| 1. |
- Gratier, Pierre, et al.
(author)
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Quantitative inference of the H2 column densities from 3mm molecular emission: case study towards Orion B
- 2021
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 645
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Journal article (peer-reviewed)abstract
- 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 H-2 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 H-2 molecular column density from radio observations.Methods. We propose a data-driven approach to determine the H-2 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 H-2 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 H-2 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 (CO)-C-13(1-0), (CO)-C-12(1-0), (CO)-O-18(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(2(0)-1(0)) lines be added for the prediction of the H-2 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.
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| 2. |
- Hernández-Vera, C., et al.
(author)
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The extremely sharp transition between molecular and ionized gas in the Horsehead nebula
- 2023
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In: Astronomy and Astrophysics. - 0004-6361 .- 1432-0746. ; 677
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Journal article (peer-reviewed)abstract
- Massive stars can determine the evolution of molecular clouds by eroding and photo-evaporating their surfaces with strong ultraviolet (UV) radiation fields. Moreover, UV radiation is relevant in setting the thermal gas pressure in star-forming clouds, whose influence can extend across various spatial scales, from the rims of molecular clouds to entire star-forming galaxies. Probing the fundamental structure of nearby molecular clouds is therefore crucial to understand how massive stars shape their surrounding medium and how fast molecular clouds are destroyed, specifically at their UV-illuminated edges, where models predict an intermediate zone of neutral atomic gas between the molecular cloud and the surrounding ionized gas whose size is directly related to the exposed physical conditions. We present the highest angular resolution (0.≥ 5, corresponding to 207 au) and velocity-resolved images of the molecular gas emission in the Horsehead nebula, using CO J = 3- 2 and HCO+ J = 4- 3 observations with the Atacama Large Millimeter/submillimeter Array (ALMA). We find that CO and HCO+ are present at the edge of the cloud, very close to the ionization (H+/H) and dissociation fronts (H/H2), suggesting a very thin layer of neutral atomic gas (<650 au) and a small amount of CO-dark gas (AV = 0.006- 0.26 mag) for stellar UV illumination conditions typical of molecular clouds in the Milky Way. The new ALMA observations reveal a web of molecular gas filaments with an estimated thermal gas pressure of Pth = (2.3 - 4.0) - 106 K cm- 3, and the presence of a steep density gradient at the cloud edge that can be well explained by stationary isobaric photo-dissociation region (PDR) models with pressures consistent with our estimations. However, in the H≥ ¯II region and PDR interface, we find Pth,PDR > Pth,H≥ ¯II suggesting the gas is slightly compressed. Therefore, dynamical effects cannot be completely ruled out and even higher angular observations will be needed to unveil their role.
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| 3. |
- Vono, Maxime, et al.
(author)
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A Fully Bayesian Approach for Inferring Physical Properties with Credibility Intervals from Noisy Astronomical Data
- 2019
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In: 2019 10TH WORKSHOP ON HYPERSPECTRAL IMAGING AND SIGNAL PROCESSING - EVOLUTION IN REMOTE SENSING (WHISPERS). - 2158-6268. - 9781728152943
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Conference paper (peer-reviewed)abstract
- The atoms and molecules of interstellar clouds emit photons when passing from an excited state to a lower energy state. The resulting emission lines can be detected by telescopes in the different wavelength domains (radio, infrared, visible, UV...). Through the excitation and chemical conditions they reveal, these lines provide key constraints on the local physical conditions reigning in giant molecular clouds (GMCs), which constitute the birthplace of stars in galaxies. Inferring these physical conditions from observed maps of GMCs using complex astrophysical models of these regions remains a complicated challenge due to potentially degenerate solutions and widely varying signal-to-noise ratios over the map. We propose a Bayesian framework to infer the probability distributions associated to each of these physical parameters, taking a spatial smoothness prior into account to tackle the challenge of low signal-to-noise ratio regions of the observed maps. A numerical astrophysical model of the cloud is involved in the likelihood within an approximate Bayesian computation (ABC) method. This enables to both infer point-wise estimators (e.g., minimum mean square or maximum a posteriori) and quantify the uncertainty associated to the estimation process. The benefits of the proposed approach are illustrated based on noisy synthetic observation maps.
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