Chemical complexity in the Horsehead photodissociation region

The interstellar medium is known to be chemically complex. Organic molecules with up to 11 atoms have been detected in the interstellar medium, and are believed to be formed on the ices around dust grains. The ices can be released into the gas-phase either through thermal desorption, when a newly formed star heats the medium around it and completely evaporates the ices; or through non-thermal desorption mechanisms, such as photodesorption, when a single far-UV photon releases only a few molecules from the ices. The first one dominates in hot cores, hot corinos and strongly UV-illuminated PDRs, while the second one dominates in colder regions, such as low UV-field PDRs. This is the case of the Horsehead were dust temperatures are ~20-30K, and therefore offers a clean environment to investigate what is the role of photodesorption. We have carried-out an unbiased spectral line survey at 3, 2 and 1mm with the IRAM-30m telescope in the Horsehead nebula, with an unprecedented combination of bandwidth high spectral resolution and sensitivity. Two positions were observed: the warm PDR and a cold condensation shielded from the UV field (dense core), located just behind the PDR edge. We summarize our recently published results from this survey and present the first detection of the complex organic molecules HCOOH, CH2CO, CH3CHO and CH3CCH in a PDR. These species together with CH3CN present enhanced abundances in the PDR compared to the dense core. This suggests that photodesorption is an efficient mechanism to release complex molecules into the gas-phase in far-UV illuminated regions.Comment: 15 pages, 7 figures, 7 tables, Accepted in Faraday discussions 16

Herschel observations of interstellar chloronium

Using the Herschel Space Observatory's Heterodyne Instrument for the Far-Infrared (HIFI), we have observed para-chloronium (H2Cl+) toward six sources in the Galaxy. We detected interstellar chloronium absorption in foreground molecular clouds along the sight-lines to the bright submillimeter continuum sources Sgr A (+50 km/s cloud) and W31C. Both the para-H2-35Cl+ and para-H2-37Cl+ isotopologues were detected, through observations of their 1(11)-0(00) transitions at rest frequencies of 485.42 and 484.23 GHz, respectively. For an assumed ortho-to-para ratio of 3, the observed optical depths imply that chloronium accounts for ~ 4 - 12% of chlorine nuclei in the gas phase. We detected interstellar chloronium emission from two sources in the Orion Molecular Cloud 1: the Orion Bar photodissociation region and the Orion South condensation. For an assumed ortho-to-para ratio of 3 for chloronium, the observed emission line fluxes imply total beam-averaged column densities of ~ 2.0E+13 cm-2 and ~ 1.2E+13 cm-2, respectively, for chloronium in these two sources. We obtained upper limits on the para-H2-35Cl+ line strengths toward H2 Peak 1 in the Orion Molecular cloud and toward the massive young star AFGL 2591. The chloronium abundances inferred in this study are typically at least a factor ~10 larger than the predictions of steady-state theoretical models for the chemistry of interstellar molecules containing chlorine. Several explanations for this discrepancy were investigated, but none has proven satisfactory, and thus the large observed abundances of chloronium remain puzzling.Comment: Accepted for publication in the Astrophysical Journa

Neural network-based emulation of interstellar medium models

The interpretation of observations of atomic and molecular tracers in the galactic and extragalactic interstellar medium (ISM) requires comparisons with state-of-the-art astrophysical models to infer some physical conditions. Usually, ISM models are too time-consuming for such inference procedures, as they call for numerous model evaluations. As a result, they are often replaced by an interpolation of a grid of precomputed models. We propose a new general method to derive faster, lighter, and more accurate approximations of the model from a grid of precomputed models. These emulators are defined with artificial neural networks (ANNs) designed and trained to address the specificities inherent in ISM models. Indeed, such models often predict many observables (e.g., line intensities) from just a few input physical parameters and can yield outliers due to numerical instabilities or physical bistabilities. We propose applying five strategies to address these characteristics: 1) an outlier removal procedure; 2) a clustering method that yields homogeneous subsets of lines that are simpler to predict with different ANNs; 3) a dimension reduction technique that enables to adequately size the network architecture; 4) the physical inputs are augmented with a polynomial transform to ease the learning of nonlinearities; and 5) a dense architecture to ease the learning of simple relations. We compare the proposed ANNs with standard classes of interpolation methods to emulate the Meudon PDR code, a representative ISM numerical model. Combinations of the proposed strategies outperform all interpolation methods by a factor of 2 on the average error, reaching 4.5% on the Meudon PDR code. These networks are also 1000 times faster than accurate interpolation methods and require ten to forty times less memory. This work will enable efficient inferences on wide-field multiline observations of the ISM

Bias versus variance when fitting multi-species molecular lines with a non-LTE radiative transfer model

Robust radiative transfer techniques are requisite for efficiently extracting the physical and chemical information from molecular rotational lines.We study several hypotheses that enable robust estimations of the column densities and physical conditions when fitting one or two transitions per molecular species. We study the extent to which simplifying assumptions aimed at reducing the complexity of the problem introduce estimation biases and how to detect them.We focus on the CO and HCO+ isotopologues and analyze maps of a 50 square arcminutes field. We used the RADEX escape probability model to solve the statistical equilibrium equations and compute the emerging line profiles, assuming that all species coexist. Depending on the considered set of species, we also fixed the abundance ratio between some species and explored different values. We proposed a maximum likelihood estimator to infer the physical conditions and considered the effect of both the thermal noise and calibration uncertainty. We analyzed any potential biases induced by model misspecifications by comparing the results on the actual data for several sets of species and confirmed with Monte Carlo simulations. The variance of the estimations and the efficiency of the estimator were studied based on the Cram{\'e}r-Rao lower bound.Column densities can be estimated with 30% accuracy, while the best estimations of the volume density are found to be within a factor of two. Under the chosen model framework, the peak 12CO(1--0) is useful for constraining the kinetic temperature. The thermal pressure is better and more robustly estimated than the volume density and kinetic temperature separately. Analyzing CO and HCO+ isotopologues and fitting the full line profile are recommended practices with respect to detecting possible biases.Combining a non-local thermodynamic equilibrium model with a rigorous analysis of the accuracy allows us to obtain an efficient estimator and identify where the model is misspecified. We note that other combinations of molecular lines could be studied in the future.Comment: Astronomy and Astrophysics - A\&A, In pres

Gas kinematics around filamentary structures in the Orion B cloud

Context. Understanding the initial properties of star-forming material and how they affect the star formation process is key. From an observational point of view, the feedback from young high-mass stars on future star formation properties is still poorly constrained. Aims. In the framework of the IRAM 30m ORION-B large program, we obtained observations of the translucent (2 ≤ AV < 6 mag) and moderately dense gas (6 ≤ AV < 15 mag), which we used to analyze the kinematics over a field of 5 deg2 around the filamentary structures. Methods. We used the Regularized Optimization for Hyper-Spectral Analysis (ROHSA) algorithm to decompose and de-noise the C 18 O(1−0) and 13CO(1−0) signals by taking the spatial coherence of the emission into account. We produced gas column density and mean velocity maps to estimate the relative orientation of their spatial gradients. Results. We identified three cloud velocity layers at different systemic velocities and extracted the filaments in each velocity layer. The filaments are preferentially located in regions of low centroid velocity gradients. By comparing the relative orientation between the column density and velocity gradients of each layer from the ORION-B observations and synthetic observations from 3D kinematic toy models, we distinguish two types of behavior in the dynamics around filaments: (i) radial flows perpendicular to the filament axis that can be either inflows (increasing the filament mass) or outflows and (ii) longitudinal flows along the filament axis. The former case is seen in the Orion B data, while the latter is not identified. We have also identified asymmetrical flow patterns, usually associated with filaments located at the edge of an H II region. Conclusions. This is the first observational study to highlight feedback from H II regions on filament formation and, thus, on star formation in the Orion B cloud. This simple statistical method can be used for any molecular cloud to obtain coherent information on the kinematics

Quantitative inference of the H2 column densities from 3mm molecular emission: a case study towards Orion B

Context. Based on the finding that molecular hydrogen is unobservable in cold molecular clouds, the column density measurements of molecular gas currently rely either on dust emission observation in the far-infrared, which requires space telescopes, or on star counting, which is limited in angular resolution by the stellar density. The (sub)millimeter observations of numerous trace molecules can be effective using ground-based telescopes, but the relationship between the emission of one molecular line and the H2 column density is non-linear and sensitive to excitation conditions, optical depths, and abundance variations due to the underlying physico- chemistry. Aims. We aim to use multi-molecule line emission to infer the H2 molecular column density from radio observations. Methods. We propose a data-driven approach to determine the H2 gas column densities from radio molecular line observations. We use supervised machine-learning methods (random forest) on wide-field hyperspectral IRAM-30m observations of the Orion B molecular cloud to train a predictor of the H2 column density, using a limited set of molecular lines between 72 and 116 GHz as input, and the Herschel-based dust-derived column densities as “ground truth” output. Results. For conditions similar to those of the Orion B molecular cloud, we obtained predictions of the H2 column density within a typical factor of 1.2 from the Herschel-based column density estimates. A global analysis of the contributions of the different lines to the predictions show that the most important lines are 13CO(1–0), 12CO(1–0), C18O(1–0), and HCO+(1–0). A detailed analysis distinguishing between diffuse, translucent, filamentary, and dense core conditions show that the importance of these four lines depends on the regime, and that it is recommended that the N2H+(1–0) and CH3OH(20–10) lines be added for the prediction of the H2 column density in dense core conditions. Conclusions. This article opens a promising avenue for advancing direct inferencing of important physical parameters from the molecular line emission in the millimeter domain. The next step will be to attempt to infer several parameters simultaneously (e.g., the column density and far-UV illumination field) to further test the method