39 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

thumbnail Fig. D.4

Same as Fig. 3 for CH3COCH3 lines at 240.9988 GHz (left) and 254.0822 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 4.96 × 10−3 and 5.32 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

thumbnail Fig. D.5

Same as Fig. 3 for CH3OCHO lines at 240.0211 GHz (left) and 256.4478 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 5.36 × 10−3 and 5.96 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

thumbnail Fig. D.6

Same as Fig. 3 for CH3CN lines at 239.0965 GHz (left) and 238.8439 GHz (right). Contours start at 9σ and are in steps of 12σ, with σ = 1.26 × 10−2 and 5.78 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

thumbnail Fig. D.7

Same as Fig. 3 for vibrationally excited CH3CN lines at 240.0895 GHz (left) and 239.7917 GHz (right). Contours start at 9σ and are in steps of 12σ and 6σ, with σ = 5.41 × 10−3, for the left and right panels, respectively.

thumbnail Fig. D.8

Same as Fig. 3 for C2H3CN lines at 254.1375 GHz (left) and 256.4480 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 5.29 × 10−3 and 6.0 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

thumbnail Fig. D.9

Same as Fig. 3 for C2H5CN lines at 254.6336 GHz (left) and 256.3959 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 5.18 × 10−3 and 5.7 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

thumbnail Fig. D.10

Same as Fig. 3 for H2CS lines at 240.5491 GHz (left) and 240.3322 GHz (right). Contours start at 9σ and are in steps of 12σ, with σ = 6.81 × 10−3 and 5.27 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

thumbnail Fig. D.11

Same as Fig. 3 for SO2 lines at 256.2469 GHz (left) and 238.9925 GHz (right). Contours start at 9σ and are in steps of 12σ, with σ = 1.96 × 10−2 and 8.63 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

Table D.1

Spatial extent of molecules mapped towards AFGL 4176.

References

  1. Bailer-Jones, C. A. L., Rybizki, J., Fouesneau, M., Mantelet, G., & Andrae, R. 2018, AJ, 156, 58 [NASA ADS] [CrossRef] [Google Scholar]
  2. Balucani, N., Ceccarelli, C., & Taquet, V. 2015, MNRAS, 449, L16 [NASA ADS] [CrossRef] [Google Scholar]
  3. Barone, V., Latouche, C., Skouteris, D., et al. 2015, MNRAS, 453, L31 [NASA ADS] [CrossRef] [Google Scholar]
  4. Belloche, A., Müller, H. S. P., Garrod, R. T., & Menten, K. M. 2016, A&A, 587, A91 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Beltrán, M. T., Brand, J., Cesaroni, R., et al. 2006, A&A, 447, 221 [NASA ADS] [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  6. Beltrán, M. T., Codella, C., Viti, S., Neri, R., & Cesaroni, R. 2009, ApJ, 690, L93 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bennett, C. J., & Kaiser, R. I. 2007, ApJ, 661, 899 [NASA ADS] [CrossRef] [Google Scholar]
  8. Bisschop, S. E., Jørgensen, J. K., van Dishoeck, E. F., & de Wachter, E. B. M. 2007, A&A, 465, 913 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Blake, G. A., Sutton, E. C., Masson, C. R., & Phillips, T. G. 1987, ApJ, 315, 621 [NASA ADS] [CrossRef] [Google Scholar]
  10. Boley, P. A., Linz, H., van Boekel, R., et al. 2012, A&A, 547, A88 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Bonfand, M., Belloche, A., Menten, K. M., Garrod, R. T., & Müller, H. S. P. 2017, A&A, 604, A60 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  12. Boonman, A. M. S., van Dishoeck, E. F., Lahuis, F., & Doty, S. D. 2003, A&A, 399, 1063 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. Bottinelli, S., Ceccarelli, C., Lefloch, B., et al. 2004, ApJ, 615, 354 [NASA ADS] [CrossRef] [Google Scholar]
  14. Brouillet, N., Despois, D., Baudry, A., et al. 2013, A&A, 550, A46 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Brouillet, N., Despois, D., Lu, X.-H., et al. 2015, A&A, 576, A129 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Calcutt, H., Jørgensen, J. K., Müller, H. S. P., et al. 2018, A&A, 616, A90 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Caselli, P., & Ceccarelli, C. 2012, A&ARv, 20, 56 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  18. Caux, E., Kahane, C., Castets, A., et al. 2011, A&A, 532, A23 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. Cazaux, S., Tielens, A. G. G. M., Ceccarelli, C., et al. 2003, ApJ, 593, L51 [NASA ADS] [CrossRef] [Google Scholar]
  20. Charnley, S. 2001, in The Bridge Between the Big Bang and Biology: Stars, Planetary Systems, Atmospheres, ed. F. Giovannelli (Volcanoes: Their Link to Life), 139 [Google Scholar]
  21. Charnley, S. B., Tielens, A. G. G. M., & Millar, T. J. 1992, ApJ, 399, L71 [NASA ADS] [CrossRef] [Google Scholar]
  22. Chuang, K.-J., Fedoseev, G., Qasim, D., et al. 2017, MNRAS, 467, 2552 [NASA ADS] [Google Scholar]
  23. Codella, C., Ceccarelli, C., Caselli, P., et al. 2017, A&A, 605, L3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Cole, C. A., Wehres, N., Yang, Z., et al. 2012, ApJ, 754, L5 [NASA ADS] [CrossRef] [Google Scholar]
  25. Colzi, L., Fontani, F., Rivilla, V. M., et al. 2018, MNRAS, 478, 3693 [NASA ADS] [CrossRef] [Google Scholar]
  26. Coutens, A., Jørgensen, J. K., van der Wiel, M. H. D., et al. 2016, A&A, 590, L6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Coutens, A., Persson, M. V., Jørgensen, J. K., Wampfler, S. F., & Lykke, J. M. 2015, A&A, 576, A5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. Coutens, A., Viti, S., Rawlings, J. M. C., et al. 2018, MNRAS, 475, 2016 [NASA ADS] [CrossRef] [Google Scholar]
  29. Crockett, N. R., Bergin, E. A., Neill, J. L., et al. 2015, ApJ, 806, 239 [NASA ADS] [CrossRef] [Google Scholar]
  30. Crockett, N. R., Bergin, E. A., Neill, J. L., et al. 2014, ApJ, 787, 112 [NASA ADS] [CrossRef] [Google Scholar]
  31. De Buizer, J. M. 2003, MNRAS, 341, 277 [NASA ADS] [CrossRef] [Google Scholar]
  32. Drozdovskaya, M. N., van Dishoeck, E. F., Jørgensen, J. K., et al. 2018, MNRAS, 476, 4949 [NASA ADS] [CrossRef] [Google Scholar]
  33. Dzib, S. A., Loinard, L., Ortiz-León, G. N., Rodríguez, L. F., & Galli, P. A. B. 2018, ApJ, 867, 151 [NASA ADS] [CrossRef] [Google Scholar]
  34. Endres, C. P., Schlemmer, S., Schilke, P., Stutzki, J., & Müller, H. S. P. 2016, J. Mol. Spectr., 327, 95 [NASA ADS] [CrossRef] [Google Scholar]
  35. Favre, C., Pagani, L., Goldsmith, P. F., et al. 2017, A&A, 604, L2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  36. Fayolle, E. C., Öberg, K. I., Garrod, R. T., van Dishoeck, E. F., & Bisschop, S. E. 2015, A&A, 576, A45 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  37. Fedoseev, G., Chuang, K.-J., van Dishoeck, E. F., Ioppolo, S., & Linnartz, H. 2016, MNRAS, 460, 4297 [NASA ADS] [CrossRef] [Google Scholar]
  38. Garrod, R. T. 2013, ApJ, 765, 60 [NASA ADS] [CrossRef] [Google Scholar]
  39. Garrod, R. T., Belloche, A., Müller, H. S. P., & Menten, K. M. 2017, A&A, 601, A48 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. Garrod, R. T., & Herbst, E. 2006, A&A, 457, 927 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  41. Garrod, R. T., Widicus Weaver, S. L., & Herbst, E. 2008, ApJ, 682, 283 [NASA ADS] [CrossRef] [Google Scholar]
  42. Green, J. A., & McClure-Griffiths, N. M. 2011, MNRAS, 417, 2500 [NASA ADS] [CrossRef] [Google Scholar]
  43. Hatchell, J., Thompson, M. A., Millar, T. J., & MacDonald, G. H. 1998, A&AS, 133, 29 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Henning, T., Friedemann, C., Guertler, J., & Dorschner, J. 1984, Astronomische Nachrichten, 305, 67 [NASA ADS] [CrossRef] [Google Scholar]
  45. Herbst, E., & van Dishoeck E. F. 2009, ARA&A, 47, 427 [NASA ADS] [CrossRef] [Google Scholar]
  46. Hollis, J. M., Lovas, F. J., & Jewell, P. R. 2000, ApJ, 540, L107 [NASA ADS] [CrossRef] [Google Scholar]
  47. Ikeda, M., Ohishi, M., Nummelin, A., et al. 2001, ApJ, 560, 792 [NASA ADS] [CrossRef] [Google Scholar]
  48. Isokoski, K., Bottinelli, S., & van Dishoeck, E. F. 2013, A&A, 554, A100 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. Johnston, K. G., Beuther, H., Linz, H., et al. 2014, in The Labyrinth of Star Formation, Astrophysics and Space Science Proceedings eds. D. Stamatellos, S. Goodwin, & D. Ward-Thompson (Switzerland: Springer International Publishing), 36, 413 [NASA ADS] [CrossRef] [Google Scholar]
  50. Johnston, K. G., Robitaille, T. P., Beuther, H., et al. 2015, ApJ, 813, L19 [NASA ADS] [CrossRef] [Google Scholar]
  51. Jones, B. M., Bennett, C. J., & Kaiser, R. I. 2011, ApJ, 734, 78 [NASA ADS] [CrossRef] [Google Scholar]
  52. Jørgensen, J. K., Favre, C., Bisschop, S. E., et al. 2012, ApJ, 757, L4 [NASA ADS] [CrossRef] [Google Scholar]
  53. Jørgensen, J. K., Müller, H. S. P., Calcutt, H., et al. 2018, A&A, 620, A170 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Jørgensen, J. K., van der Wiel, M. H. D., Coutens, A., et al. 2016, A&A, 595, A117 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Kalenskii, S. V., & Johansson, L. E. B. 2010, Astron. Rep., 54, 1084 [NASA ADS] [CrossRef] [Google Scholar]
  56. Ligterink, N. F. W., Terwisscha van Scheltinga, J., Taquet, V., et al. 2018, MNRAS, 480, 3628 [NASA ADS] [CrossRef] [Google Scholar]
  57. López-Sepulcre, A., Jaber, A. A., Mendoza, E., et al. 2015, MNRAS, 449, 2438 [NASA ADS] [CrossRef] [Google Scholar]
  58. Lykke, J. M., Coutens, A., Jørgensen, J. K., et al. 2017, A&A, 597, A53 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Lykke, J. M., Favre, C., Bergin, E. A., & Jørgensen, J. K. 2015, A&A, 582, A64 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. McGuire, B. A., Brogan, C. L., Hunter, T. R., et al. 2018, ApJ, 863, L35 [NASA ADS] [CrossRef] [Google Scholar]
  61. McGuire, B. A., Shingledecker, C. N., Willis, E. R., et al. 2017, ApJ, 851, L46 [NASA ADS] [CrossRef] [Google Scholar]
  62. Menten, K. M., Reid, M. J., Forbrich, J., & Brunthaler, A. 2007, A&A, 474, 515 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  63. Milam, S. N., Savage, C., Brewster, M. A., Ziurys, L. M., & Wyckoff, S. 2005, ApJ, 634, 1126 [NASA ADS] [CrossRef] [Google Scholar]
  64. Müller, H. S. P., Schlöder, F., Stutzki, J., & Winnewisser, G. 2005, J. Mol. Struct., 742, 215 [NASA ADS] [CrossRef] [Google Scholar]
  65. Müller, H. S. P., Thorwirth, S., Roth, D. A., & Winnewisser, G. 2001, A&A, 370, L49 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  66. Neill, J. L., Bergin, E. A., Lis, D. C., et al. 2014, ApJ, 789, 8 [NASA ADS] [CrossRef] [Google Scholar]
  67. Neill, J. L., Steber, A. L., Muckle, M. T., et al. 2011, J. Phys. Chem. A, 115, 6472 [CrossRef] [Google Scholar]
  68. Noble, J. A., Theule, P., Congiu, E., et al. 2015, A&A, 576, A91 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  69. Ossenkopf, V., & Henning, T. 1994, A&A, 291, 943 [NASA ADS] [Google Scholar]
  70. Pagani, L., Favre, C., Goldsmith, P. F., et al. 2017, A&A, 604, A32 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  71. Peng, Y., Qin, S.-L., Schilke, P., et al. 2017, ApJ, 837, 49 [NASA ADS] [CrossRef] [Google Scholar]
  72. Peng, Y., Rivilla, V. M., Zhang, L., Ge, J. X., & Zhou, B. 2019, ApJ, 871, 251 [NASA ADS] [CrossRef] [Google Scholar]
  73. Phillips, C. J., Norris, R. P., Ellingsen, S. P., & McCulloch, P. M. 1998, MNRAS, 300, 1131 [NASA ADS] [CrossRef] [Google Scholar]
  74. Pickett, H. M., Poynter, R. L., Cohen, E. A., et al. 1998, J. Quant. Spectr. Rad. Transf., 60, 883 [NASA ADS] [CrossRef] [Google Scholar]
  75. Qin, S.-L., Wu, Y., Huang, M., et al. 2010, ApJ, 711, 399 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  76. Quénard, D., Jiménez-Serra, I., Viti, S., Holdship, J., & Coutens, A. 2018, MNRAS, 474, 2796 [NASA ADS] [CrossRef] [Google Scholar]
  77. Rivilla, V. M., Beltrán, M. T., Cesaroni, R., et al. 2017, A&A, 598, A59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  78. Sakai, N., & Yamamoto, S. 2013, Chem. Rev., 113, 8981 [CrossRef] [Google Scholar]
  79. Schöier, F. L., Jørgensen, J. K., van Dishoeck, E. F., & Blake, G. A. 2002, A&A, 390, 1001 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  80. Suzuki, T., Ohishi, M., Saito, M., et al. 2018, ApJS, 237, 3 [NASA ADS] [CrossRef] [Google Scholar]
  81. Taquet, V., López-Sepulcre, A., Ceccarelli, C., et al. 2015, ApJ, 804, 81 [NASA ADS] [CrossRef] [Google Scholar]
  82. Taquet, V., Wirström, E. S., & Charnley, S. B. 2016, ApJ, 821, 46 [NASA ADS] [CrossRef] [Google Scholar]
  83. Tercero, B., Cuadrado, S., López, A., et al. 2018, A&A, 620, L6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  84. Tielens, A. G. G. M. 2013, Rev. Mod. Phys., 85, 1021 [NASA ADS] [CrossRef] [Google Scholar]
  85. van der Tak, F. F. S., van Dishoeck, E. F., & Caselli, P. 2000, A&A, 361, 327 [NASA ADS] [Google Scholar]
  86. van Dishoeck, E. F., Blake, G. A., Jansen, D. J., & Groesbeck, T. D. 1995, ApJ, 447, 760 [NASA ADS] [CrossRef] [Google Scholar]
  87. van Dishoeck, E. F., & Helmich, F. P. 1996, A&A, 315, L177 [NASA ADS] [Google Scholar]
  88. van Dishoeck, E. F., Helmich, F. P., de Graauw, T., et al. 1996, A&A, 315, L349 [NASA ADS] [Google Scholar]
  89. Vasyunin, A. I., & Herbst, E. 2013, ApJ, 769, 34 [NASA ADS] [CrossRef] [Google Scholar]
  90. Woods, P. M., Kelly, G., Viti, S., et al. 2012, ApJ, 750, 19 [NASA ADS] [CrossRef] [Google Scholar]
  91. Woods, P. M., Slater, B., Raza, Z., et al. 2013, ApJ, 777, 90 [NASA ADS] [CrossRef] [Google Scholar]

3

Centre d’Analyse Scientifique de Spectres Instrumentaux et Synthétiques: http://cassis.irap.omp.eu

All Tables

Table 1

Overview of spectral cubes.

Table 2

Summary of detected lines.

Table 3

Summary of derived values of Ns and Tex.

Table 4

Summary of isotopologues with only blended lines.

Table 5

Overview of oxygen-, nitrogen-, and sulphur-bearing species detected towards AFGL 4176.

Table 6

Summary of column density ratios with respect to methanol predicted by models and derived towards AFGL 4176, Sgr B2(N), Orion KL, and IRAS 16293B.

Table A.1

Overview of model grids.

Table B.1

Unidentified lines.

Table D.1

Spatial extent of molecules mapped towards AFGL 4176.

All Figures

thumbnail Fig. 1

Continuum image of AFGL 4176 at 1.2 mm. Contours are [5, 10, 15, 25, 35, 45, 55]σ, with σ = 0.5 mJy beam−1. The peak continuum location at which the spectra have been extracted is marked by the black cross. The synthesised beam (0′′.33 × 0′′.31 ~1210 × 1140 au) is shown in the bottom left corner.

In the text
thumbnail Fig. 2

Full model (red), i.e., the sum of synthetic spectra, for all species detected towards AFGL 4176 in the spectral window centred at 256.3 GHz. Frequencies are shifted to the systemic velocity of the region. The data are shown in black. Bottom panel: zoom-in of the top panel to highlight weak lines.

In the text
thumbnail Fig. 3

First-moment (intensity-weighted velocity) map of CH3OH (top panels) lines at 254.0153 GHz (left) and 241.2679 GHz (right). Pixels with S/Ns of less than three are masked out. The zero-moment (integrated intensity) map for each line is overlaid in grey contours. Contours start at 9σ and are in steps of 12σ, with σ = 1.34 × 10−2 and 7.16 × 10−3 Jy beam−1 km s−1, for the left and right panel, respectively. The black and green crosses mark the locations of the peak continuum emission and peak integrated line intensity, respectively. Middle panel: same as top panels but for NH2 CHO lines at 239.952 GHz (left) and 254.727 GHz (right). Contours start at 9σ and are in steps of 12σ, with σ = 6.17 × 10−3 and 6.25 × 10−3 Jy beam−1 km s−1, for the left and right panel, respectively. Bottom panels: same as top panels but for CH3 C2H lines at 239.2523 GHz (left) and 239.2112 GHz (right). Contours start at 6σ and are in steps of 3σ, with σ = 7.29 × 10−3 and 6.35 × 10−3 Jy beam−1 km s−1, for the left and right panel, respectively.

In the text
thumbnail Fig. 4

Ratio between the fitted FWHM of the low and high upper state energy transitions listed in Table D.1. A ratio lager than 1 indicates that the spatial extent of the low upper state energy transition is larger than that of the high upper state energy transition.

In the text
thumbnail Fig. 5

Relative abundances of N-bearing species (top panel) and O-bearing species (bottom panel) predicted by models and detected towards AFGL 4176, Sgr B2(N), and IRAS 16293B. For AFGL 4176, Sgr B2(N), and IRAS 16293B the colours of bars indicate the excitation temperature derived for each species.

In the text
thumbnail Fig. 6

Relative abundances of species detected towards AFGL 4176 and IRAS 16293B. Labels in bold refer to species with five or more detected transitions towards AFGL 4176. Oxygen-bearing species are labelled by numbers 1–9 in red, N-bearing species are labelled by numbers 20–25 in blue, and S-bearing species are labelled by numbers 30–34 in yellow. Black arrows indicate upper limits on the relative abundance of CH2 (OH)CHO in AFGL 4176 and of SO in IRAS 16293B. The dashed grey line indicates the 1:1 ratio of relative abundances in AFGL 4176and IRAS 16293B.

In the text
thumbnail Fig. C.1

Same as Fig. 2 for species detected towards AFGL 4176 in the spectral window centred at 254.0 GHz.

In the text
thumbnail Fig. C.1

continued.

In the text
thumbnail Fig. C.2

Same as Fig. 2 for species detected towards AFGL 4176 in the spectral window centred at 240.5 GHz.

In the text
thumbnail Fig. C.2

continued.

In the text
thumbnail Fig. C.3

Same as Fig. 2 for species detected towards AFGL 4176 in the spectral window centred at 239.0 GHz.

In the text
thumbnail Fig. D.1

Same as Fig. 3 for 13CH3OH lines at 256.1716 GHz (left) and 253.6895 GHz (right). Contours start at 9σ and are in steps of 6σ and 12σ, with σ = 6.27 × 10−3 and 6.87 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

In the text
thumbnail Fig. D.2

Same as Fig. 3 for CH3OCH3 lines at 240.9851 GHz (left) and 253.9075 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 4.95 × 10−3 and 6.08 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

In the text
thumbnail Fig. D.3

Same as Fig. 3 for C2H5OH lines at 254.3841 GHz (left) and 253.3274 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 5.58 × 10−3 and 5.39 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

In the text
thumbnail Fig. D.4

Same as Fig. 3 for CH3COCH3 lines at 240.9988 GHz (left) and 254.0822 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 4.96 × 10−3 and 5.32 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

In the text
thumbnail Fig. D.5

Same as Fig. 3 for CH3OCHO lines at 240.0211 GHz (left) and 256.4478 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 5.36 × 10−3 and 5.96 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

In the text
thumbnail Fig. D.6

Same as Fig. 3 for CH3CN lines at 239.0965 GHz (left) and 238.8439 GHz (right). Contours start at 9σ and are in steps of 12σ, with σ = 1.26 × 10−2 and 5.78 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

In the text
thumbnail Fig. D.7

Same as Fig. 3 for vibrationally excited CH3CN lines at 240.0895 GHz (left) and 239.7917 GHz (right). Contours start at 9σ and are in steps of 12σ and 6σ, with σ = 5.41 × 10−3, for the left and right panels, respectively.

In the text
thumbnail Fig. D.8

Same as Fig. 3 for C2H3CN lines at 254.1375 GHz (left) and 256.4480 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 5.29 × 10−3 and 6.0 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

In the text
thumbnail Fig. D.9

Same as Fig. 3 for C2H5CN lines at 254.6336 GHz (left) and 256.3959 GHz (right). Contours start at 3σ and are in steps of 6σ, with σ = 5.18 × 10−3 and 5.7 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

In the text
thumbnail Fig. D.10

Same as Fig. 3 for H2CS lines at 240.5491 GHz (left) and 240.3322 GHz (right). Contours start at 9σ and are in steps of 12σ, with σ = 6.81 × 10−3 and 5.27 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

In the text
thumbnail Fig. D.11

Same as Fig. 3 for SO2 lines at 256.2469 GHz (left) and 238.9925 GHz (right). Contours start at 9σ and are in steps of 12σ, with σ = 1.96 × 10−2 and 8.63 × 10−3 Jy beam−1 km s−1, for the left and right panels, respectively.

In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.