Jonathan Holdship

We use a neural network model and Atacama Large Millimeter/submillimeter Array (ALMA) observations of HCN and HNC to constrain the physical conditions, most notably the cosmic-ray ionization rate (CRIR, ζ), 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 ∼3% 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 ζ ∼ a few ×10⁻¹⁴ s⁻¹ at r ≳ 100 pc from the nucleus, increasing to ζ > 10⁻¹³ s⁻¹ at its center. These inferred CRIRs are consistent within 1 dex with theoretical predictions based on nonthermal 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.

Molecular emission from the galactic and extragalactic interstellar medium (ISM) is often used to determine the physical conditions of the dense gas. However, even from spatially resolved regions, the observed molecules do not necessarily arise from a single component. Disentangling multiple gas components is often a degenerate problem in radiative transfer studies. In this paper, we investigate the use of the nonnegative matrix factorization (NMF) approach as a means to recover gas components from a set of blended line intensity maps of molecular transitions that may trace different physical conditions. We run a series of experiments on synthetic data sets designed to replicate conditions in two very different environments: galactic pre-stellar cores and the ISM in high-redshift galaxies. We find that the NMF algorithm often recovers the multiple components resembling those used in the data-generating process, provided that the different components have similar column densities. When NMF fails to recover all the individual components it does however group together the most similarly emitting ones. We further found that initialization and regularisation are key factors in the efficiency of the NMF algorithm.

Molecular emission from the galactic and extragalactic interstellar medium (ISM) is often used to determine the physical conditions of the dense gas. However, even from spatially resolved regions, the observed molecules are not necessarily arising from a single component. Disentangling multiple gas components is often a degenerate problem in radiative transfer studies. In this paper we investigate the use of the non-negative matrix factorization (NMF) approach as a means to recover gas components from a set of blended line intensity maps of molecular transitions which may trace different physical conditions. We run a series of experiments on synthetic datasets designed to replicate conditions in two very different environments: galactic pre-stellar cores and the ISM in high redshift galaxies. We find that the NMF algorithm often recovers the multiple components resembling those used in the data-generating process, provided that the different components have similar column densities. When NMF fails to recover all the individual components it does however group together the most similarly emitting ones. We further found that initialisation and regularisation are key factors in the efficiency of the NMF algorithm.

We analyze HCN and HNC emission in the nearby starburst galaxy NGC 253 to investigate its effectiveness in tracing heating processes associated with star formation. This study uses multiple HCN and HNC rotational transitions observed using the Atacama Large Millimeter/submillimeter Array via the ALCHEMI Large Program. To understand the conditions and associated heating mechanisms within NGC 253's dense gas, we employ Bayesian nested sampling techniques applied to chemical and radiative transfer models, which are constrained using our HCN and HNC measurements. We find that the volume density n H 2 and cosmic-ray ionization rate (CRIR) ζ are enhanced by about an order of magnitude in the galaxy’s central regions as compared to those further from the nucleus. In NGC 253's central giant molecular clouds (GMCs), where observed HCN/HNC abundance ratios are the lowest, n ∼ 10^5.5 cm⁻³ and ζ ∼ 10⁻¹² s⁻¹ (greater than 10⁴ times the average Galactic rate). We find a positive correlation in the association of both density and CRIR with the number of star formation-related heating sources (supernova remnants, H ii regions, and super hot cores) located in each GMC, as well as a correlation between CRIRs and supernova rates. Additionally, we see an anticorrelation between the HCN/HNC ratio and CRIR, indicating that this ratio will be lower in regions where ζ is higher. Though previous studies suggested HCN and HNC may reveal strong mechanical heating processes in NGC 253's CMZ, we find cosmic-ray heating dominates the heating budget, and mechanical heating does not play a significant role in the HCN and HNC chemistry.

Molecular line ratios, such as HCN(1-0)/HCO$^+$(1-0) and HCN(4-3)/CS(7-6), are routinely used to identify active galactic nuclei (AGN) activity in galaxies. Such ratios are, however, hard to interpret as they are highly dependent on the physics and energetics of the gas, and hence can seldom be used as a unique, unambiguous diagnostic. We used the composite galaxy NGC 1068 as a `laboratory' to investigate whether molecular line ratios between HCN, HCO$^+$, and CS are useful tracers of AGN-dominated gas and determine the origin of the differences in such ratios across different types of gas. Such a determination will enable a more rigorous use of such ratios. First, we empirically examined the aforementioned ratios at different angular resolutions to quantify correlations. We then used local thermodynamic equilibrium (LTE) and non-LTE analyses coupled with Markov chain Monte Carlo (MCMC) sampling in order to determine the origin of the underlying differences in ratios. We propose that at high spatial resolution (< 50 pc) the HCN(4-3)/CS(2-1) is a reliable tracer of AGN activity. We also find that the variations in ratios are not a consequence of different densities or temperature but of different fractional abundances, yielding to the important result that it is essential to consider the chemical processes at play when drawing conclusions from radiative transfer calculations. From analyses at varying spatial scales, we find that previously proposed molecular line ratios, as well as a new one, have varying levels of consistency. We also determine from an investigation of radiative transfer modelling of our data that it is essential to consider the chemistry of the species when reaching conclusions from radiative transfer calculations.

In order to understand grain-surface chemistry, one must have a good understanding of the reaction rate parameters. For diffusion-based reactions, these parameters are binding energies of the reacting species. However, attempts to estimate these values from grain-surface abundances using Bayesian inference are inhibited by a lack of enough sufficiently constraining data. In this work, we use the Massive Optimised Parameter Estimation and Data compression algorithm to determine which species should be prioritized for future ice observations to better constrain molecular binding energies. Using the results from this algorithm, we make recommendations for which species future observations should focus on.

The cosmic-ray ionization rate (CRIR) is a key parameter in understanding the physical and chemical processes in the interstellar medium. Cosmic rays are a significant source of energy in star formation regions, impacting the physical and chemical processes that drive the formation of stars. Previous studies of the circum-molecular zone of the starburst galaxy NGC 253 have found evidence for a high CRIR value: 10³–10⁶ times the average CRIR within the Milky Way. This is a broad constraint, and one goal of this study is to determine this value with much higher precision. We exploit ALMA observations toward the central molecular zone of NGC 253 to measure the CRIR. We first demonstrate that the abundance ratio of H₃O⁺ and SO is strongly sensitive to the CRIR. We then combine chemical and radiative transfer models with nested sampling to infer the gas properties and CRIR of several star-forming regions in NGC 253 from emission from their transitions. We find that each of the four regions modeled has a CRIR in the range (1–80) × 10⁻¹⁴ s⁻¹ and that this result adequately fits the abundances of other species that are believed to be sensitive to cosmic rays, including C₂H, HCO⁺, HOC⁺, and CO. From shock and photon-dominated/X-ray dominated region models, we further find that neither UV-/X-ray-driven nor shock-dominated chemistry is a viable single alternative as none of these processes can adequately fit the abundances of all of these species.