Testing the Physical Parameter Constraining Power of HCN and HNC with Neural Networks

TL;DR

This study evaluates how effectively different HCN and HNC molecular line transitions can constrain gas conditions in a starburst galaxy, using neural networks and Bayesian methods to optimize observational strategies.

Contribution

It introduces a neural network-based approach combined with Bayesian analysis to determine the minimal set of molecular transitions needed for accurate gas parameter estimation.

Findings

Multiple transitions are necessary for accurate parameter constraints.

Transitions with a range of upper-state energies are most effective.

Single transitions often lead to significant deviations from full data results.

Abstract

We quantify the utility of HCN and HNC to characterize gas conditions in the nearby starburst galaxy NGC 253. We use measurements from the Atacama Large Millimeter/Submillimeter Array (ALMA) Large Program ALCHEMI: the ALMA Comprehensive High-resolution Molecular Inventory. Using different subsets of the eight total HCN and HNC transitions measured by ALCHEMI, we test the number and combinations of transitions necessary for constraining the temperature, H$_2$ volume and column densities, cosmic-ray ionization rate, and beam-filling factor in three representative regions within NGC 253. We use these combinations of HCN and HNC transitions to constrain chemical and radiative transfer models and infer the gas conditions using a Bayesian nested sampling algorithm combined with neural network models for increased efficiency. By comparing the shapes of the resulting posterior distributions, as well as the medians and uncertainties for each gas parameter, from each test case to what we obtain with the full set of eight transitions (the control), we quantify how well each test reproduces the control. We find that multiple transitions each of both molecules are required to obtain a median parameter value within a factor of 2 of the control with an uncertainty less than 2-3 times that of the control. We also find that transition combinations that feature a range of upper-state energies are most effective. We show that single transitions, such as HCN J = 1-0 or 3-2, are among the worst-performing combinations and result in parameter values up to an order of magnitude different than the control.