Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds. I. Application to model predictions
arXiv:2408.08114 · doi:10.1051/0004-6361/202451588
Abstract
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. We search for a quantitative statistical criterion to evaluate the constraining power of a (or combination of) tracer(s) with respect to physical conditions in order to improve our understanding of the statistical relationships between ISM tracers and physical conditions and helps observers to motivate their observation proposals. The best tracers are obtained by comparing the mutual information between a physical parameter and different sets of lines. We apply this method to simulations of radio molecular lines emitted by a photodissociation region similar to the Horsehead Nebula that would be observed at the IRAM 30m telescope. We search for the best lines to constrain the visual extinction or the far UV illumination . The most informative lines change with the physical regime (e.g., cloud extinction). Short integration time of the CO isotopologue lines already yields much information on the total column density most regimes. The best set of lines to constrain the visual extinction does not necessarily combine the most informative individual lines. Precise constraints on are more difficult to achieve with molecular lines. They require spectral lines emitted at the cloud surface (e.g., [CII] and [CI] lines). This approach allows one to better explore the knowledge provided by ISM codes, and to guide future observation campaigns.
References in corpus (19)
- Power-law distributions in empirical data
- The Herschel Dwarf Galaxy Survey: I. Properties of the low-metallicity ISM from PACS spectroscopy
- The anatomy of the Orion B Giant Molecular Cloud: A local template for studies of nearby galaxies
- IZI: Inferring the Gas Phase Metallicity (Z) and Ionization Parameter (q) of Ionized Nebulae using Bayesian Statistics
- The upGREAT 1.9 THz multi-pixel high resolution spectrometer for the SOFIA Observatory
- The penetration of FUV radiation into molecular clouds
- Dissecting the molecular structure of the Orion B cloud: Insight from Principal Component Analysis
- [CII] emission from L1630 in the Orion B molecular cloud
- Tracing Interstellar Heating: An ALCHEMI Measurement of the HCN Isomers in NGC 253
- How much a galaxy knows about its large-scale environment?: An information theoretic perspective
- Reducing the complexity of chemical networks via interpretable autoencoders
- Inferring the HII region escape fraction of ionizing photons from infrared emission lines in metal-poor star-forming dwarf galaxies
- Chemulator: Fast, accurate thermochemistry for dynamical models through emulation
- A Near-infrared Survey of UV-excited Molecular Hydrogen in Photodissociation Regions
- Tracers of the ionization fraction in dense and translucent gas: I. Automated exploitation of massive astrochemical model grids
- Quantitative inference of the column densities from 3 mm molecular emission: A case study towards Orion B
- Deep learning denoising by dimension reduction: Application to the ORION-B line cubes
- Machine learning-accelerated chemistry modeling of protoplanetary disks
- Neural network-based emulation of interstellar medium models
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- Identification of molecular line emission using Convolutional Neural Networks
- Estimating the dense gas mass of molecular clouds using spatially unresolved 3 mm line observations