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A&A
Volume 669, January 2023
Article Number A137
Number of page(s) 20
Section Interstellar and circumstellar matter
DOI https://doi.org/10.1051/0004-6361/202244183
Published online 25 January 2023

© The Authors 2023

Licence Creative CommonsOpen 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

The discovery of complex organic molecules (COMs, i.e., C-bearing molecules containing ≥6 atoms, Herbst & van Dishoeck 2009) in high-mass star-forming regions has led to the finding that these COMs are sublimated from the icy mantles into the gas-phase in the hot (≥100 K) and dense (≥107 cm−3) regions heated by the central source. These molecules are also present in chemically rich cores and disks (Caselli & Ceccarelli 2012; van Dishoeck 2014, and references therein). In low-mass star-forming regions, the COM emission is confined in a compact region with T≥100 K surrounding the protostar and it is called “hot corino” (Ceccarelli 2004). In these environments, COMs are believed to be at least partially released from the icy mantles due to thermal evaporation and also formed in the gas phase upon sublimation of parent species. However, it has also been suggested that such COM emission can trace accretion shocks, disk atmospheres, or outflow cavities (Drozdovskaya et al. 2015; Csengeri et al. 2018; Belloche et al. 2020). It is therefore important to study these objects and their surroundings in detail to unveil their nature.

The symmetric top CH3CN molecule, with its K-ladder transitions, provides a powerful tool for diagnosing the temperature structure in hot dense regions and it is commonly observed in high-mass star-forming regions (Galván-Madrid et al. 2010; Beltrán et al. 2011; Hunter et al. 2014; Ilee et al. 2016). In low-mass star-forming regions, CH3CN has been detected in NGC 1333 IRAS4B/2A (Bottinelli et al. 2007), IRAS 16293-2422 (Bisschop et al. 2008; Calcutt et al. 2018), as well as in several sources from the CALYPSO (Belloche et al. 2020) and PEACHES surveys (Yang et al. 2021). In addition, with high angular-resolution observations, CH3CN has been found to directly trace the Class II disk surfaces in recent works (Bergner et al. 2018; Loomis et al. 2018).

SVS13 is a multiple system located in the NGC 1333 cluster within the Perseus molecular cloud complex (d = 293 pc, Ortiz-León et al. 2018). The multiple system includes a Class I close binary SVS13A (VLA 4A/B or Per-emb-44B/A), with a separation of 0″.3 (~90 au) and three companions at a distance of 5″.3 (SVS13A2, ~1600 au), 14″.9 (SVS13B, ~4400 au), and 34″.5 (SVS13C, ~10 000 au) based on continuum emission (Anglada et al. 2004; Tobin et al. 2016, 2018; Segura-Cox et al. 2018; Tychoniec et al. 2020). The close binary drives multiple jets and outflows (Lefèvre et al. 2017; Lee et al. 2016; Stephens et al. 2018), and the presence of jet wiggling suggests that an extra undetected source may be present (~20–30 au from VLA4B, Lefèvre et al. 2017). A dusty tail or spiral at 1.3 mm is seen connected to VLA4A (Tobin et al. 2018), suggested to be a fragmenting disk based on its gravitational instability. Recently, with the Atacama Large Millimeter Array (ALMA) 0.9 mm observations at high angular resolution (0″.1 or ~30 au), Diaz-Rodriguez et al. (2022) found a circumbinary disk, as well as two circumstellar disks, around VLA4A/B. These authors estimated the stellar masses using orbital motions (see also Maureira et al. 2020 for a similar method on the Class 0 binary IRAS 16293A). The total mass of the binary was found to be 1.0±0.4 M⊙ and the masses of the individual protostars were estimated as 0.27±0.10 M⊙ for VLA4A and 0.60±0.20 M⊙ for VLA4B. In addition, SVS13A is considered to be a protostar system undergoing an accretion burst, given the high bolometric luminosity, Lbol = 45.3 L⊙ (Hsieh et al. 2019). The origin of such accretion outbursts are still unclear (Audard et al. 2014), however, they may be triggered by gravitational instability (Vorobyov & Basu 2010) or magnetorotational instability (Armitage et al. 2001). In addition, anisotropic envelope accretion could also lead to bursts. Indeed, recent observations have found infalling streamers at envelope scales, which have also been suggested to change the protostellar accretion by funneling material into the inner region (Pineda et al. 2020, 2022; Alves et al. 2020; Ginski et al. 2021; Murillo et al. 2022; Cabedo et al. 2021; Garufi et al. 2022; Thieme et al. 2022; Valdivia-Mena et al. 2022). Such infalling streamers are also found in numerical simulations (Seifried et al. 2015; Seifried & Walch 2015; Zhao et al. 2018).

Regarding the chemical complexity, a large number of COMs have been detected toward SVS13A (De Simone et al. 2017; Belloche et al. 2020; Yang et al. 2021). For instance, water emission was detected with a broad linewidth ~5–20 km s−1 (NGC 1333-IRAS3A; Kristensen et al. 2012). Using the IRAM 30 m telescope, Codella et al. (2016) detected deuterated water (HDO) emission toward SVS13A, and a non-local thermal equilibrium (non-LTE) analysis has suggested that it comes from a hot (150–260 K), dense (≥3 × 107 cm−3), and compact (R ~ 25 au) region. Given such physical conditions, it has been suggested that the COM emission comes from a hot corino inside SVS13A. Diaz-Rodriguez et al. (2022) have found a circumstellar disk around VLA4A, with a radius of ~30 au traced by ethylene glycol (aGg’-(CH2OH)2). Furthermore, Bianchi et al. (2017, 2019) found that the COM abundances are not signifcantly different between this Class I source and Class 0 sources, but the deuteration fraction of organic molecules is lower. A high CH2DCN/CH3CN ratio was later measured for SVS13A, suggesting that CH3CN was formed via gas-phase reactions during the pre-stellar phase (Bianchi et al. 2022a).

In this work, we present NOrthern Extended Millimeter Array (NOEMA) CH3CN, CH313CN, DCN, and C18O observations toward SVS13A. Thanks to the wide bandwidth and high spectral resolution that are achievable with the PolyFiX correlator, along with the sensitivity achievable with NOEMA, we are able to study the temperature and kinematics of SVS13Athrough CH3CN and DCN. In Sect. 2, we present the data and observations used in this paper. The results and analysis are detailed in Sect. 3. In Sect. 4, we discuss the scientific outputs and the conclusions are summarized in Sect. 5.

2 Observations

2.1 NOEMA observations

We observed SVS13A using the NOrthern Extended Millimeter Array (NOEMA) of the Institut de Radioastronomie Millimétrique (IRAM). The observations are part of the MPG – IRAM observing program PROtostars & DIsks: Global Evolution (PRODIGE; Project ID: L19MB002, PIs: P. Caselli and T. Henning). The observations consist of C-array configurations executed on December 29, 2019 and January 5, 2020, as well as D-array configurations executed on August 6, 2020 and September 7, 2020. In all executions, ten antennas were used. The on-source observing time is 2.74 h for the C-array and 2.22 h for the D-array. The bandpass calibrators were 3C84 and 3C454.3. Two phase calibrators, 0333+321 and 0322+222, were observed for all executions. The flux calibrators were LKHA101 and MWC349. Combining the C- and D-arrays with baselines 15.6–368 m (11.5–270.0 kλ) resulted in a synthesized beam size of 1″.2 × 0″.7 with natural weighting for line cubes and 1″.1 × 0″.6 with uniform weighting for the continuum image at ~220 GHz. The maximum recoverable scale is 22″.07.

The spectral setup consists of four broadband low-resolution windows and 39 narrow-band high-resolution windows within the broad-band frequency region. The broadband windows cover the frequency ranges of 214.7–218.8 GHz, 218.8–222.8 GHz, 230.2–234.2 GHz, and 234.2–238.3 GHz with a channel width of 2 MHz (~2.7 km s−1). In this paper, we present three narrowband windows that were designed to cover the CH3CN J = 12–11 K-ladder, DCN J = 3–2, and C18O J = 2–1. The channel width of these three windows is 62.5 kHz (~0.08 km s−1).

The data reduction was performed by the NOEMA pipeline calibration routine using the GILDAS-CLIC package1, with additional manual flagging. We performed a phase-only self-calibration with the GILDAS MAPPING package using the continuum maps from the broad-band spectral windows (see Sect. 3.1). The iterative self-calibration was performed with solution intervals of 300 s, 135 s, and 45 s on the data which includes only line-free channels.

For the imaging of the line emission, the continuum subtraction for the narrow spectral windows is done using the uv_baseline task in the GILDAS/MAPPING package. Given the line-rich spectra, the line free regions are manually selected after visually examining the spectra. Imaging is done using the clean task with natural weighting for line cubes in order to increase the sensitivity. The continuum map was produced with robust = 1 Briggs weighting because the sensitivity is already high given the broadband width (4 GHz for each). With a manual clean mask, multi-scale clean algorithm is performed with a clean threshold of 1σ. The resulting data cubes have a beam size of 1″.23 × (0″.72 with a pixel size of 0″.15. The rms noise level is ~0.013 Jy beam−1 for these image cubes with a channel width of ~0.08 km s−1. In comparison, the beam size of the continuum image is 1″.07 × 0″.61, with a pixel size of 0″.15, and the rms noise level is 0.5 mJy beam−1 (0.02 K).

thumbnail Fig. 1

1.3 mm continuum emission toward the SVS13 region from NOEMA (left, rms ~20 mK) and ALMA (right) observations. In the left panel, the dashed circle shows the primary beam of the NOEMA observation and the dashed box represents the FoV in the right panel. The black plus signs represent the positions of VLA4A/B from Hsieh et al. (2019). The arrow in the top right corner indicates the shift (0″.13) of the self-calibrated ALMA image.

2.2 ALMA archival data

We used the ALMA archival continuum and C18O maps from VANDAM2 (Tobin et al. 2016, 2018). The ALMA project ID is 2013.1.00031.S. The observations and data calibration are detailed in Tobin et al. (2018). The angular resolutions are 0″.26 × 0″.16 for continuum image and 0″.36 × 0″.26 for C18O channel maps given the weighing and uv-range in use (see Tobin et al. 2018). The channel width of the C18O maps is 0.25 km s−1.

As the ALMA data from Tobin et al. (2018) were self-calibrated, we needed to apply a shift to the coordinates to match the position of VLA4A/B as observed in ALMA observations by Hsieh et al. (2019), for which no self-calibration was performed. The spatial resolutions of these observations are 0″.27 and 0″.11, respectively. The final shift applied to the ALMA observation is ~0″.13 as shown in Fig. 1.

3 Results and analysis

3.1 Continuum images

The left panel of Fig. 1 shows the NOEMA continuum image. The multiple system consisting of SVS13A, SVS13A2, and SVS13B is resolved. Extended dust emission is also detected between SVS13A and SVS13A2, separated by ~1600 au. The right panel shows a zoomed-in image on SVS13A from the high-resolution ALMA observation at 1.3 mm from VANDAM (Tobin et al. 2018). The close binary is resolved, as well as a spiral pattern that is interpreted as a fragmenting disk due to gravitational instability (Tobin et al. 2018).

3.2 CH3CN and CH313CN emission

The high spectral-resolution narrowband spectrum of CH3CN and CH313CN toward the peak continuum emission of SVS13A is shown in Fig. 2. The CH3CN J = 12–11 K-ladder emission (K = 0 to K = 7) is clearly detected and, albeit with a lower signal-to-noise ratio (S/N), the CH313CN J = 12–11 K = 0 to K = 5 emission is also marginally detected (S/N~3.1 for K = 5). A number of additional lines were identified using an automatic line identification algorithm (XCLASS, Möller et al. 2017), applied to the full broadband windows with a spectral coverage of 16 GHz. The LTE radiative transfer models for each identified molecule are provided. In order to check the line contamination, the identified lines with Einstein coefficients > 10−6 s−1 and Eup < 500 K are labeled. Based on the LTE model, the emission from the CH3OCHO (Aul = 6 × 10−6 and Eup = 382 K) and 33SO2 (Aul = 1 × 10−4 and Eup = 50 K), labeled in purple, is very weak or undetectable and should not significantly affect the later CH313CN analysis. However, a detailed study including full high-spectral resolution windows is required. We will present the full chemical inventory in a future paper (Hsieh et al., in prep.).

Figure 3 (left and center) shows the integrated intensity of the CH3CN (123-113) and CH313CN (121−111)${\rm{CH}}_3^{13}{\rm{CN}}\,{\rm{(1}}{{\rm{2}}_1} - {\rm{1}}{{\rm{1}}_1}{\rm{)}}$ emission, the brightest component with less contamination from other line emission. As the emission appears compact, we fit it with a Gaussian in wu-space. The derived size of the CH3CN emission is 0″.42 + 0″.02 × 0″.34 + 0″.01 (~110 au), likely overestimated due to insufficient resolution. The size of CH313CN is found to be 0.3″ × 10−5″ from the fitting for which the minor axis of 10−5 arcsec is unlikely to be constrained. Indeed, higher-resolution observations (beam ~0″.5) derived a smaller size of 0″.3 (90 au) for the COM emitting region (Belloche et al. 2020), which is likely also an upper limit (see Sect. 3.3).

thumbnail Fig. 2

Spectrum of CH3CN and CH313CN toward the peak of the continuum emission. The labels of the line frequencies, blue (CH3CN), orange (CH313CN), grey and pink bars, are shifted assuming a systemic velocity of 8.1 km s−1. We only marked lines with an Einstein coefficient > 10−6 s−1 and Eup < 500 K; the Einstein coefficients are 6 × 10−6 s−1 (Eup = 382 K) for the CH3OCHO line and 1 × 10−4 s−1 (Eup = 50 K) for the 33SO2 line in pink while both are very weak or undetectable based on the XCLASS model from wideband spectra including more transitions. The horizontal dashed line indicates the continuum level of 5.8 K determined from line free channels. The sensitivity per channel is 380 mK.
thumbnail Fig. 3

Integrated intensity map of CH3CN 123–113 emission (4.0–12.0 km s−1) shown to the left. The contours represent the ALMA 1.3 mm continuum emission from Fig. 1. with levels of 3σ, 5σ, 7σ, 10σ, 30σ, and 70σ. The dashed box shows the field of view in the right panel. The central panel shows the same details as the left, but for CH313CN 121–111 emission. Gaussian centers from the uυ-domain fitting shown on the right. The colored circles (CH3CN 123–113) and triangles (CH3CN 127–117) show the positions from the uv-domain fitting, with the color indicating the velocity.

3.3 CASSIS fitting

The CH3CN K-ladder gives us the opportunity to study the physical conditions of the hot gas. We performed the CASSIS3 (Vastel et al. 2015) Markov chain Monte Carlo (MCMC) fitting to the CH3CN (J = 12–11, K = 0–7) and CH313CN (J = 12–11, K = 0–5) emission. The line parameters are taken from CDMS4 (Müller et al. 2005; Endres et al. 2016), while the parameters for CH3CN and CH313CN were originally provided by Müller et al. (2015) and Müller et al. (2009), and references therein. The partition function Q(T) of CH3CN includes vibrational contribution up to v8 = 1, while only the ground state is available for CH313CN in the CDMS database. Vibrational correction of the isotopologue CH313CN can be done by scaling Q(T), using the ratios of the Q(T) of CH3CN with and without a vibrational contribution. Since the ground state Q(T) of CH3CN and that of CH313CN are almost the same (difference <0.06% at T < 500 K), the scaled partition function of CH313CN, namely, vibrational-corrected, will be the same as that of the CH3CN one within a factor of 0.06 %. We thus use the partition function of CH3CN including vibrational contributions for the CH313CN in our MCMC fitting. We excluded the CH3CN K = 4 component, CH313CN K = 3 component, and the blueshifted wing of the CH3CN K = 6 component from our fitting due to contamination from nearby lines (see Fig. 2). A continuum level of 5.77 K was determined based on the line free channels (Fig. 2) and taken as an input for the simultaneous line fit performed by CASSIS. Technical information on the MCMC sampling is detailed in Appendix A.

Figure 4 (top panel) shows the best-fit spectrum with a one-component LTE model. Table 1 lists the best-fit column density, rotation temperature, linewidth, source size and central velocity, and isotopologue ratio (CH3CN/CH313CN). The posterior probability distribution of each parameter is shown in Fig. A.7. The resulting CH3CN column density and temperature are 5.13 × 1016cm−2 and 247.8 K, respectively, with a source size of (0″.27. The column density and temperature are larger by a factor of 1.3–8.5 and 1.4–1.7 than that in Belloche et al. (2020) and Yang et al. (2021) (Table 1). The difference between our LTE model and the literature most likely comes from the assumption of the source size in the previous works. In Belloche et al. (2020), a source size of 0″.3 based on Gaussian fitting in uv-space was adopted, while Yang et al. (2021) fixed a size of 0″.5 for the unresolved case. As shown in Fig. A.7, the column density and excitation temperature are highly correlated with the source size; we note here that the optical depths from the fitting are 0.36–3.20 for the CH3CN J = 12–11, K = 0–7 emission. It is clear that the adopted source size will determine the column density and excitation temperature as well as the optical depth τ, although this may not significantly change the relative abundances of COMs in the literature (Belloche et al. 2020; Yang et al. 2021). In our case, with the source size as a free parameter, the optical depth is determined based on the line intensity ratios. Because the K = 3n (i.e., 3 and 6) transitions have a degeneracy larger than the others by a factor of 2, increasing the column density will change their optical depths much more than the others (i.e., the K = 3n components will saturate faster than their neighboring transitions). The opacities of the best-fit model are listed in Table A.2. In this case, the source size scales the intensities of all transitions, and the temperature affects the level populations as well as the optical depths. As a result, the high sensitivity data enable us to determine the optical depths as well as column density from the accurate measurement of the intensity ratios.

This one-component model does not adequately reproduce the peak of the optically thick transitions (K = 0–3). In particular, for the K = 3 component, the synthetic line profile is much broader than that of the observation (see also Fig. A.3). This suggests that the optical depths are not properly estimated. Interestingly, the isotopologue ratio [CH3CN/CH313CN]≃16 is surprisingly low compared to the canonical ratio 12C/13C≃68 (Milam et al. 2005). A possibility is that the optical depth, τ, is not properly estimated by this simple one-component model. We therefore performed a fitting that takes CH3CN and CH313CN as independent species with different temperatures, velocities, and linewidths (Fig. 4, second panel). This gives us very different temperatures for CH3CN (246.6 K) and CH313CN (166.8 K), implying that they originate from different regions. In addition, the observed line profile shows an asymmetric structure; the emission is stronger in the blueshifted side in the low-energy transitions, but reversed in the K = 7 component (Fig. A.3).

It is most likely that a one-component model cannot adequately it the CH3CN emission from this known complex structure. We thus performed a two-component LTE model to it the observational spectrum, which results in a better it (Fig. 4, third panel); the synthetic spectral profile and observed profile have similar emission peaks in the K = 0 and K = 1 components and similar widths in the K = 3 component. The best-fit parameters are listed in Table 1 and their opacities are listed in Table A.2. These two components are assumed to be spatially separated, that is, the interaction mode is turned off in our CASSIS setup. Resolving such a complicated scenario requires data with a higher spatial resolution. In fact, the binary system has been spatially resolved by continuum emission (Tobin et al. 2018) and velocity gradient toward SVS13A is revealed via other COM emission (Diaz-Rodriguez et al. 2022). A compact, optically thick, redshifted component and a thin blueshifted component are separated, which is in agreement with the asymmetric structures in the line profiles. This result suggests that the traditional rotation diagram or one-component model cannot accurately describe this complex profile. In this case, the two-component model implies the presence of an optically thick component. This would result in an underestimation of the column density when using one-component fitting. It is, however, noteworthy to observe that although the isotopologue ratio is higher in the two-component model as compared to the one-component model, they are still lower than the canonical ratio (see Table 1).

Furthermore, we estimated the H2 density by applying the non-LTE radiative transfer code RADEX (van der Tak et al. 2007) in CASSIS (Fig. 4, bottom panel). The collision rates are taken from LAMDA5 as calculated by Green (1986). We note here the geometry of the emitting gas is described as a sphere in the RADEX calculation and the two components are spatially separated. We have a compact optically thick component and a relatively extended optically thin component at the blue- and red-shifted sides, similarly to the LTE case (Table 1). The compact component is close to LTE with nH2~1.2×108${n_{{{\rm{H}}_{\rm{2}}}}}\~1.2 \times {10^8}$ cm−3 (critical density ~107–108 cm−3 for a temperature of ~200 K). Given the nH2${n_{{{\rm{H}}_{\rm{2}}}}}$ densities of 6.2 × 106 cm−3 and >1.2 × 108 cm−3, we estimated the enclosed masses of 1.1 × 10−5 M⊙ within 0″.287 (r = 42 au) for the extended component and >2.4 × 10−5 M⊙ within 0″.141 (r = 21 au) for the compact component. These values are smaller than the disk masses (0.004–0.009 M⊙ for VLA4A and 0.009–0.030 M⊙ for VLA4B) derived from the dust continuum emission (Diaz-Rodriguez et al. 2022). One possibility is that the optically thick CH3CN emission does not trace the dense disk midplanes. Alternatively, the nH2 density can be higher than the critical density by about two orders of magnitude. We note that here the comparison simply assumes a uniform density to give a very rough idea.

thumbnail Fig. 4

Modeled CH3CN and CH313CN spectra overlaid on the observed spectra. The first panel shows the LTE model one velocity component fitting. The second panel is the same as the first panel but treats CH3CN and CH313CN as two species with independent temperature, velocity and linewidth. The third panel is the LTE two velocity component fitting, while the fourth panel shows the non-LTE one. The red curve shows the modeled spectrum. The K = 4 component of CH3CN and K = 3 component of CH313CN suffer from severe line contamination so they were not included in the fitting. The model in these frequency ranges is shown with the dashed line. The red and blue shaded areas in the two bottom panels represent the modeled spectrum from the individual components. The residual from the best-fit is shown in the bottom frame of each panel. The best-fit parameters are listed in Table 1 and the corner plots are shown in Appendix A.
Table 1

CH3CN fitting results.

3.4 Kinematics of CH3CN

For a spectrally resolved, optically thin line, the peak position of the line emission is believed to represent the most likely location of the emitting gas (Sargent & Beckwith 1987; Harsono et al. 2013). To disentangle the velocity structure of the CH3CN, we performed a uυ-domain Gaussian fitting to the spatial emission at different velocity channels. For CH3CN, the thermal width of CH3CN (12–11) at 200 K is ~0.5 km s−1. Thus, we took a step of 0.5 km s−1 to increase S/N, while resolving the gas kinematics, along with a velocity range from 6 to 10 km s−1 (Fig. A.3). We used the K = 3 and K = 7 components, which do not suffer as much from contamination of other lines (Fig. 2). The K = 7 emission is most likely optically thin, and the K = 3 emission (although it is optically thick) has the highest S/N among the K-ladder transitions. Figure 3 (right) shows the fitted central positions from different velocities. The emitting centers show similar trends in these two transitions; the majority of the CH3CN emission is associated with VLA4A, which is considered to be the warmer source with a higher accretion rate (Hsieh et al. 2019). A clear velocity gradient from the west to the east is seen. The direction of this velocity gradient is broadly consistent with the position angle of the circumstellar disk around VLA4A and the circumbinary disk (Diaz-Rodriguez et al. 2022); in the literature, ethylene glycol (aGg’-(CH2OH)2) is found to trace rotating gas from the circumstellar disk of VLA4A (Diaz-Rodriguez et al. 2022). By comparing these emitting centers to kinematic models, we find that the CH3CN emission is most likely associated with a disk or a rotating envelope around VLA4A, rather than that of VLA4B or the circumbinary disk (see Appendix B). However, this analysis shows that the velocity gradient from CH3CN cannot be interpreted by a simple rotationally supported disk or from angular momentum conservation. It is then likely that CH3CN and aGg’-(CH2OH)2 trace different gas components. High angular-resolution observations are required to confirm the origins of the CH3CN emission.

3.5 A streamer in the DCN and C18O emission

Figure 5 shows the NOEMA DCN (3–2) integrated intensity map. The DCN emission traces the central binary system and reveals a streamer in the east. Interestingly, this streamer matches the tail of the spiral structure that is seen in the continuum emission and extends this spiral to outer regions at a few arcsec (~2″.4, 700 au). These kinds of streamers connecting inner and outer regions have been recently identified in embedded systems and are considered to be infalling flows that provide the material supplying the growth of disks and central protostars (Pineda et al. 2020; Alves et al. 2020; Murillo et al. 2022; Cabedo et al. 2021; Thieme et al. 2022; Valdivia-Mena et al. 2022).

Because the DCN spectral profile is likely to have originated from a complex structure (Fig. 6), we employed a multiple-component fitting in order to isolate the streamer and further study its kinematics. We fit the hyperfine structure of the DCN (3–2) line emission using PySpecKit (Ginsburg & Mirocha 2011) with the line parameters from CDMS (Müller et al. 2005; Endres et al. 2016), for which the DCN parameters were taken from Brünken et al. (2004) and references therein. Although the satellite line emission is marginally detected (Fig. 6), the weak emission in the streamer suggests that the DCN (3–2) emission is optically thin. Toward the center, the hyperfine structure is not clearly seen with the complex and broad line emission. However, analyzing this DCN structure toward the center is beyond the scope of this paper. Thus, we assumed a total optical depth of 0.1 in the hyperfine structure fitting (HFS). Toward the center region, several velocity components are revealed from the spectra. We therefore performed a hyperfine fitting with multiple velocity components (up to three, see Appendix D). We only focus on the regions where the peak intensity has a S/N>25 from the spectrum to distinguish the streamer from the more extended emission background. Figure 6 shows the maps of the fitted centroid velocity and linewidth of the blueshifted component which traces the streamer. A small velocity gradient ~0.12 km s−1 arcsec−1 along the streamer with a relatively narrow line width of ~0.7 km s−1 is revealed. We note that a very small velocity break from 8.4 km s−1 to 8.1 km s−1 comes from a change of fitting from two to three velocity components (see Appendix D).

Using ALMA, Tobin et al. (2018) found that the dusty spiral from the continuum emission aptly matches the C18O (2–1) blueshifted emission (6.0–8.5 km s−1, their Fig. 15). Figure 7 shows the NOEMA C18O (2–1) channel maps at 7.7–8.5 km s−1 in comparison with the streamer traced by the DCN and continuum emission. Although at the velocity of the DCN streamer (~8.3–8.6 km s−1, Fig. 6), the C18O emission is offset from the DCN emission, the emission peak matches the inner spiral at a velocity ≤8.2 km s−1 well; and this velocity well connects the DCN blueshifted end from ~8.3 km s−1 to a velocity of ~7.7 km s−1 in the inner region (see also Fig. C.2 from the ALMA data). The channel maps of DCN/C18O in the region of interest are provided in Appendix C.

Figure 6 also shows the DCN and C18O line profiles at four selected positions from the streamer. In the outer part of the streamer (i.e., position 4), the C18O emission is offset from the DCN emission in velocity. In the middle part of the streamer (i.e., position 3), the DCN and C18O lines have a similar centroid velocity. At position 2, while the DCN emission becomes relatively weak compared with that of C18O, the lines show similar profiles. It seems that along the streamer, the physical properties change, producing a different DCN/C18O integrated intensity ratio. However, the velocity gradient revealed by DCN emission is broadly consistent with that of C18O at velocities ≲8.3 km s−1 (Fig. C.2) and might trace an infall of material. More observations at higher spatial resolutions are needed to test for infall via modeling.

To estimate the mass of the streamer, we calculated the DCN column density (Fig. 8) over a velocity range of 7–10 km s−1 targeting the streamer. The column density is calculated under assumptions of LTE and optically thin emission (i.e., Eq. (80) in Mangum & Shirley 2015). Due to the lack of measurements for the DCN collision rates, we consider those for HCN (van der Tak et al. 2020). The critical density of HCN (3–2) is 1.0 × 107 cm−3 at 20 K and 5.7 × 106 cm−3 at 50 K in the optically thin limit (Shirley 2015). This is close to the lower bound estimated from previous works nH2≥106${n_{{{\rm{H}}_{\rm{2}}}}} \ge {10^6}$ cm−3 within 600 au using H2CO (Bianchi et al. 2017). Thus, the LTE assumption might cause an underestimate of the actual streamer mass. The excitation temperature is estimated considering the luminosity of SVS13A (45.3 L⊙) and the projected distance to VLA4A (i.e., Td=2.4×10(r/pc)2/5(L106L⊙)1/5${T_{\rm{d}}} = {{2.4 \times 10} \over {{{\left( {{r \mathord{\left/ {\vphantom {r {pc}}} \right. \kern-\nulldelimiterspace} {pc}}} \right)}^{{2 \mathord{\left/ {\vphantom {2 5}} \right. \kern-\nulldelimiterspace} 5}}}}}{\left( {{L \over {{{10}^6}{L_ \odot }}}} \right)^{{1 \mathord{\left/ {\vphantom {1 5}} \right. \kern-\nulldelimiterspace} 5}}}$, Goldreich & Kwan 1974). This gives a temperature of ~35–45 K in the streamer. To exclude the extended DCN emission, we employ a mask with a peak emission above 25 σ and a linewidth <1 km s−1 from the hyperfine fitting (Fig. 6). This gives a conservative lower limit to the streamer area. Figure 8 shows the DCN column density of the streamer. Adopting the same LTE excitation state for C18O, we further estimate the abundance ratio of [DCN]/[C18O] to be ~1.36 × 10−3 at position 3 in Fig. 6. We note that with a different Tex = 20–80 K, this abundance ratio varies from (1.54–1.28) × 10−3. Taking the canonical ISM value of CO/C18O~560 and CO abundance of 10−4 (Wilson & Rood 1994), we find a DCN abundance of ~2.4 × 10−10, with respect to H2. As a result, the streamer mass is estimated to be ≳0.0042 M⊙.

thumbnail Fig. 5

Integrated intensity map of DCN (3–2) over a velocity range of 8.26–8.8 km s−1. The contours represent the ALMA 1.3 mm continuum emission with levels at 3, 5. 7, 10, 30, and 70σ. The blue and red arrows indicate the CO outflow directions from Lefèvre et al. (2017).
thumbnail Fig. 6

DCN (3–2) centroid-velocity (top-left) and linewidth (top-right) maps from the blueshifted component in the hyperfine structure fitting (see Appendix D). The white contours show its peak intensity. The contour levels start at 25σ and increase in 5σ step, where σ = 0.3 K. The blue contour shows the ALMA 1.3 mm continuum emission at the 3σ level. Spectra of DCN (3–2), C18O (2–1), and CH3CN (123–113) toward the four positions marked in the right panel (bottom). The blue bars show the relative intensities of the DCN (3–2) hyperfine structure at the velocity of the blueshifted component (see Appendix D).

4 Discussion

4.1 Physical and chemical conditions from CH3CN

Table 1 lists the physical conditions of SVS13A derived from the CH3CN K-ladder, with different models. Besides the main CH3CN isotopologue, the high-sensitivity data also allowed us to detect the isotopologue CH313CN (Fig. 2). With the LTE one-component model, the abundance ratio CH3CN/CH313CN is found to be ~20. This is surprisingly low given the interstellar isotopologue ratio [12C]/[13C]~68 (Milam et al. 2005). In IRAS 16293, Calcutt et al. (2018) find the column density ratios CH3CN/CH313CN~67 and CH3CN/13CH3CN~77. 12C/13C ratio can be affected by selective photodissociation and fractionation. For example, the 12CO/13CO ratio is found to vary by a factor of >4 from the inner to outer parts of the TW Hya disk (Yoshida et al. 2022). As selective photodissociation is suggested to affect the isotopologue ratios in the disk atmosphere (Miotello et al. 2014; Smirnov-Pinchukov et al. 2020), this mechanism can decrease 12C/13C ratio by destruction of the main isotopologue. On the other hand, isotopic fractionation reaction can enhance the 13C abundances in molecules but usually happen in cold region with exothermic reactions (Roueff et al. 2015). It is noteworthy that in SVS13A, CH3CN is suggested to be synthesised via gas-phase reactions during cold prestellar phase (Bianchi et al. 2022a). Alternatively, the CH3CN column density is underestimated due to an inaccurate opacity correction. This scenario is similar to that reported by Diaz-Rodriguez et al. (2022) with the 13CH3OH/CH318OH~3, significantly smaller than the expected value of ~7. Broadly speaking, the optically thick CH3CN emission (which depends on K-transition) and the optically thin CH313CN emission can trace a different region or layer.

Thus, we conducted a fitting procedure assuming that CH3CN and CH313CN are independent species (i.e., different Vlsr, ΔV, Tex and Nmol, Table 1 LTE two species). However, the source size of CH313CN was fixed to 0″.27 to match that of CH3CN. Given that the emission of CH313CN is optically thin, the derived column density is inversely proportional to the square of the source size. As a result, the temperature of CH313CN is significantly lower than that of CH3CN. This result (Table 1) implies that the CH313CN with a smaller optical depth traces a region different from CH3CN, which might explain the small column density ratio.

We also employed a two velocity-component model, in an attempt to resolve the issues of optical depth (K = 0–3) and asymmetric line profile (Fig. A.3). A relatively optically thin blueshifted component (τ = 0.28–3.2) and an optically thick (τ = 0.8–6.4) redshifted component are found (Fig. 4). The residual from the optically thick K = 3 component is now significantly decreased. Table 1 lists the fitted parameters from the MCMC fitting as shown in Fig. 4. The result implies that an optically thick compact component was missed in the one-component model, while the optically thin component can dominate the line emission. Because of the missing of the optically thick component due to insufficient spatial resolution, the column density of the CH3CN can be severely underestimated. It is noteworthy that, with the two-velocity component model, the isotopologue ratios of CH3CNCH313CN${{{\rm{C}}{{\rm{H}}_3}{\rm{CN}}} \over {{\rm{C}}{{\rm{H}}_3}^{{\rm{13}}}{\rm{CN}}}}$ (~25 and ~17) are still too low compared to the canonical value (~68; Milam et al. 2005). Therefore, the fitting procedure taking CH313CN as an independent species is still required.

Previous works conducted non-LTE analysis using different chemical tracers to estimate the H2 density (nH2${n_{{{\rm{H}}_2}}}$) toward SVS13A. Using the IRAM 30 m telescope, Codella et al. (2016) estimate a H2 volume density of ≥3 × 107 cm−3 inside a small radius of 25 au with a temperature of 150–260 K through HDO observations. This compact source is believed to originate from a hot corino. With CH3OH, Bianchi et al. (2017) found a similarly compact component with nH2>108${n_{{{\rm{H}}_2}}} > {10^8}$ cm−3 but lower Tkin ~ 80 K, within a radius of ~35 au, and an additional extended component with nH2>106${n_{{{\rm{H}}_2}}} > {10^6}$ cm−3 and Tkin < 70 K within ~350 au. Recently, Codella et al. (2021) suggest that the SO, SO2, and H2CS emission from SVS-13A is confined within the hot corino, with a size 60–120 au, nH2≥1×105−6×106${n_{{{\rm{H}}_2}}} \ge 1 \times {10^5} - 6 \times {10^6}$ cm−3 and Tkin = 100–300 K. In our non-LTE two-component models of CH3CN, we find a compact component with a size of 0″.14 (40 au), with nH2>108${n_{{{\rm{H}}_2}}} > {10^8}$ cm−3 (LTE) and an extended component with a size of 0″.29 (85 au) and nH2~6×106${n_{{{\rm{H}}_2}}}\~6 \times {10^6}$ cm−3. Our result is broadly consistent with the literature; most likely, SVS 13A consists of a compact high-density hot corino with a diameter of 50–70 au, and an extended warm structure up to ~120 au. It is noteworthy that with the high-resolution ALMA observation, Diaz-Rodriguez et al. (2022) decompose SVS 13A into three disks: VLA4A (67 × 62 au), VLA4B (unclear from line observation), and a circumbinary disk (240 × 180 au).

Codella et al. (2016) reported the HDO observation toward SVS 13A; the non-LTE analysis gives the best-fit column density of 4 × 1017 cm−2, and a temperature of 200 K with a source size of 0″.2. The source size as well as the linewidth of ~4.5 km s−1 (H2O, Kristensen et al. 2012) are broadly consistent with that of CH3CN. Further, Codella et al. (2016) found an upper limit of water column density ≤8 × 1017 cm−2 based on a H218O non-detection. If we assume that the CH3CN compact component originates from the same region as the HDO and water, we obtain lower limits of CH3CN abundance relative to H2O ≥ 18–26% for the compact component (Table 1). This is higher than the values reported in comets (Hyakutake: 0.01%; Hale-Bopp: 0.02%; 2001 A2: 0.028%; 73P/SW3: 0.030%; 67P: 0.59%) and between interstellar sources (IRAS 16253: 0.25%; Sgr B2(N): 30%) (Mumma & Charnley 2011; Bockelée-Morvan & Biver 2017; Rubin et al. 2019).

thumbnail Fig. 7

C18O (2–1) channel map with the velocity range from 7.7–8.5 km s−1. The blue contour shows the ALMA continuum emission at 3σ and the white contour shows the DCN (3–2) peak intensity map at 25σ
thumbnail Fig. 8

DCN column density map. The blue contours represent the ALMA 1.3 mm dust continuum emission. The black polygon encloses the area that is used to measure the mass of the streamer.

4.2 Accretion flow traced by DCN?

During the embedded phase of a protostar, infall of material plays a crucial role in supplying material to the central source and protoplanetary disk. Recent studies have detected large infalling streamers at an early stage, which are believed to affect the protostellar accretion (Pineda et al. 2020; Alves et al. 2020; Cabedo et al. 2021; Ginski et al. 2021; Murillo et al. 2022; Garufi et al. 2022; Thieme et al. 2022; Valdivia-Mena et al. 2022).

In SVS13A, the streamer is seen in DCN, C18O and partially in dust continuum emission (see Sect. 3.5), connecting the outer envelope with the central binary. At early evolutionary stages, large infalling streamers (1000 s of au) are identified via Carbon-chain species such as HC3N toward Per-emb-2 (Pineda et al. 2020) and HC5N toward IRAS 16293-2422 (Murillo et al. 2022); these molecules usually trace less chemically evolved regions, while the deuterated species DCN traces relatively chemically evolved materials. The fresh materials, together with the enormous streamers, in the literature suggest that these streamers originate from outside the cloud core. In contrast, the streamer toward SVS13A via DCN is much smaller, that is, ~700 au. It is unclear if this streamer is extended farther away or not due to the insufficient sensitivity or lack of proper tracers.

The velocity gradient along the streamer revealed by DCN and C18O suggests it is related to infalling material towards the protostars. The total mass of this streamer is estimated to be Mstreamer ~ 0.0042M⊙ (Sect. 3.5 and Fig. 8). Assuming a freefall collapse, the mass infalling rate from the streamer can be estimated following Pineda et al. (2020): M˙infall=Mstreamertff,$ {\dot M_{{\rm{infall}}}} = {{{M_{{\rm{streamer}}}}} \over {{t_{{\rm{ff}}}}}}, $(1)

where tff is the free-fall timescale. Here, tff can be obtained given the enclosed mass, M, within a radius R, namely, tff=R3GM.$ {t_{{\rm{ff}}}} = \sqrt {{{{R^3}} \over {GM}}} . $(2)

By measuring the proper motion, Diaz-Rodriguez et al. (2022) derive a total mass of VLA4A/B as 1 M⊙ from the orbital motion. Given the detected farthest projected distance of the DCN streamer ~700 au, we derived a free-fall time of ~3000 yr. We note that this can be taken as an upper limit since the initial velocity of DCN is ignored in the calculation (Valdivia-Mena et al. 2022). As a result, we find a mass infalling rate of ≥1.4 × 10−6 M⊙ yr−1 if this streamer is in a free-fall motion. This value is similar to that of the streamers found in other Class I protostars (Per-emb-2: 10−6 M⊙ yr−1 Pineda et al. 2020; Per-emb-50: 1.3 × 10−6 M⊙ yr−1 Valdivia-Mena et al. 2022). The infalling streamer appears to connect to the bursting source VLA4A from the ALMA high-resolution continuum image (Diaz-Rodriguez et al. 2022), for which the circumstellar disk has a mass of 0.004–0.009 M⊙. It would take less than 2800–6400 yr for the infalling streamer to replenish this disk. If the streamer supplies the material to the circumbinary disk with Mdisk = 0.052 M⊙, a replenishment could happen in a timescale of <37 000 yr. These timescales are broadly consistent with the recurrence timescale of 103–105 yr for accretion bursts (Scholz et al. 2013; Contreras Peña et al. 2019; Fischer et al. 2019; Hsieh et al. 2018, 2019; Park et al. 2021), for which the timescale is believed to be smaller in earlier stages.

The high luminosity (L ~ 45.3 L⊙) of SVS13A suggests that it is undergoing an accretion burst with a protostellar mass accretion rate of 2.8 × 10−5 M⊙ yr−1 (Hsieh et al. 2019). As a result, the infalling rate from the envelope to the disk contributed via the streamer can be ≥5% of the protostellar mass accretion rate. If we assume the streamer dominates the mass infalling rate and the disk mass is constant, this implies that the protostar spends >5% of the time in the burst phase, that is, “duty cycle” in episodic accretion (Audard et al. 2014). However, if a significant mass is accreted onto the central source during the quiescent phase, this will reduce this value. For comparison, by modeling the luminosity distribution, Evans et al. (2009) derived a duty cycle of 7% and Dunham & Vorobyov (2012) estimate a value of 1.3%. We note that SVS13A appears to be a binary system (or perhaps triple, Lefèvre et al. 2017) and it is still unclear if the infalling streamer is feeding directly the disk around VLA4A or the circumbinary disk without higher-resolution observations.

4.3 Structure of SVS13A

The origin of the COMs in star-forming regions is not fully understood at present. These molecules are typically found to trace inner compact regions, where the temperature is higher than ~100 K. These regions are called hot corinos in low-mass star-forming regions (Ceccarelli 2004). However, COMs can also be present in the shocked gas near the centrifugal barrier (Csengeri et al. 2018) and irradiated cavity walls of outflows (Drozdovskaya et al. 2015). SVS13A contains a central binary (VLA4A and VLA4B), circumbinary disk, and a few spiral features (Fig. 1, also see Diaz-Rodriguez et al. 2022). Furthermore, the presence of infalling streamers has been suggested to produce shocked gas, enriching the chemistry (Bianchi et al. 2022b) and causing the increase of velocity dispersion (Diaz-Rodriguez et al. 2022) in VLA4A. Toward SVS13A, the close binary with the spiral structure seen in continuum emission was considered to be a fragmenting disk due to gravitational instability (Tobin et al. 2018). However, through DCN emission, we find that the spiral is connected to an infalling streamer that extends to an outer region (~700 au) of the protostellar envelope. Although the size of the CH3CN emitting region (0″.27, Table 1) is comparable to the size of VLA4A’s circumstellar disk (~0″.23 in diameter, Diaz-Rodriguez et al. 2022), the CH3CN line profile (position 1 in Fig. 6) suggests that the emission does not only come from a single component. The two-component model further supports this hypothesis; the CH3CN emission, at different velocities, originates from regions with different column densities and temperatures (or H2 density). In the two-component model, the blueshifted component dominates the line emission and has a velocity relatively close to the rotating envelope or disk surrounding VLA4A (Vlsr = 7.36 km s−1) seen via aGg’-(CH2OH)2 by Diaz-Rodriguez et al. (2022). This also corresponds to the velocity of the DCN and C18O streamer at the landing site, suggesting that the accretion from the large scale affects the circumstellar material. On the other hand, the optically thick component at the redshifted side is not present in the aGg’-(CH2OH)2 line. One possibility is that it comes from the presumed primary VLA4B (Vlsr = 9.33 km s−1). Kinematically, the central positions of the CH3CN K = 3 and K = 7 emission at different velocities also reveal a velocity structure that cannot be explained by a simple rotationally supported disk (Figs. 3 and B.1). Therefore, we suggest that CH3CN as a COM not only traces disk material, but is also affected by the large-scale infall of material provided by the streamer. This scenario is also proposed by Bianchi et al. (2022b) based on the elongated COM emission toward both VLA4A/4B. Supporting this scenario, in the case of the close Class 0 binary IRAS 16293-2422 A, the COM emission was resolved down to a scale of 10 au, and displayed several peaks outside the individual compact disks (Maureira et al. 2020). These peaks, located at about 30 au from the protostars, presumably trace shocks related to the accretion of the circumbinary material into the individual Keplerian disks (Mösta et al. 2019; Maureira et al. 2020).

We find the large-scale infalling streamer funneling material to the protostellar system. The infalling streamer seen via DCN, C18O and continuum emission lands closer to VLA4A (both spatially and spectrally), the source undergoing an accretion burst. For example, toward the binary system [BHB2007] 11A, Alves et al. (2019) find a filamentary structure that connects the central binary system to the circumbinary disk. However, at the current spatial resolution, it is unclear if the infalling streamer in SVS13A lands on the disk surrounding VLA4A or the circumbinary disk. A higher resolution observation is required to disentangle the complex structure in the central region.

5 Conclusion

We present CH3CN (12K–11K), CH313CN (12K – 11K), DCN (3–2), and C18O (2–1) observations toward SVS13A (Per-emb-44) from the NOEMA PRODIGE Large Program. Through the line emission, we study the dynamics and physical conditions of the system. Our main conclusions are summarized as follows:

  1. Through MCMC modeling, we find a low CH3CN/CH313CN ratio of 16, which is much lower than the isotope ratio value. We suggest that the simple one-velocity component assumption is affected severely by optical depth, especially in a complex structure as SVS13A. Properly measuring the column density requires data including multiple transitions with different optical depths and high spatial and spectral resolution to decompose the line emission;

  2. The CH3CN line profile is better explained by a two-component velocity model. By splitting it into two components, we find CH3CN can trace regions with dramatic differences in density and/or temperature. The kinematics show that the CH3CN emission can be affected by the infalling material from a streamer connecting the protostellar system to the outer envelope;

  3. Contrary to the previous interpretation, namely, that the spiral seen via the continuum emission is part of a gravitationally unstable disk, we find that it is in fact connected to a much larger feature. A streamer, possibly infalling, with a length of ~700 au is identified; it is partially seen through multiple tracers, including DCN, C18O, and continuum emission. Under the assumption that the streamer is infalling, it contributes a mass infalling rate onto the protostellar system of ≥1.4 × 10−6 M⊙ yr−1, ≥5% of the current protostellar mass accretion rate, 2.8 × 10−5 M⊙ yr−1. VLA4A might spend ~5% of time in the bursting phase of episodic accretion.

Our results show that the CH3CN and CH313CN emission traces hot gas in a complex structure in SVS13A. This region is connected to a streamer with a length of ~700 au. If this streamer is feeding the central protobinary system, it can contribute to a significant mass-accretion rate. We speculate that the complexity might be caused by or associated with the large-scale streamer. Observations with a higher spatial resolution are required to disentangle it.

Acknowledgements

We are grateful for the anonymous referee for the thorough and insightful comments that helped to improve this paper significantly. The authors thank Dr. Emmanuel Caux, Dr. Valerio Lattanzi, Dr. Silvia Spezzano, and Dr. Christian Endres for valuable discussion in the line analyzing using CASSIS and CDMS. T.-H.H., D.S.-C., J.E.P., P.C., M.T.V, and M.J.M. acknowledge the support by the Max Planck Society. D.S.-C. is supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-2102405. A.L.S. acknowledges support from the European Union’s Horizon 2020 research and innovation program under the Marie SkłodowskaCurie grant agreement no. 811312 for the Project “Astro-Chemical Origin” (ACO). A.F. acknowledges Spanish MICIN for funding support from PID2019-106235GB-I00. N.C. acknowledges funding from the European Research Council (ERC) via the ERC Synergy Grant ECOGAL (grant 855130), and from the French Agence Nationale de la Recherche (ANR) through the project COSMHIC (ANR-20- CE31-0009). M.T. acknowledges partial support from project PID2019-108765GB-I00 funded by MCIN/AEI/10.13039/501100011033. This work is based on observations carried out under project number L19MB with the IRAM NOEMA Interferometer. IRAM is supported by INSU/CNRS (France), MPG (Germany) and IGN (Spain). This paper makes use of the following ALMA data: ADS/JAO.ALMA#2013.1.00031.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST 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 MCMC fitting to CH3CN/CH313CN spectrum

We conducted an MCMC itting to the CH3CN and CH313CN spectrum using CASSIS (Vastel et al. 2015). The information behind the Markov chain for each model is listed in Table A.1. The same process is applied for each of these four models, and here we show the plots for the LTE one-velocity-component model as an example. Long Markov chains are taken in order to better sample the parameter space. We use trace plot and autocorrelation time to test the convergence (Figures A.1 and A.2); however, we note that these tests are only heuristic indicators of convergence (Hogg & Foreman-Mackey 2018). The ^2 and acceptance rate as functions of step are plotted with trace plots. The acceptance rate is from the number of the accepted and rejected proposals from the random walker (or Markov chain). The recommanded value is 0.2–0.5 and 0.234 for bestperformance in high-dimensional problems (Hogg & Foreman-Mackey 2018). The autocorrelation time at lags is estimated using the Python Package statsmodels6. We compare the estimated autocorrelation time τestimate with different sample sizes. The autocorrelation time indicates a sequence within which the sampling points are similar. In other words, two sampled points with a separation > τestimate are considered to be independent. We find that τestimate had become stable in larger sample sizes in our chain (Figure A.2). The trace plots and autocorrelation time also act as a guide for tuning the steps of the walkers for each parameter. The step sizes are thus tuned to be comparable to the dispersion of the sample, and keep an acceptance rate between 0.2–0.5. For each model, this procedure is repeated several times until a proper tuning is found. We checked the trace plots (as well as the χ2 derived by CASSIS) and removed the beginning of the chain (burn-in phase; 1500–15000 steps depending on models). To obtain independent points from the sample, we take one data point every τint/2 sample, i.e., thinning process; while the τint can be very different among the fitting parameters (Table A.1), here we take 1/2 of the smallest τint in order to preserve a large sample (Table 1). The physical parameters and their uncertainties are thus estimated using these thinning samples. Finally, the best it of the four models is shown in Figures A.3 to A.6. The opacities of the CH3CN K-ladder transitions for each component from the four best-it models are listed in Table A.2. The corner plot for the one velocity component model is shown in Figure A.7

Table A.1

MCMC Atting

Table A.2

Opacities of best-fit CH3CN model

thumbnail Fig. A.1

Trace plot of the MCMC fitting for the LTE 1-component model. Each panel is for one physical parameter, for which the panel “isotopologue” is the column density ratio CH3CNCH313CN${{{\rm{C}}{{\rm{H}}_3}{\rm{CN}}} \over {{\rm{C}}{{\rm{H}}_3}^{13}{\rm{CN}}}}$. The vertical dashed line represents the cut before which the data points are discarded and the histogram in the right is plotted based on the saved sample. The bottom panels show the χ2 derived by CASSIS and acceptance rate measured in every 300 steps.
thumbnail Fig. A.2

Autocorrelation time as a function of number of samples for estimation for the LTE 1-component model in the MCMC fitting. The horizontal line represents the maximal autocorrelation time (τ), and the solid and dashed lines represent the number of sample with 50 and 500 times of τ.
thumbnail Fig. A.3

Zoom-in spectra for each K-ladder component of CH3CN J=12-11. The orange curve shows the best-fit one-component LTE model. The bottom frame in each panel shows the residual from the fitting.
thumbnail Fig. A.4

Zoom-in spectra for each K-ladder component of CH3CN J=12-11. The orange curve shows the best-fit LTE model considering CH3CN and CH313CN as two species. The bottom frame in each panel shows the residual from the fitting.
thumbnail Fig. A.5

Zoom-in spectra for each K-ladder component of CH3CN J=12-11. The orange curve shows the best-fit two-component LTE model. The bottom frame in each panel shows the residual from the fitting.
thumbnail Fig. A.6

Zoom-in spectra for each K-ladder component of CH3CN J=12-11. The orange curve shows the best-fit two-component non-LTE model. The bottom frame in each panel shows the residual from the fitting.
thumbnail Fig. A.7

Corner plot of the one-component MCMC fitting. The parameters from left to right are CH3CN column density, excitation temperature, FWHM line width, source size, systemic velocity, and isotopologue ratio CH3CNCH313CN${{{\rm{C}}{{\rm{H}}_3}{\rm{CN}}} \over {{\rm{C}}{{\rm{H}}_3}^{13}{\rm{CN}}}}$.

Appendix B Kinematic models

In order to probe the origin of the CH3CN emission in SVS13A, we compared the kinematic models with the three disks from Diaz-Rodriguez et al. (2022), that is, the circumstellar disks of VLA4A, VLA4B as well as the circumbinary disk (Figure B.1). The emitting position at each velocity is obtained by Gaussian fitting in uv-space (Figure 3). Using the central position, position angle, and inclination angle of the VLA4A, VLA4B disks, and the circumbinary disk from Diaz-Rodriguez et al. (2022), we derived the de-projected radius of the CH3CN emission from the Gaussian position. Then, we plotted the measured and modeled velocities of the CH3CN emission corresponding to these three disks. Comparing the line-of-sight velocity of a Keplerian model, the CH3CN emission seems to trace part of a disk of VLA4A (see the top panel of Figure B.1). However, at least at the current spatial resolution, we find that the rotation curve cannot be simply interpreted by a simple slope for Keplerian motion, V ∝ r−0.5, or conservation of angular momentum (infalling disk), V ∝ r−1 (see Figure B.1).

thumbnail Fig. B.1

Gaussian centers of CH3CN 123 – 113 emission at different velocities from the uv-domain fitting in comparison with the line-of-sight velocity of a Keplerian disk model (V ∝ r−0.5), shown at the top. These models adopte three sets of Vlsr, PA, θinc listed in the upper-left corner from Diaz-Rodriguez et al. (2022) for VLA4A/B circumstellar disks and the circumbinary disk. Line-of-sight velocities of these points as a function of the inclination-corrected radius, shown at the bottom. The solid and dotted lines show modeled velocities at the corresponding emitting positions assuming Keplerian motion (V ∝ r−0.5) and conservation of angular momentum (V ∝ r−1), respectively. We note that the modeled curves will only shift vertically if the velocity of the model is scaled.

Appendix C Channel maps

Figure C.1 shows the channel maps from 7.22 to 10.16 km s−1. The full channel map shows that in addition to the infalling streamer, DCN actually traces more extended gas components. At low velocities ~7.4 – 8.0 km s−1, DCN traces a gas component offset from the continuum streamer, which is also seen in the C18O emission (Figure 7). At ~8.26 – 8.95 km s−1, the DCN emission is dominated by the infalling streamer. The redshifted emission >9 km s−1 is likely associated with another extended component.

The C18O blueshifted component is found to spatially match the inner part of the spiral or streamer seen in the continuum emission (Tobin et al. 2018). Figure C.2 shows the channel maps of this blueshifted component. The C18O emission at this velocity range 7.50 – 8.25 km s−1 is consistent with the velocity map of the DCN blueshifted component (Figure D.1) and spatially matches the continuum spiral well.

thumbnail Fig. C.1

DCN (3-2) channel map. The blue contour shows the ALMA continuum emission at 3σ.
thumbnail Fig. C.2

ALMA C18O channel map. The blue contour shows the ALMA continuum emission at 3σ and the white contour shows the DCN (3-2) peak intensity map at 25σ.

Appendix D Decomposition of DCN (3-2) spectra

We conducted an HFS itting in the DCN (3-2) channel map. Three different velocity components are found (Figure D.1). The fitting is firstly conducted with one, two, and three velocity components. To proceed with the fitting, we took several boundary constraints in the three components; blueshifted component: Vlsr < 8.8 km s−1 and σ < 0.6 km s−1, redshifted component: Vlsr > 8.8 km s−1 and σ < 0.6 km s−1, and broad component: σ < 2 km s−1.

For each pixel, we selected the model with one, two or three velocity components based on the Akaike information criterion (AIC), AIC=2k+χ2+C,$ AIC = 2{\rm{k + }}{\chi ^{\rm{2}}} + C, $(D.1)

where k is the degree of freedom (3, 6, and 9), χ2 is obtained from the fitting residual, and C is a constant (see Appendix E. in Choudhury et al. 2020). Comparing the AIC in each pixel, we used the model with the lowest AIC value.

thumbnail Fig. D.1

DCN (3-2) centroid-velocity maps of the three velocity components from the hyperfine structure fitting, shown at the top. The white contour shows the peak intensity above 25σ. The blue contour shows the ALMA 1.3 mm continuum emission at the 3σ level. Below, we have the spectra of DCN (3-2) with the hyperfine structure fitting toward the four positions marked in the top right panel. The three vertical lines represent the systemic velocites of VLA4A, VLA4B, and the circumbinary disk from Diaz-Rodriguez et al. (2022).

References

  1. Alves, F. O., Caselli, P., Girart, J. M., et al. 2019, Science, 366, 90 [NASA ADS] [CrossRef] [Google Scholar]
  2. Alves, F. O., Cleeves, L. I., Girart, J. M., et al. 2020, ApJ, 904, L6 [NASA ADS] [CrossRef] [Google Scholar]
  3. Anglada, G., Rodríguez, L. F., & Torrelles, J. M. 2000, ApJ, 542, L123 [NASA ADS] [CrossRef] [Google Scholar]
  4. Anglada, G., Rodríguez, L. F., Osorio, M., et al. 2004, ApJ, 605, L137 [Google Scholar]
  5. Armitage, P. J., Livio, M., & Pringle, J. E. 2001, MNRAS, 324, 705 [NASA ADS] [CrossRef] [Google Scholar]
  6. Astropy Collaboration (Robitaille, T. P., et al.) 2013, A&A, 558, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Audard, M., Ábrahám, P., Dunham, M. M., et al. 2014, Protostars and Planets VI, 387 [Google Scholar]
  8. Belloche, A., Maury, A. J., Maret, S., et al. 2020, A&A, 635, A198 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Beltrán, M. T., Cesaroni, R., Neri, R., et al. 2011, A&A, 525, A151 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Bergner, J. B., Guzmán, V. G., Öberg, K. I., et al. 2018, ApJ, 857, 69 [NASA ADS] [CrossRef] [Google Scholar]
  11. Bisschop, S. E., Jørgensen, J. K., Bourke, T. L., et al. 2008, A&A, 488, 959 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  12. Bianchi, E., Codella, C., Ceccarelli, C., et al. 2017, MNRAS, 467, 3011 [NASA ADS] [CrossRef] [Google Scholar]
  13. Bianchi, E., Codella, C., Ceccarelli, C., et al. 2019, MNRAS, 483, 1850 [Google Scholar]
  14. Bianchi, E., Ceccarelli, C., Codella, C., et al. 2022a, A&A, 662, A103 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Bianchi, E., López-Sepulcre, A., Ceccarelli, C., et al. 2022, ApJ, 928, L3 [NASA ADS] [CrossRef] [Google Scholar]
  16. Bockelée-Morvan, D., & Biver, N. 2017, Philos. Trans. Roy. Soc. Lond. A, 375, 20160252 [Google Scholar]
  17. Bottinelli, S., Ceccarelli, C., Williams, J. P., et al. 2007, A&A, 463, 601 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Brünken, S., Fuchs, U., Lewen, F., et al. 2004, J. Mol. Spectrosc., 225, 152 [CrossRef] [Google Scholar]
  19. Cabedo, V., Maury, A., Girart, J. M., et al. 2021, A&A, 653, A166 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  20. Calcutt, H., Jørgensen, J. K., Müller, H. S. P., et al. 2018, A&A, 616, A90 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Caselli, P., & Ceccarelli, C. 2012, A&AR, 20, 56 [NASA ADS] [CrossRef] [Google Scholar]
  22. Ceccarelli, C. 2004, Star Formation in the Interstellar Medium: In Honor of David Hollenbach, 323, 195 [NASA ADS] [Google Scholar]
  23. Choudhury, S., Pineda, J. E., Caselli, P., et al. 2020, A&A, 640, A6 [Google Scholar]
  24. Contreras Peña, C., Naylor, T., & Morrell, S. 2019, MNRAS, 486, 4590 [CrossRef] [Google Scholar]
  25. Codella, C., Ceccarelli, C., Bianchi, E., et al. 2016, MNRAS, 462, L75 [NASA ADS] [CrossRef] [Google Scholar]
  26. Codella, C., Bianchi, E., Podio, L., et al. 2021, A&A, 654, A52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Csengeri, T., Bontemps, S., Wyrowski, F., et al. 2018, A&A, 617, A89 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. De Simone, M., Codella, C., Testi, L., et al. 2017, A&A, 599, A121 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  29. Diaz-Rodriguez, A. K., Anglada, G., Blázquez-Calero, G., et al. 2022, ApJ, 930, 91 [NASA ADS] [CrossRef] [Google Scholar]
  30. Drozdovskaya, M. N., Walsh, C., Visser, R., et al. 2015, MNRAS, 451, 3836 [NASA ADS] [CrossRef] [Google Scholar]
  31. Dunham, M. M., & Vorobyov, E. I. 2012, ApJ, 747, 52 [NASA ADS] [CrossRef] [Google Scholar]
  32. Endres, C. P., Schlemmer, S., Schilke, P., et al. 2016, J. Mol. Spectrosc., 327, 95 [NASA ADS] [CrossRef] [Google Scholar]
  33. Evans, N. J., Dunham, M. M., Jørgensen, J. K., et al. 2009, ApJS, 181, 321 [NASA ADS] [CrossRef] [Google Scholar]
  34. Fischer, W. J., Safron, E., & Megeath, S. T. 2019, ApJ, 872, 183 [NASA ADS] [CrossRef] [Google Scholar]
  35. Garufi, A., Dominik, C., Ginski, C., et al. 2022, A&A, 658, A137 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  36. Galván-Madrid, R., Zhang, Q., Keto, E., et al. 2010, ApJ, 725, 17 [Google Scholar]
  37. Ginsburg, A., & Mirocha, J. 2011, Astrophysics Source Code Library, [ascl:1109.001] [Google Scholar]
  38. Ginski, C., Facchini, S., Huang, J., et al. 2021, ApJ, 908, L25 [NASA ADS] [CrossRef] [Google Scholar]
  39. Goldreich, P., & Kwan, J. 1974, ApJ, 189, 441 [CrossRef] [Google Scholar]
  40. Green, S. 1986, ApJ, 309, 331 [NASA ADS] [CrossRef] [Google Scholar]
  41. Harsono, D., Visser, R., Bruderer, S., et al. 2013, A&A, 555, A45 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  42. Herbst, E., & van Dishoeck, E. F. 2009, ARA&A, 47, 427 [NASA ADS] [CrossRef] [Google Scholar]
  43. Hogg, D. W., & Foreman-Mackey, D. 2018, ApJS, 236, 11 [NASA ADS] [CrossRef] [Google Scholar]
  44. Hsieh, T.-H., Murillo, N. M., Belloche, A., et al. 2018, ApJ, 854, 15 [NASA ADS] [CrossRef] [Google Scholar]
  45. Hsieh, T.-H., Murillo, N. M., Belloche, A., et al. 2019, ApJ, 884, 149 [Google Scholar]
  46. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [Google Scholar]
  47. Hunter, T. R., Brogan, C. L., Cyganowski, C. J., et al. 2014, ApJ, 788, 187 [NASA ADS] [CrossRef] [Google Scholar]
  48. Ilee, J. D., Cyganowski, C. J., Nazari, P., et al. 2016, MNRAS, 462, 4386 [NASA ADS] [CrossRef] [Google Scholar]
  49. Kristensen, L. E., van Dishoeck, E. F., Bergin, E. A., et al. 2012, A&A, 542, A8 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  50. Lefèvre, C., Cabrit, S., Maury, A. J., et al. 2017, A&A, 604, L1 [CrossRef] [EDP Sciences] [Google Scholar]
  51. Lee, K. I., Dunham, M. M., Myers, P. C., et al. 2016, ApJ, 820, L2 [NASA ADS] [CrossRef] [Google Scholar]
  52. Loomis, R. A., Cleeves, L. I., Öberg, K. I., et al. 2018, ApJ, 859, 131 [Google Scholar]
  53. Mangum, J. G., & Shirley, Y. L. 2015, PASP, 127, 266 [Google Scholar]
  54. Maureira, M. J., Pineda, J. E., Segura-Cox, D. M., et al. 2020, ApJ, 897, 59 [NASA ADS] [CrossRef] [Google Scholar]
  55. McMullin, J. P., Waters, B., Schiebel, D., et al. 2007, Astronomical Data Analysis Software and Systems XVI, 376, 127 [NASA ADS] [Google Scholar]
  56. Milam, S. N., Savage, C., Brewster, M. A., et al. 2005, ApJ, 634, 1126 [Google Scholar]
  57. Miotello, A., Bruderer, S., & van Dishoeck, E. F. 2014, A&A, 572, A96 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Möller, T., Endres, C., & Schilke, P. 2017, A&A, 598, A7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Mösta, P., Taam, R. E., & Duffell, P. C. 2019, ApJ, 875, L21 [CrossRef] [Google Scholar]
  60. Müller, H. S. P., Schlöder, F., Stutzki, J., et al. 2005, J. Mol. Struct., 742, 215 [CrossRef] [Google Scholar]
  61. Müller, H. S. P., Drouin, B. J., & Pearson, J. C. 2009, A&A, 506, 1487 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Müller, H. S. P., Brown, L. R., Drouin, B. J., et al. 2015, J. Mol. Spectrosc„ 312, 22 [CrossRef] [Google Scholar]
  63. Mumma, M. J., & Charnley, S. B. 2011, ARA&A, 49, 471 [NASA ADS] [CrossRef] [Google Scholar]
  64. Murillo, N. M., van Dishoeck, E. F., Hacar, A., et al. 2022, A&A, 658, A53 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  65. Ortiz-León, G. N., Loinard, L., Dzib, S. A., et al. 2018, ApJ, 869, L33 [Google Scholar]
  66. Park, W., Lee, J.-E., Contreras Peña, C., et al. 2021, ApJ, 920, 132 [NASA ADS] [CrossRef] [Google Scholar]
  67. Pineda, J. E., Segura-Cox, D., Caselli, P., et al. 2020, Nat. Astron., 4, 1158 [NASA ADS] [CrossRef] [Google Scholar]
  68. Pineda, J. E., Arzoumanian, D., André, P., et al. 2022 ArXiv e-prints [arXiv:2205.03935] [Google Scholar]
  69. Robitaille, T. & Bressert, E. 2012, Astrophysics Source Code Library, [ascl:1208.017] [Google Scholar]
  70. Roueff, E., Loison, J. C., & Hickson, K. M. 2015, A&A, 576, A99 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  71. Rubin, M., Altwegg, K., Balsiger, H., et al. 2019, MNRAS, 489, 594 [Google Scholar]
  72. Sargent, A. I. & Beckwith, S. 1987, ApJ, 323, 294 [NASA ADS] [CrossRef] [Google Scholar]
  73. Scholz, A., Froebrich, D., & Wood, K. 2013, MNRAS, 430, 2910 [Google Scholar]
  74. Seifried, D., & Walch, S. 2015, MNRAS, 452, 2410 [Google Scholar]
  75. Seifried, D., Banerjee, R., Pudritz, R. E., et al. 2015, MNRAS, 446, 2776 [NASA ADS] [CrossRef] [Google Scholar]
  76. Segura-Cox, D. M., Looney, L. W., Tobin, J. J., et al. 2018, ApJ, 866, 161 [Google Scholar]
  77. Shirley, Y. L. 2015, PASP, 127, 299 [Google Scholar]
  78. Smirnov-Pinchukov, G. V., Semenov, D. A., Akimkin, V. V., et al. 2020, A&A, 644, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  79. Stephens, I. W., Dunham, M. M., Myers, P. C., et al. 2018, ApJS, 237, 22 [NASA ADS] [CrossRef] [Google Scholar]
  80. Thieme, T. J., Lai, S.-P., Lin, S.-J., et al. 2022, ApJ, 925, 32 [NASA ADS] [CrossRef] [Google Scholar]
  81. Tobin, J. J., Looney, L. W., Li, Z.-Y., et al. 2016, ApJ, 818, 73 [CrossRef] [Google Scholar]
  82. Tobin, J. J., Looney, L. W., Li, Z.-Y., et al. 2018, ApJ, 867, 43 [CrossRef] [Google Scholar]
  83. Tychoniec, Ł., Tobin, J. J., Karska, A., et al. 2018, ApJS, 238, 19 [Google Scholar]
  84. Tychoniec, Ł., Manara, C. F., Rosotti, G. P., et al. 2020, A&A, 640, A19 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  85. Valdivia-Mena, M. T., Pineda, J. E., Segura-Cox, D. M., et al. 2022, A&A, 667, A12 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  86. van der Tak, F. F. S., Black, J. H., Schöier, F. L., et al. 2007, A&A, 468, 627 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  87. van der Tak, F. F. S., Lique, F., Faure, A., et al. 2020, Atoms, 8, 15 [NASA ADS] [CrossRef] [Google Scholar]
  88. Van Der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Computing in Science & Engineering, 13, 22 [NASA ADS] [CrossRef] [Google Scholar]
  89. van Dishoeck, E. F. 2014, Faraday Discuss., 168, 9 [NASA ADS] [CrossRef] [Google Scholar]
  90. Vastel, C., Bottinelli, S., Caux, E., et al. 2015, SF2A-2015: Proceedings of he Annual meeting of the French Society of Astronomy and Astrophysics, 313 [Google Scholar]
  91. Virtanen, P., Gommers, R., Oliphant, et al. (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods [Google Scholar]
  92. Vorobyov, E. I., & Basu, S. 2010, ApJ, 719, 1896 [NASA ADS] [CrossRef] [Google Scholar]
  93. Wilson, T. L., & Rood, R. 1994, ARA&A, 32, 191 [Google Scholar]
  94. Yang, Y.-L., Sakai, N., Zhang, Y., et al. 2021, ApJ, 910, 20 [Google Scholar]
  95. Yoshida, T. C., Nomura, H., Furuya, K., et al. 2022, ApJ, 932, 126 [NASA ADS] [CrossRef] [Google Scholar]
  96. Zhao, B., Caselli, R., Li, Z.-Y., et al. 2018, MNRAS, 473, 4868 [CrossRef] [Google Scholar]

All Tables

Table 1

CH3CN fitting results.

In the text
Table A.1

MCMC Atting

In the text
Table A.2

Opacities of best-fit CH3CN model

In the text

All Figures

thumbnail Fig. 1

1.3 mm continuum emission toward the SVS13 region from NOEMA (left, rms ~20 mK) and ALMA (right) observations. In the left panel, the dashed circle shows the primary beam of the NOEMA observation and the dashed box represents the FoV in the right panel. The black plus signs represent the positions of VLA4A/B from Hsieh et al. (2019). The arrow in the top right corner indicates the shift (0″.13) of the self-calibrated ALMA image.
In the text
thumbnail Fig. 2

Spectrum of CH3CN and CH313CN toward the peak of the continuum emission. The labels of the line frequencies, blue (CH3CN), orange (CH313CN), grey and pink bars, are shifted assuming a systemic velocity of 8.1 km s−1. We only marked lines with an Einstein coefficient > 10−6 s−1 and Eup < 500 K; the Einstein coefficients are 6 × 10−6 s−1 (Eup = 382 K) for the CH3OCHO line and 1 × 10−4 s−1 (Eup = 50 K) for the 33SO2 line in pink while both are very weak or undetectable based on the XCLASS model from wideband spectra including more transitions. The horizontal dashed line indicates the continuum level of 5.8 K determined from line free channels. The sensitivity per channel is 380 mK.
In the text
thumbnail Fig. 3

Integrated intensity map of CH3CN 123–113 emission (4.0–12.0 km s−1) shown to the left. The contours represent the ALMA 1.3 mm continuum emission from Fig. 1. with levels of 3σ, 5σ, 7σ, 10σ, 30σ, and 70σ. The dashed box shows the field of view in the right panel. The central panel shows the same details as the left, but for CH313CN 121–111 emission. Gaussian centers from the uυ-domain fitting shown on the right. The colored circles (CH3CN 123–113) and triangles (CH3CN 127–117) show the positions from the uv-domain fitting, with the color indicating the velocity.
In the text
thumbnail Fig. 4

Modeled CH3CN and CH313CN spectra overlaid on the observed spectra. The first panel shows the LTE model one velocity component fitting. The second panel is the same as the first panel but treats CH3CN and CH313CN as two species with independent temperature, velocity and linewidth. The third panel is the LTE two velocity component fitting, while the fourth panel shows the non-LTE one. The red curve shows the modeled spectrum. The K = 4 component of CH3CN and K = 3 component of CH313CN suffer from severe line contamination so they were not included in the fitting. The model in these frequency ranges is shown with the dashed line. The red and blue shaded areas in the two bottom panels represent the modeled spectrum from the individual components. The residual from the best-fit is shown in the bottom frame of each panel. The best-fit parameters are listed in Table 1 and the corner plots are shown in Appendix A.
In the text
thumbnail Fig. 5

Integrated intensity map of DCN (3–2) over a velocity range of 8.26–8.8 km s−1. The contours represent the ALMA 1.3 mm continuum emission with levels at 3, 5. 7, 10, 30, and 70σ. The blue and red arrows indicate the CO outflow directions from Lefèvre et al. (2017).
In the text
thumbnail Fig. 6

DCN (3–2) centroid-velocity (top-left) and linewidth (top-right) maps from the blueshifted component in the hyperfine structure fitting (see Appendix D). The white contours show its peak intensity. The contour levels start at 25σ and increase in 5σ step, where σ = 0.3 K. The blue contour shows the ALMA 1.3 mm continuum emission at the 3σ level. Spectra of DCN (3–2), C18O (2–1), and CH3CN (123–113) toward the four positions marked in the right panel (bottom). The blue bars show the relative intensities of the DCN (3–2) hyperfine structure at the velocity of the blueshifted component (see Appendix D).
In the text
thumbnail Fig. 7

C18O (2–1) channel map with the velocity range from 7.7–8.5 km s−1. The blue contour shows the ALMA continuum emission at 3σ and the white contour shows the DCN (3–2) peak intensity map at 25σ
In the text
thumbnail Fig. 8

DCN column density map. The blue contours represent the ALMA 1.3 mm dust continuum emission. The black polygon encloses the area that is used to measure the mass of the streamer.
In the text
thumbnail Fig. A.1

Trace plot of the MCMC fitting for the LTE 1-component model. Each panel is for one physical parameter, for which the panel “isotopologue” is the column density ratio CH3CNCH313CN${{{\rm{C}}{{\rm{H}}_3}{\rm{CN}}} \over {{\rm{C}}{{\rm{H}}_3}^{13}{\rm{CN}}}}$. The vertical dashed line represents the cut before which the data points are discarded and the histogram in the right is plotted based on the saved sample. The bottom panels show the χ2 derived by CASSIS and acceptance rate measured in every 300 steps.
In the text
thumbnail Fig. A.2

Autocorrelation time as a function of number of samples for estimation for the LTE 1-component model in the MCMC fitting. The horizontal line represents the maximal autocorrelation time (τ), and the solid and dashed lines represent the number of sample with 50 and 500 times of τ.
In the text
thumbnail Fig. A.3

Zoom-in spectra for each K-ladder component of CH3CN J=12-11. The orange curve shows the best-fit one-component LTE model. The bottom frame in each panel shows the residual from the fitting.
In the text
thumbnail Fig. A.4

Zoom-in spectra for each K-ladder component of CH3CN J=12-11. The orange curve shows the best-fit LTE model considering CH3CN and CH313CN as two species. The bottom frame in each panel shows the residual from the fitting.
In the text
thumbnail Fig. A.5

Zoom-in spectra for each K-ladder component of CH3CN J=12-11. The orange curve shows the best-fit two-component LTE model. The bottom frame in each panel shows the residual from the fitting.
In the text
thumbnail Fig. A.6

Zoom-in spectra for each K-ladder component of CH3CN J=12-11. The orange curve shows the best-fit two-component non-LTE model. The bottom frame in each panel shows the residual from the fitting.
In the text
thumbnail Fig. A.7

Corner plot of the one-component MCMC fitting. The parameters from left to right are CH3CN column density, excitation temperature, FWHM line width, source size, systemic velocity, and isotopologue ratio CH3CNCH313CN${{{\rm{C}}{{\rm{H}}_3}{\rm{CN}}} \over {{\rm{C}}{{\rm{H}}_3}^{13}{\rm{CN}}}}$.
In the text
thumbnail Fig. B.1

Gaussian centers of CH3CN 123 – 113 emission at different velocities from the uv-domain fitting in comparison with the line-of-sight velocity of a Keplerian disk model (V ∝ r−0.5), shown at the top. These models adopte three sets of Vlsr, PA, θinc listed in the upper-left corner from Diaz-Rodriguez et al. (2022) for VLA4A/B circumstellar disks and the circumbinary disk. Line-of-sight velocities of these points as a function of the inclination-corrected radius, shown at the bottom. The solid and dotted lines show modeled velocities at the corresponding emitting positions assuming Keplerian motion (V ∝ r−0.5) and conservation of angular momentum (V ∝ r−1), respectively. We note that the modeled curves will only shift vertically if the velocity of the model is scaled.
In the text
thumbnail Fig. C.1

DCN (3-2) channel map. The blue contour shows the ALMA continuum emission at 3σ.
In the text
thumbnail Fig. C.2

ALMA C18O channel map. The blue contour shows the ALMA continuum emission at 3σ and the white contour shows the DCN (3-2) peak intensity map at 25σ.
In the text
thumbnail Fig. D.1

DCN (3-2) centroid-velocity maps of the three velocity components from the hyperfine structure fitting, shown at the top. The white contour shows the peak intensity above 25σ. The blue contour shows the ALMA 1.3 mm continuum emission at the 3σ level. Below, we have the spectra of DCN (3-2) with the hyperfine structure fitting toward the four positions marked in the top right panel. The three vertical lines represent the systemic velocites of VLA4A, VLA4B, and the circumbinary disk from Diaz-Rodriguez et al. (2022).
In the text

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