Astrochemistry

Neural Network Constraints on the Cosmic-Ray Ionization Rate and Other Physical Conditions in NGC 253 with ALCHEMI Measurements of HCN and HNC

By Keith Cowing

Status Report

astro-ph.GA

October 25, 2024

Filed under ALMA, astro-ph.GA, astrochemistry, astronomy, central molecular zone, cosmic-ray, cosmic-ray ionization rate, Interstellar, Neural Network, NGC 253, organic chemistry, starburst galaxy, UCLCHEM

Neural Network Constraints on the Cosmic-Ray Ionization Rate and Other Physical Conditions in NGC 253 with ALCHEMI Measurements of HCN and HNC — CRIR map estimated from our parameter inference algorithm. All grayed out, hatched regions are estimated by our algorithm to have ζ > 5 × 10−⁻¹³ s ⁻¹ and one or more of the following issues. The ‘X’ hatching indicates regions where the estimated HCN or HNC abundance was below 10⁻¹² with respect to hydrogen, and the dotted regions indicate locations where X_HCN/X_HNC > 4, with the two appearing together when both conditions are true. White and black numbers indicate the number of transitions that are detected with S/N > 3 for a given region, and purple outlines indicate when there are no transitions above the J = 2 − 1 that meet this threshold. Gray contours signify HCN 1 − 0 emission at 1, 3, and 5 Jy km s⁻¹ beam⁻¹ . Colored dots indicate the locations of recent star formation via radio continuum sources (supernova remnants and H II regions, Ulvestad & Antonucci 1997) and super hot cores (Rico-Villas et al. 2020). Note half of the unclassified sources are expected to be H II regions and half are thought to be supernova remnants. The green circle in the bottom right corner represents the ALCHEMI 1.′′6 (∼ 28 pc) beam. — astro-ph.GA

We use a neural network model and ALMA observations of HCN and HNC to constrain the physical conditions, most notably the cosmic-ray ionization rate (CRIR, zeta), in the Central Molecular Zone (CMZ) of the starburst galaxy NGC 253.

Using output from the chemical code UCLCHEM, we train a neural network model to emulate UCLCHEM and derive HCN and HNC molecular abundances from a given set of physical conditions. We combine the neural network with radiative transfer modeling to generate modeled integrated intensities, which we compare to measurements of HCN and HNC from the ALMA Large Program ALCHEMI. Using a Bayesian nested sampling framework, we constrain the CRIR, molecular gas volume and column densities, kinetic temperature, and beam-filling factor across NGC 253’s CMZ.

The neural network model successfully recovers UCLCHEM molecular abundances with about 3 percent error and, when used with our Bayesian inference algorithm, increases the parameter inference speed tenfold. We create images of these physical parameters across NGC 253’s CMZ at 50 pc resolution and find that the CRIR, in addition to the other gas parameters, is spatially variable with zeta a few times 10^{14} s^{-1} at greater than 100 pc from the nucleus, increasing to zeta greater than 10^{-13} s^{-1} at its center.

These inferred CRIRs are consistent within 1 dex with theoretical predictions based on non-thermal emission. Additionally, the high CRIRs estimated in NGC 253’s CMZ can be explained by the large number of cosmic-ray-producing sources as well as a potential suppression of cosmic-ray diffusion near their injection sites.

Erica Behrens, Jeffrey G. Mangum, Serena Viti, Jonathan Holdship, Ko-Yun Huang, Mathilde Bouvier, Joshua Butterworth, Cosima Eibensteiner, Nanase Harada, Sergio Martin, Kazushi Sakamoto, Sebastien Muller, Kunihiko Tanaka, Laura Colzi, Christian Henkel, David S. Meier, Victor M. Rivilla, Paul P. van der Werf

Subjects: Astrophysics of Galaxies (astro-ph.GA)
Cite as: arXiv:2409.13821 [astro-ph.GA] (or arXiv:2409.13821v1 [astro-ph.GA] for this version)
https://doi.org/10.48550/arXiv.2409.13821
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Submission history
From: Erica Behrens
[v1] Fri, 20 Sep 2024 18:01:41 UTC (10,918 KB)
https://arxiv.org/abs/2409.13821
Astrobiology, Astrochemistry,

Keith Cowing

Explorers Club Fellow, ex-NASA Space Station Payload manager/space biologist, Away Teams, Journalist, Lapsed climber, Synaesthete, Na’Vi-Jedi-Freman-Buddhist-mix, ASL, Devon Island and Everest Base Camp veteran, (he/him) 🖖🏻

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