arXiv subjects

Jonathan Holdship

Publications and source records attributed to Jonathan Holdship.

At least 19 recordsLinked to original sources

arXiv2024

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

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.

astro-ph.GA↗

arXiv2023

Disentangling Multiple Emitting Components in Molecular Observations with Non-negative Matrix Factorization

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.

astro-ph.GA↗

arXiv2023

GMC Collisions As Triggers of Star Formation. IX. Chemical Evolution

Collisions between giant molecular clouds (GMCs) have been proposed as a mechanism to trigger massive star and star cluster formation. To investigate the astrochemical signatures of such collisions, we carry out 3D magnetohydrodynamics simulations of colliding and non-colliding clouds exposed to a variety of cosmic ray ionization rates (CRIRs), $\zeta$, following chemical evolution including gas and ice-phase components. At the GMC scale, carbon starts mostly in $\rm{C^+}$, but then transitions into C, CO, followed by ice-phase CO and $\rm{CH_3OH}$ as dense, cooler filaments, clumps and cores form from the clouds. The oxygen budget is dominated by O, CO and water ice. In dense regions, we explore the gas phase CO depletion factor, $f_D$, that measures the extent of its freeze-out onto dust grains, including dependence on CRIR and observables of mass surface density and temperature. We also identify dense clumps and analyze their physical and chemical properties, including after synthetic line emission modeling, investigating metrics used in studies of infrared dark clouds (IRDCs), especially abundances of CO, $\rm HCO^+$ and $\rm N_2H^+$. For the colliding case, we find clumps have typical densities of $n_{\rm H}\sim10^5\:{\rm{cm}}^{-3}$ and temperatures of $\sim20\:$K, while those in non-colliding GMCs are cooler. Depending on $\zeta$ and GMC dynamical history, we find CO depletion factors of up to $f_D\sim10$, and abundances of HCO$^+\sim 10^{-9}$ to $10^{-8}$ and $\rm{N_2H^+}\sim10^{-11}$ to $10^{-10}$. Comparison with observed IRDC clumps indicates a preference for low CRIRs ($\sim10^{-18}\:{\rm{s}}^{-1}$) and a more quiescent (non-colliding), cooler and evolved chemodynamical history. We discuss the general implications of our results and their caveats for interpretation of molecular cloud observations.

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arXiv2023

Investigating the impact of reactions of C and CH with molecular hydrogen on a glycine gas-grain network

The impact of including the reactions of C and CH with molecular hydrogen in a gas-grain network is assessed via a sensitivity analysis. To this end, we vary 3 parameters, namely, the efficiency for the reaction \ce{C + H2 -> CH2}, and the cosmic ray ionisation rate, with the third parameter being the final density of the collapsing dark cloud. A grid of 12 models is run to investigate the effect of all parameters on the final molecular abundances of the chemical network. We find that including reactions with molecular hydrogen alters the hydrogen economy of the network; since some species are hydrogenated by molecular hydrogen, atomic hydrogen is freed up. The abundances of simple molecules produced from hydrogenation, such as \ce{CH4}, \ce{CH3OH} and \ce{NH3}, increase, and at the same time, more complex species such as glycine and its precursors see a significant decrease in their final abundances. We find that the precursors of glycine are being preferentially hydrogenated, and therefore glycine itself is produced less efficiently.

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arXiv2022

Tracing Interstellar Heating: An ALCHEMI Measurement of the HCN Isomers in NGC 253

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 ALMA 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 $n_{\text{H}_{2}}$ 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 GMCs, where observed HCN/HNC abundance ratios are lowest, $n \sim 10^{5.5}$ cm$^{-3}$ and $ζ\sim 10^{-12}$ s$^{-1}$ (greater than $10^4$ 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, HII 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.

astro-ph.GA↗

arXiv2022

Identifying the most constraining ice observations to infer molecular binding energies

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 (MOPED) compression algorithm to determine which species should be prioritised 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.

astro-ph.GA↗

arXiv2022

Energizing Star Formation: The Cosmic Ray Ionization Rate in NGC 253 Derived From ALCHEMI Measurements of H$_3$O$^+$ and SO

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, which impacts the physical and chemical processes which drive the formation of stars. Previous studies of the circum-molecular zone (CMZ) of the starburst galaxy NGC 253 have found evidence for a high CRIR value; $10^3-10^6$ times the average cosmic ray ionization rate 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 towards the central molecular zone of NGC 253 to measure the CRIR. We first demonstrate that the abundance ratio of H$_3$O$^+$ 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 due to emission from their transitions. We find that each of the four regions modelled has a CRIR in the range $(1-80)\times10^{-14}$ s$^{-1}$ and that this result adequately fits the abundances of other species that are believed to be sensitive to cosmic rays including C$_2$H, HCO$^+$, HOC$^+$, and CO. From shock and PDR/XDR models, we further find that neither UV/X-ray driven nor shock dominated chemistry are a viable single alternative as none of these processes can adequately fit the abundances of all of these species.

astro-ph.GA↗

arXiv2022

Using Statistical Emulation and Knowledge of Grain-Surface Diffusion for Bayesian Inference of Reaction Rate Parameters: An Application to a Glycine Network

There exists much uncertainty surrounding interstellar grain-surface chemistry. One of the major reaction mechanisms is grain-surface diffusion for which the the binding energy parameter for each species needs to be known. However, these values vary significantly across the literature which can lead to debate as to whether or not a particular reaction takes place via diffusion. In this work we employ Bayesian inference to use available ice abundances to estimate the reaction rates of the reactions in a chemical network that produces glycine. Using this we estimate the binding energy of a variety of important species in the network, by assuming that the reactions take place via diffusion. We use our understanding of the diffusion mechanism to reduce the dimensionality of the inference problem from 49 to 14, by demonstrating that reactions can be separated into classes. This dimensionality reduction makes the problem computationally feasible. A neural network statistical emulator is used to also help accelerate the Bayesian inference process substantially. The binding energies of most of the diffusive species of interest are found to match some of the disparate literature values, with the exceptions of atomic and diatomic hydrogen. The discrepancies with these two species are related to limitations of the physical and chemical model. However, the use of a dummy reaction of the form H + X -> HX is found to somewhat reduce the discrepancy with the binding energy of atomic hydrogen. Using the inferred binding energies in the full gas-grain version of UCLCHEM results in almost all the molecular abundances being recovered.

astro-ph.GA↗

arXiv2022

UCLCHEMCMC: A MCMC Inference tool for Physical Parameters of Molecular Clouds

We present the publicly available, open source code UCLCHEMCMC, designed to estimate physical parameters of an observed cloud of gas by combining Monte Carlo Markov Chain (MCMC) sampling with chemical and radiative transfer modeling. When given the observed values of different emission lines, UCLCHEMCMC runs a Bayesian parameter inference, using a MCMC algorithm to sample the likelihood and produce an estimate of the posterior probability distribution of the parameters. UCLCHEMCMC takes a full forward modeling approach, generating model observables from the physical parameters via chemical and radiative transfer modeling. While running UCLCHEMCMC, the created chemical models and radiative transfer code results are stored in an SQL database, preventing redundant model calculations in future inferences. This means that the more UCLCHEMCMC is used, the more efficient it becomes. Using UCLCHEM and RADEX, the increase of efficiency is nearly two orders of magnitude, going from 5185.33 \pm 1041.96 s for ten walkers to take one thousand steps when the database is empty, to 68.89 \pm 45.39 s when nearly all models requested are in the database. In order to demonstrate its usefulness we provide an example inference of UCLCHEMCMC to estimate the physical parameters of mock data, and perform two inferences on the well studied prestellar core, L1544, one of which show that it is important to consider the substructures of an object when determining which emission lines to use.

astro-ph.GA↗

arXiv2022

History-independent tracers: Forgetful molecular probes of the physical conditions of the dense interstellar medium

Molecular line emission is a powerful probe of the physical conditions of astrophysical objects but can be complex to model, and it is often unclear which transitions would be the best targets for observers who wish to constrain a given parameter. We therefore produce a list of molecular species for which the gas history can be ignored, removing a major modelling complexity. We then determine the best of these species to observe when attempting to constrain various physical parameters. To achieve this, we use a large set of chemical models with different chemical histories to determine which species have abundances at 1 MYr that are insensitive to the initial conditions. We then use radiative transfer modelling to produce the intensity of every transition of these molecules. We finally compute the mutual information between the physical parameters and all transitions and transition ratios in order to rank their usefulness in determining the value of a given parameter. We find 48 species that are insensitive to the chemical history of the gas, 23 of which have collisional data available. We produce a ranked list of all the transitions and ratios of these species using their mutual information with various gas properties. We show mutual information is an adequate measure of how well a transition can constrain a physical parameter by recovering known probes and demonstrating that random forest regression models become more accurate predictors when high-scoring features are included. Therefore, this list can be used to select target transitions for observations in order to maximize knowledge about those physical parameters.

astro-ph.GA↗

arXiv2021

Starburst Energy Feedback Seen Through HCO$^+$/HOC$^+$ Emission in NGC 253 from ALCHEMI

Molecular abundances are sensitive to UV-photon flux and cosmic-ray ionization rate. In starburst environments, the effects of high-energy photons and particles are expected to be stronger. We examine these astrochemical signatures through multiple transitions of HCO$^+$ and its metastable isomer HOC$^+$ in the center of the starburst galaxy NGC 253 using data from the ALMA large program ALCHEMI. The distribution of the HOC$^+$(1-0) integrated intensity shows its association with "superbubbles", cavities created either by supernovae or expanding HII regions. The observed HCO$^+$/HOC$^+$ abundance ratios are $\sim 10-150$, and the fractional abundance of HOC$^+$ relative to H$_2$ is $\sim 1.5\times 10^{-11} - 6\times 10^{-10}$, which implies that the HOC$^+$ abundance in the center of NGC 253 is significantly higher than in quiescent spiral-arm dark clouds in the Galaxy and the Galactic center clouds. Comparison with chemical models implies either an interstellar radiation field of $G_0\gtrsim 10^3$ if the maximum visual extinction is $\gtrsim 5$, or a cosmic-ray ionization rate of $ζ\gtrsim 10^{-14}$ s$^{-1}$ (3-4 orders of magnitude higher than that within clouds in the Galactic spiral-arms) to reproduce the observed results. From the difference in formation routes of HOC$^+$, we propose that a low-excitation line of HOC$^+$ traces cosmic-ray dominated regions, while high-excitation lines trace photodissociation regions. Our results suggest that the interstellar medium in the center of NGC 253 is significantly affected by energy input from UV-photons and cosmic rays, sources of energy feedback.

astro-ph.GA↗

arXiv2021

On the Formation of Deuterated Methyl Formate in Hot Corinos

Methyl formate, HCOOCH$_3$, and many of its isotopologues have been detected in astrophysical regions with considerable abundances. However, the recipe for the formation of this molecule and its isotopologues is not yet known. In this work, we attempt to investigate, theoretically, the successful recipe for the formation of interstellar HCOOCH$_3$ and its deuterated isotopologues. We used the gas-grain chemical model, UCLCHEM, to examine the possible routes of formation of methyl formate on grain surfaces and in the gas-phase in low-mass star-forming regions. Our models show that radical-radical association on grains are necessary to explain the observed abundance of DCOOCH$_3$ in the protostar IRAS~16293--2422. H-D substitution reactions on grains significantly enhance the abundances of HCOOCHD$_2$, DCOOCHD$_2$, and HCOOCD$_3$. The observed abundance of HCOOCHD$_2$ in IRAS 16293--2422 can only be reproduced if H-D substitution reactions are taken into account. However, HCOOCH$_2$D remain underestimated in all of our models. The deuteration of methyl formate appears to be more complex than initially thought. Additional studies, both experimentally and theoretically, are needed for a better understanding of the interstellar formation of these species.

astro-ph.GA↗

arXiv2021

Constraining chemical networks inAstrochemistry

Databases of gas and surface chemical reactions are a key tool for scientists working in a wide range of physical sciences. In Astrochemistry, databases of chemical reactions are used as inputs to chemical models to determine the abundances of the interstellar medium. Gas chemistry and, in particular, grain surface chemistry and its treatment in gas-grain chemical models are however areas of large uncertainty. Many reactions - especially on the dust grains - have not been systematically experimentally studied. Moreover, experimental measurements are often not easily translated to the rate equation approach most commonly used in astrochemical modelling. Reducing the degree of uncertainty intrinsic in these databases is therefore a prime problem, but has so far been approached mainly by ad hoc procedures of essentially trial and error. In this chapter we review the problem of the determination of accurate and complete chemical networks in the wider context of Astrochemistry and explore the possibility of using statistical methods and machine learning (ML) techniques to reduce the uncertainty in chemical networks.

astro-ph.GA↗

arXiv2020

Exploiting Network Topology for Accelerated Bayesian Inference of Grain Surface Reaction Networks

In the study of grain-surface chemistry in the interstellar medium, there exists much uncertainty regarding the reaction mechanisms with few constraints on the abundances of grain-surface molecules. Bayesian inference can be performed to determine the likely reaction rates. In this work, we consider methods for reducing the computational expense of performing Bayesian inference on a reaction network by looking at the geometry of the network. Two methods of exploiting the topology of the reaction network are presented. One involves reducing a reaction network to just the reaction chains with constraints on them. After this, new constraints are added to the reaction network and it is shown that one can separate this new reaction network into sub-networks. The fact that networks can be separated into sub-networks is particularly important for the reaction networks of interstellar complex organic molecules, whose surface reaction networks may have hundreds of reactions. Both methods allow the maximum-posterior reaction rate to be recovered with minimal bias.

astro-ph.GA↗

arXiv2019

Tracing shock type with chemical diagnostics: an application to L1157

Aims: The physical structure of a shock wave may take a form unique to its shock type, implying that the chemistry of each shock type is unique as well. We aim to investigate the different chemistries of J-type and C-type shocks in order to identify unique molecular tracers of both shock types. We apply these diagnostics to the protostellar outflow L1157 to establish whether the B2 clump could host shocks exhibiting type-specific behaviour. Of particular interest is the L1157-B2 clump, which has been shown to exhibit bright emission in S-bearing species and HNCO. Methods: We simulate, using a parameterised approach, a planar, steady-state J-type shock wave using UCLCHEM. We compute a grid of models using both C-type and J-type shock models to determine the chemical abundance of shock-tracing species as a function of distance through the shock and apply it to the L1157 outflow. We focus on known shock-tracing molecules such as H2O, HCN, and CH3 OH. Results: We find that a range of molecules including H2O and HCN have unique behaviour specific to a J-type shock, but that such differences in behaviour are only evident at low $v_{s}$ and low $n_{H}$. We find that CH3OH is enhanced by shocks and is a reliable probe of the pre-shock gas density. However, we find no difference between its gas-phase abundance in C-type and J-type shocks. Finally, from our application to L1157, we find that the fractional abundances within the B2 region are consistent with both C-type and J-type shock emission.

astro-ph.SR↗

arXiv2019

Incorporating Astrochemistry into Molecular Line Modelling via Emulation

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 enforces that the molecular gas composition used by the radiative transfer models be 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 on real and synthetic observations.

astro-ph.GA↗

arXiv2019

Sulfur Chemistry in L1157-B1

The main carrier of sulfur in dense clouds, where it is depleted from the gas phase, remains a mystery. Shock waves in young molecular outflows disrupt the ice mantles and allow us to directly probe the material that is ejected into the gas phase. A comprehensive study of sulfur-bearing species towards L1157-B1, a shocked region along a protostellar outflow, has been carried out as part of the IRAM-30m large program ASAI. The dataset contains over 100 lines of CCS, H$_2$CS, OCS, SO, SO$_2$ and isotopologues. The results of these observations are presented, complementing previous studies of sulfur-bearing species in the region. The column densities and fractional abundances of these species are measured and together these species account for 10\% of the cosmic sulfur abundance in the region. The gas properties derived from the observations are also presented, demonstrating that sulfur bearing species trace a wide range of different gas conditions in the region.

astro-ph.GA↗

arXiv2019

Investigating the Efficiency of Explosion Chemistry as a Source of Complex Organic Molecules in TMC-1

Many species of complex organic molecules (COMs) have been observed in several astrophysical environments but it is not clear how they are produced, particularly in cold, quiescent regions. One process that has been proposed as a means to enhance the chemical complexity of the gas phase in such regions is the explosion of the ice mantles of dust grains. In this process, a build up of chemical energy in the ice is released, sublimating the ices and producing a short lived phase of high density, high temperature gas. The gas-grain chemical code UCLCHEM has been modified to treat these explosions in order to model the observed abundances of COMs towards the TMC-1 region. It is found that, based on our current understanding of the explosion mechanism and chemical pathways, the inclusion of explosions in chemical models is not warranted at this time. Explosions are not shown to improve the model's match to the observed abundances of simple species in TMC-1. Further, neither the inclusion of surface diffusion chemistry, nor explosions, results in the production of COMs with observationally inferred abundances.

astro-ph.GA↗