Browsing by Author "Orkisz, Jan H."

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    Bias versus variance when fitting multi-species molecular lines with a non-LTE radiative transfer model Application to the estimation of the gas temperature and volume density
    (2024) Roueff, Antoine; Pety, Jerome; Gerin, Maryvonne; Segal, Leontine E.; Goicoechea, Javier R.; Liszt, Harvey S.; Gratier, Pierre; Beslic, Ivana; Einig, Lucas; Gaudel, Mathilde; Orkisz, Jan H.; Palud, Pierre; Santa-Maria, Miriam G.; Magalhaes, Victor de Souza; Zakardjian, Antoine; Bardeau, Sebastien; Bron, Emeric; Chainais, Pierre; Coude, Simon; Demyk, Karine; Guzman, Viviana V.; Hughes, Annie; Languignon, David; Levrier, Francois; Lis, Dariusz C.; Le Bourlot, Jacques; Le Petit, Franck; Peretto, Nicolas; Roueff, Evelyne; Sievers, Albrecht; Thouvenin, Pierre-Antoine
    Context. Robust radiative transfer techniques are requisite for efficiently extracting the physical and chemical information from molecular rotational lines. Aims. 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. Methods. 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 Cramer-Rao lower bound. Results. 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 (CO)-C-12 (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. Conclusions. 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.
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    Deep learning denoising by dimension reduction: Application to the ORION-B line cubes
    (2023) Einig, Lucas; Pety, Jerome; Roueff, Antoine; Vandame, Paul; Chanussot, Jocelyn; Gerin, Maryvonne; Orkisz, Jan H.; Palud, Pierre; Santa-Maria, Miriam G.; Magalhaes, Victor de Souza; Beslic, Ivana; Bardeau, Sebastien; Bron, Emeric; Chainais, Pierre; Goicoechea, Javier R.; Gratier, Pierre; Guzman, Viviana V.; Hughes, Annie; Kainulainen, Jouni; Languignon, David; Lallement, Rosine; Levrier, Francois; Lis, Dariusz C.; Liszt, Harvey S.; Le Bourlot, Jacques; Le Petit, Franck; Oberg, Karin; Peretto, Nicolas; Roueff, Evelyne; Sievers, Albrecht; Thouvenin, Pierre-Antoine; Tremblin, Pascal
    Context. The availability of large bandwidth receivers for millimeter radio telescopes allows for the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain a lot of information on the physical, chemical, and kinematical properties of the emitting gas. However, their large size coupled with an inhomogenous signal-to-noise ratio (S/N) are major challenges for consistent analysis and interpretation.Aims. We searched for a denoising method of the low S/N regions of the studied data cubes that would allow the low S/N emission to be recovered without distorting the signals with a high S/N.Methods. We performed an in-depth data analysis of the (CO)-C-13 and (CO)-O-17 (1-0) data cubes obtained as part of the ORION-B large program performed at the IRAM 30 m telescope. We analyzed the statistical properties of the noise and the evolution of the correlation of the signal in a given frequency channel with that of the adjacent channels. This has allowed us to propose significant improvements of typical autoassociative neural networks, often used to denoise hyperspectral Earth remote sensing data. Applying this method to the (CO)-C-13 (1-0) cube, we were able to compare the denoised data with those derived with the multiple Gaussian fitting algorithm ROHSA, considered as the state-of-the-art procedure for data line cubes.Results. The nature of astronomical spectral data cubes is distinct from that of the hyperspectral data usually studied in the Earth remote sensing literature because the observed intensities become statistically independent beyond a short channel separation. This lack of redundancy in data has led us to adapt the method, notably by taking into account the sparsity of the signal along the spectral axis. The application of the proposed algorithm leads to an increase in the S/N in voxels with a weak signal, while preserving the spectral shape of the data in high S/N voxels.Conclusions. The proposed algorithm that combines a detailed analysis of the noise statistics with an innovative autoencoder architecture is a promising path to denoise radio-astronomy line data cubes. In the future, exploring whether a better use of the spatial correlations of the noise may further improve the denoising performances seems to be a promising avenue. In addition, dealing with the multiplicative noise associated with the calibration uncertainty at high S/N would also be beneficial for such large data cubes.
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    Gas kinematics around filamentary structures in the Orion B cloud
    (2023) Gaudel, Mathilde; Orkisz, Jan H.; Gerin, Maryvonne; Pety, Jerome; Roueff, Antoine; Marchal, Antoine; Levrier, Francois; Miville-Deschenes, Marc-Antoine; Goicoechea, Javier R.; Roueff, Evelyne; Le Petit, Franck; Magalhaes, Victor de Souza; Palud, Pierre; Santa-Maria, Miriam G.; Vono, Maxime; Bardeau, Sebastien; Bron, Emeric; Chainais, Pierre; Chanussot, Jocelyn; Gratier, Pierre; Guzman, Viviana; Hughes, Annie; Kainulainen, Jouni; Languignon, David; Le Bourlot, Jacques; Liszt, Harvey; Oberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal
    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 <= A(V) < 6 mag) and moderately dense gas (6 <= A(V) < 15 mag), which we used to analyze the kinematics over a field of 5 deg(2) around the filamentary structures.Methods. We used the Regularized Optimization for Hyper-Spectral Analysis (ROHSA) algorithm to decompose and de-noise the (CO)-O-18(1-0) and (CO)-C-13(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.
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    Neural network-based emulation of interstellar medium models
    (2023) Palud, Pierre; Einig, Lucas; Le Petit, Franck; Bron, Emeric; Chainais, Pierre; Chanussot, Jocelyn; Pety, Jerome; Thouvenin, Pierre-Antoine; Languignon, David; Beslic, Ivana; Santa-Maria, Miriam G.; Orkisz, Jan H.; Segal, Leontine E.; Zakardjian, Antoine; Bardeau, Sebastien; Gerin, Maryvonne; Goicoechea, Javier R.; Gratier, Pierre; Guzman, Viviana V.; Hughes, Annie; Levrier, Francois; Liszt, Harvey S.; Le Bourlot, Jacques; Roueff, Antoine; Sievers, Albrecht
    Context. 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 timeconsuming 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.Aims. We propose a new general method to derive faster, lighter, and more accurate approximations of the model from a grid of precomputed models for use in inference procedures.Methods. These emulators are defined with artificial neural networks (ANNs) with adapted architectures and are fitted using regression strategies instead of interpolation methods. The specificities inherent in ISM models need to be addressed to design and train adequate ANNs. Indeed, such models often predict numerous observables (e.g., line intensities) from just a few input physical parameters and can yield outliers due to numerical instabilities or physical bistabilities and multistabilities. 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 us 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 simpler relations between line intensities and physical parameters.Results. We compare the proposed ANNs with four standard classes of interpolation methods, nearest-neighbor, linear, spline, and radial basis function (RBF), to emulate a representative ISM numerical model known as the Meudon PDR code. Combinations of the proposed strategies produce networks that outperform all interpolation methods in terms of accuracy by a factor of 2 in terms of the average error (reaching 4.5% on the Meudon PDR code) and a factor of 3 for the worst-case errors (33%). These networks are also 1000 times faster than accurate interpolation methods and require ten to forty times less memory.Conclusions. This work will enable efficient inferences on wide-field multiline observations of the ISM.
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    Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds: I. Application to model predictions
    (2024) Einig, Lucas; Palud, Pierre; Roueff, Antoine; Pety, Jerome; Bron, Emeric; Le Petit, Franck; Gerin, Maryvonne; Chanussot, Jocelyn; Chainais, Pierre; Thouvenin, Pierre-Antoine; Languignon, David; Beslic, Ivana; Coude, Simon; Mazurek, Helena; Orkisz, Jan H.; Santa-Maria, Miriam G.; Segal, Leontine; Zakardjian, Antoine; Bardeau, Sebastien; Demyk, Karine; de Souza Magalhaes, Victor; Goicoechea, Javier R.; Gratier, Pierre; Guzman, Viviana V.; Hughes, Annie; Levrier, Francois; Le Bourlot, Jacques; Lis, Dariusz C.; Liszt, Harvey S.; Peretto, Nicolas; Roueff, Evelyne; Sievers, Albrecht
    Context. Observations of ionic, atomic, or molecular lines are performed to improve our understanding of the interstellar medium (ISM). However, the potential of a line to constrain the physical conditions of the ISM is difficult to assess quantitatively, because of the complexity of the ISM physics. The situation is even more complex when trying to assess which combinations of lines are the most useful. Therefore, observation campaigns usually try to observe as many lines as possible for as much time as possible. Aims. We have searched for a quantitative statistical criterion to evaluate the full constraining power of a (combination of) tracer(s) with respect to physical conditions. Our goal with such a criterion is twofold. First, we want to improve our understanding of the statistical relationships between ISM tracers and physical conditions. Secondly, by exploiting this criterion, we aim to propose a method that helps observers to make their observation proposals; for example, by choosing to observe the lines with the highest constraining power given limited resources and time. Methods. We propose an approach based on information theory, in particular the concepts of conditional differential entropy and mutual information. The best (combination of) tracer(s) is obtained by comparing the mutual information between a physical parameter and different sets of lines. The presented analysis is independent of the choice of the estimation algorithm (e.g., neural network or chi(2) minimization). We applied this method to simulations of radio molecular lines emitted by a photodissociation region similar to the Horsehead Nebula. In this simulated data, we considered the noise properties of a state-of-the-art single dish telescope such as the IRAM 30m telescope. We searched for the best lines to constrain the visual extinction, A(V)(tot), or the ultraviolet illumination field, G(0). We ran this search for different gas regimes, namely translucent gas, filamentary gas, and dense cores. Results. The most informative lines change with the physical regime (e.g., cloud extinction). However, the determination of the optimal (combination of) line(s) to constrain a physical parameter such as the visual extinction depends not only on the radiative transfer of the lines and chemistry of the associated species, but also on the achieved mean signal-to-noise ratio. The short integration time of the CO isotopologue J = 1 - 0 lines already yields much information on the total column density for a large range of (A(V)(tot), G(0)) space. The best set of lines to constrain the visual extinction does not necessarily combine the most informative individual lines. Precise constraints on the radiation field are more difficult to achieve with molecular lines. They require spectral lines emitted at the cloud surface (e.g., [CII] and [CI] lines). Conclusions. This approach allows one to better explore the knowledge provided by ISM codes, and to guide future observation campaigns.