Radiative and mechanical feedback into the molecular gas in the Large Magellanic Cloud: I. N159W

We present Herschel SPIRE Fourier Transform Spectrometer (FTS) observations of N159W, an active star-forming region in the Large Magellanic Cloud (LMC). In our observations, a number of far-infrared cooling lines, including carbon monoxide (CO) J = 4 → 3 to J = 12 → 11, [CI] 609 μm and 370 μm, and [NII] 205 μm, are clearly detected. With an aim of investigating the physical conditions and excitation processes of molecular gas, we first construct CO spectral line energy distributions (SLEDs) on ~10 pc scales by combining the FTS CO transitions with ground-based low-J CO data and analyze the observed CO SLEDs using non-LTE (local thermodynamic equilibrium) radiative transfer models. We find that the CO-traced molecular gas in N159W is warm (kinetic temperature of 153-754 K) and moderately dense (H number density of (1.1-4.5) × 10 cm). To assess the impact of the energetic processes in the interstellar medium on the physical conditions of the CO-emitting gas, we then compare the observed CO line intensities with the models of photodissociation regions (PDRs) and shocks. We first constrain the properties of PDRs by modeling Herschel observations of [OI] 145 μm, [CII] 158 μm, and [CI] 370 μm fine-structure lines and find that the constrained PDR components emit very weak CO emission. X-rays and cosmic-rays are also found to provide a negligible contribution to the CO emission, essentially ruling out ionizing sources (ultraviolet photons, X-rays, and cosmic-rays) as the dominant heating source for CO in N159W. On the other hand, mechanical heating by low-velocity C-type shocks with ~10 km s appears sufficient enough to reproduce the observed warm CO.M.-Y.L. acknowledges support from the DIM ACAV of the Region Ile de France, the SYMPATICO grant (ANR-11-BS56-0023) of the French Agence Nationale de la Recherche, and the CNRS PCMI program. S.H. acknowledges financial support from DFG programme HO 5475/2-1

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

Deep learning denoising by dimension reduction: Application to the ORION-B line cubes

Context. The availability of large bandwidth receivers for millimeter radio telescopes allows the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain much information on the physical, chemical, and kinematical properties of the emitting gas. However, their large size coupled with inhomogenous signal-to-noise ratio (SNR) are major challenges for consistent analysis and interpretation.Aims. We search for a denoising method of the low SNR regions of the studied data cubes that would allow to recover the low SNR emission without distorting the signals with high SNR.Methods. We perform an in-depth data analysis of the 13 CO and C 17 O (1 -- 0) data cubes obtained as part of the ORION-B large program performed at the IRAM 30m telescope. We analyse 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 allows us to propose significant improvements of typical autoassociative neural networks, often used to denoise hyperspectral Earth remote sensing data. Applying this method to the 13 CO (1 -- 0) cube, we 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 of the SNR in voxels with weak signal, while preserving the spectral shape of the data in high SNR 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 a promising avenue. In addition

PDRs4All IV. An embarrassment of riches: Aromatic infrared bands in the Orion Bar

(Abridged) Mid-infrared observations of photodissociation regions (PDRs) are dominated by strong emission features called aromatic infrared bands (AIBs). The most prominent AIBs are found at 3.3, 6.2, 7.7, 8.6, and 11.2 $\mu$m. The most sensitive, highest-resolution infrared spectral imaging data ever taken of the prototypical PDR, the Orion Bar, have been captured by JWST. We provide an inventory of the AIBs found in the Orion Bar, along with mid-IR template spectra from five distinct regions in the Bar: the molecular PDR, the atomic PDR, and the HII region. We use JWST NIRSpec IFU and MIRI MRS observations of the Orion Bar from the JWST Early Release Science Program, PDRs4All (ID: 1288). We extract five template spectra to represent the morphology and environment of the Orion Bar PDR. The superb sensitivity and the spectral and spatial resolution of these JWST observations reveal many details of the AIB emission and enable an improved characterization of their detailed profile shapes and sub-components. While the spectra are dominated by the well-known AIBs at 3.3, 6.2, 7.7, 8.6, 11.2, and 12.7 $\mu$m, a wealth of weaker features and sub-components are present. We report trends in the widths and relative strengths of AIBs across the five template spectra. These trends yield valuable insight into the photochemical evolution of PAHs, such as the evolution responsible for the shift of 11.2 $\mu$m AIB emission from class B$_{11.2}$ in the molecular PDR to class A$_{11.2}$ in the PDR surface layers. This photochemical evolution is driven by the increased importance of FUV processing in the PDR surface layers, resulting in a "weeding out" of the weakest links of the PAH family in these layers. For now, these JWST observations are consistent with a model in which the underlying PAH family is composed of a few species: the so-called 'grandPAHs'.Comment: 25 pages, 10 figures, to appear in A&

PDRs4All III: JWST's NIR spectroscopic view of the Orion Bar

(Abridged) We investigate the impact of radiative feedback from massive stars on their natal cloud and focus on the transition from the HII region to the atomic PDR (crossing the ionisation front (IF)), and the subsequent transition to the molecular PDR (crossing the dissociation front (DF)). We use high-resolution near-IR integral field spectroscopic data from NIRSpec on JWST to observe the Orion Bar PDR as part of the PDRs4All JWST Early Release Science Program. The NIRSpec data reveal a forest of lines including, but not limited to, HeI, HI, and CI recombination lines, ionic lines, OI and NI fluorescence lines, Aromatic Infrared Bands (AIBs including aromatic CH, aliphatic CH, and their CD counterparts), CO2 ice, pure rotational and ro-vibrational lines from H2, and ro-vibrational lines HD, CO, and CH+, most of them detected for the first time towards a PDR. Their spatial distribution resolves the H and He ionisation structure in the Huygens region, gives insight into the geometry of the Bar, and confirms the large-scale stratification of PDRs. We observe numerous smaller scale structures whose typical size decreases with distance from Ori C and IR lines from CI, if solely arising from radiative recombination and cascade, reveal very high gas temperatures consistent with the hot irradiated surface of small-scale dense clumps deep inside the PDR. The H2 lines reveal multiple, prominent filaments which exhibit different characteristics. This leaves the impression of a "terraced" transition from the predominantly atomic surface region to the CO-rich molecular zone deeper in. This study showcases the discovery space created by JWST to further our understanding of the impact radiation from young stars has on their natal molecular cloud and proto-planetary disk, which touches on star- and planet formation as well as galaxy evolution.Comment: 52 pages, 30 figures, submitted to A&

PDRs4All II: JWST's NIR and MIR imaging view of the Orion Nebula

The JWST has captured the most detailed and sharpest infrared images ever taken of the inner region of the Orion Nebula, the nearest massive star formation region, and a prototypical highly irradiated dense photo-dissociation region (PDR). We investigate the fundamental interaction of far-ultraviolet photons with molecular clouds. The transitions across the ionization front (IF), dissociation front (DF), and the molecular cloud are studied at high-angular resolution. These transitions are relevant to understanding the effects of radiative feedback from massive stars and the dominant physical and chemical processes that lead to the IR emission that JWST will detect in many Galactic and extragalactic environments. Due to the proximity of the Orion Nebula and the unprecedented angular resolution of JWST, these data reveal that the molecular cloud borders are hyper structured at small angular scales of 0.1-1" (0.0002-0.002 pc or 40-400 au at 414 pc). A diverse set of features are observed such as ridges, waves, globules and photoevaporated protoplanetary disks. At the PDR atomic to molecular transition, several bright features are detected that are associated with the highly irradiated surroundings of the dense molecular condensations and embedded young star. Toward the Orion Bar PDR, a highly sculpted interface is detected with sharp edges and density increases near the IF and DF. This was predicted by previous modeling studies, but the fronts were unresolved in most tracers. A complex, structured, and folded DF surface was traced by the H2 lines. This dataset was used to revisit the commonly adopted 2D PDR structure of the Orion Bar. JWST provides us with a complete view of the PDR, all the way from the PDR edge to the substructured dense region, and this allowed us to determine, in detail, where the emission of the atomic and molecular lines, aromatic bands, and dust originate

[C II] emission from L1630 in the Orion B molecular cloud

[Context] L1630 in the Orion B molecular cloud, which includes the iconic Horsehead Nebula, illuminated by the star system σ Ori, is an example of a photodissociation region (PDR). In PDRs, stellar radiation impinges on the surface of dense material, often a molecular cloud, thereby inducing a complex network of chemical reactions and physical processes.[Aims] Observations toward L1630 allow us to study the interplay between stellar radiation and a molecular cloud under relatively benign conditions, that is, intermediate densities and an intermediate UV radiation field. Contrary to the well-studied Orion Molecular Cloud 1 (OMC1), which hosts much harsher conditions, L1630 has little star formation. Our goal is to relate the [C ii] fine-structure line emission to the physical conditions predominant in L1630 and compare it to studies of OMC1.[Methods] The [C ii] 158 μm line emission of L1630 around the Horsehead Nebula, an area of 12′ × 17′, was observed using the upgraded German Receiver for Astronomy at Terahertz Frequencies (upGREAT) onboard the Stratospheric Observatory for Infrared Astronomy (SOFIA).[Results] Of the [C ii] emission from the mapped area 95%, 13 L, originates from the molecular cloud; the adjacent H ii region contributes only 5%, that is, 1 L. From comparison with other data (CO (1-0)-line emission, far-infrared (FIR) continuum studies, emission from polycyclic aromatic hydrocarbons (PAHs)), we infer a gas density of the molecular cloud of n 3 × 10 cm, with surface layers, including the Horsehead Nebula, having a density of up to n ~ 4 × 10 cm. The temperature of the surface gas is T ~ 100 K. The average [C ii] cooling efficiency within the molecular cloud is 1.3 × 10. The fraction of the mass of the molecular cloud within the studied area that is traced by [C ii] is only 8%. Our PDR models are able to reproduce the FIR-[C ii] correlations and also the CO (1-0)-[C ii] correlations. Finally, we compare our results on the heating efficiency of the gas with theoretical studies of photoelectric heating by PAHs, clusters of PAHs, and very small grains, and find the heating efficiency to be lower than theoretically predicted, a continuation of the trend set by other observations.[Conclusions] In L1630 only a small fraction of the gas mass is traced by [C ii]. Most of the [C ii] emission in the mapped area stems from PDR surfaces. The layered edge-on structure of the molecular cloud and limitations in spatial resolution put constraints on our ability to relate different tracers to each other and to the physical conditions. From our study, we conclude that the relation between [C ii] emission and physical conditions is likely to be more complicated than often assumed. The theoretical heating efficiency is higher than the one we calculate from the observed [C ii] emission in the L1630 molecular cloud.J.R.G. and E.B. thank the ERC for funding support under grant ERC-2013-Syg-610256-NANOCOSMOS, and the Spanish MINECO under grant AYA2012-32032. J.P., F.L.P., E.R., and E.B. acknowledge support from the French program “Physique et Chimie du Milieu Interstellaire” (PCMI) funded by the Centre National de la Recherche Scientifique (CNRS) and Centre National d’Études Spatiales (CNES). Studies of the ISM at Leiden observatory are supported through the Spinoza Prize of the Dutch Science Foundation (NWO)

Réduction d’un modèle astrophysique par réseaux de neurones

National audienceNumerical models requiring significant resources in time, memory and computing power are present in many scientific disciplines. We consider an astrophysical simulation that computes many outputs from few input parameters, and that may occasionally produce anomalies. We propose a neural network based regression model reduction method and an anomaly robust learning method. We inform the choice of an architecture with a statistical analysis of the code predictions. We demonstrate the interest of the proposed approach by comparing it with model reduction methods commonly used in radio astronomy.Les modèles numériques nécessitant des ressources importantes en temps, mémoire et puissance de calcul sont présents dans de nombreuses disciplines scientifiques. Nous considérons une simulation astrophysique qui calcule de nombreuses sorties à partir de peu de paramètres d’entrée, et qui peut ponctuellement produire des anomalies. Nous proposons une méthode de réduction de modèle par régression basée sur des réseaux de neurones et une méthode d’apprentissage robuste aux anomalies. Nous informons le choix d’une architecture avec une analyse statistique des prédictions du code. Nous démontrons l’intérêt de l’approche proposée en la comparant avec les méthodes de réduction de modèle couramment utilisées en radioastronomie

Réduction d’un modèle astrophysique par réseaux de neurones

Numerical models requiring significant resources in time, memory and computing power are present in many scientific disciplines. We consider an astrophysical simulation that computes many outputs from few input parameters, and that may occasionally produce anomalies. We propose a neural network based regression model reduction method and an anomaly robust learning method. We inform the choice of an architecture with a statistical analysis of the code predictions. We demonstrate the interest of the proposed approach by comparing it with model reduction methods commonly used in radio astronomy.Les modèles numériques nécessitant des ressources importantes en temps, mémoire et puissance de calcul sont présents dans de nombreuses disciplines scientifiques. Nous considérons une simulation astrophysique qui calcule de nombreuses sorties à partir de peu de paramètres d’entrée, et qui peut ponctuellement produire des anomalies. Nous proposons une méthode de réduction de modèle par régression basée sur des réseaux de neurones et une méthode d’apprentissage robuste aux anomalies. Nous informons le choix d’une architecture avec une analyse statistique des prédictions du code. Nous démontrons l’intérêt de l’approche proposée en la comparant avec les méthodes de réduction de modèle couramment utilisées en radioastronomie

Réduction d’un modèle astrophysique par réseaux de neurones

Numerical models requiring significant resources in time, memory and computing power are present in many scientific disciplines. We consider an astrophysical simulation that computes many outputs from few input parameters, and that may occasionally produce anomalies. We propose a neural network based regression model reduction method and an anomaly robust learning method. We inform the choice of an architecture with a statistical analysis of the code predictions. We demonstrate the interest of the proposed approach by comparing it with model reduction methods commonly used in radio astronomy.Les modèles numériques nécessitant des ressources importantes en temps, mémoire et puissance de calcul sont présents dans de nombreuses disciplines scientifiques. Nous considérons une simulation astrophysique qui calcule de nombreuses sorties à partir de peu de paramètres d’entrée, et qui peut ponctuellement produire des anomalies. Nous proposons une méthode de réduction de modèle par régression basée sur des réseaux de neurones et une méthode d’apprentissage robuste aux anomalies. Nous informons le choix d’une architecture avec une analyse statistique des prédictions du code. Nous démontrons l’intérêt de l’approche proposée en la comparant avec les méthodes de réduction de modèle couramment utilisées en radioastronomie