A Fast Neural Emulator For Interstellar Chemistry
Astrochemical models are important tools to interpret observations of molecular and atomic species in different environments. However, these models are time-consuming, precluding a thorough exploration of the parameter space, leading to uncertainties and biased results. Using neural networks to simulate the behavior of astrochemical models is a way to circumvent this problem, providing fast calculations that are based on real astrochemical models.
In this paper, we present a fast neural emulator of the astrochemical code Nautilus based on conditional neural fields. The resulting model produces the abundance of 192 species for arbitrary times between 1 and 107 years. Uncertainties well below 0.2 dex are found for all species, while the computing time is of the order of 104 smaller than Nautilus.
This will open up the possibility of performing much more complex forward models to better understand the physical properties of the interstellar medium. As an example of the power of these models, we ran a feature importance analysis on the electron abundance predicted by Nautilus.
We found that the electron density is coupled to the initial sulphur abundance in a low density gas. Increasing the initial sulphur abundance from a depleted scenario to the cosmic abundance leads to an enhancement of an order of magnitude of the electron density. This enhancement can potentially influence the dynamics of the gas in star formation sites.
A. Asensio Ramos, C. Westendorp Plaza, D. Navarro-Almaida, P. Rivière-Marichalar, V. Wakelam, A. Fuente
Comments: 12 pages, 8 figures, accepted for publication in MNRAS
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Astrophysics of Galaxies (astro-ph.GA)
Cite as: arXiv:2406.02387 [astro-ph.IM] (or arXiv:2406.02387v1 [astro-ph.IM] for this version)
https://doi.org/10.48550/arXiv.2406.02387
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Submission history
From: Andres Asensio Ramos
[v1] Tue, 4 Jun 2024 15:01:20 UTC (8,750 KB)
https://arxiv.org/abs/2406.02387
Astrobiology
