2019/07/17 by Damien de Mijolla, D. de Mijolla, S. Viti +7
Chemistry · Physics and Astronomy · #Accretion (finance) #Astrochemistry #Astrophysics and Star Formation Studies #Atmospheric radiative transfer codes #Emission spectrum #Emulation #Galaxies: Formation, Evolution, Phenomena #Interstellar medium #Line (geometry) #Radiative transfer #Spectroscopy and Laser Applications #astro-ph.GA
paper · pdf · doi:10.1051/0004-6361/201935973
published as A&A 630, A117 (2019) · Accepted by A&A; Emulator soon to be publicly released
arxiv created 2019/07/17 · openalex created_date 2019/07/23 · openalex publication_date 2019/08/05 · arxiv updated 2019/10/02 · openalex updated_date 2026/08/05
In studies of the interstellar medium in galaxies, radiative transfer models of molecular emission are useful for relating molecular line observations back to the physical conditions of the gas they trace. However, doing this requires solving a highly degenerate inverse problem. In order to alleviate these degeneracies, the abundances derived from astrochemical models can be converted into column densities and fed into radiative transfer models. This ensures that the molecular gas composition used by the radiative transfer models is chemically realistic. However, because of the complexity and long running time of astrochemical models, it can be difficult to incorporate chemical models into the radiative transfer framework. In this paper, we introduce a statistical emulator of the UCLCHEM astrochemical model, built using neural networks. We then illustrate, through examples of parameter estimations, how such an emulator can be applied to real and synthetic observations.