3 posts tagged with "astrochemistry"

Authors: J. Holdship; S. Viti; S. Martín; N. Harada; J. Mangum; K. Sakamoto; S. Muller; K. Tanaka; Y. Yoshimura; K. Nakanishi; R. Herrero-Illana; S. Mühle; R. Aladro; L. Colzi; K. L. Emig; S. García-Burillo; C. Henkel; P. Humire; D. S. Meier; V. M. Rivilla; and P. van der Werf
Arxiv: https://arxiv.org/abs/2107.04580
DOI: https://doi.org/10.1051/0004-6361/202141233

Context. Observations of chemical species can provide insights into the physical conditions of the emitting gas however it is important to understand how their abundances and excitation vary within different heating environments. C2H is a molecule typically found in PDR regions of our own Galaxy but there is evidence to suggest it also traces other regions undergoing energetic processing in extragalactic environments.

Aims. As part of the ALCHEMI ALMA large program; we map the emission of C2H in the central molecular zone of the nearby starburst galaxy NGC 253 at 1.6″ (28 pc) resolution and characterize it to understand its chemical origins.

Methods. We used spectral modeling of the N = 1−0 through N = 4−3 rotational transitions of C2H to derive the C2H column densities towards the dense clouds in NGC 253. We then use chemical modeling; including photodissociation region (PDR); dense cloud; and shock models to investigate the chemical processes and physical conditions that are producing the molecular emission.

Results. We find high C2H column densities of ∼1015 cm−2 detected towards the dense regions of NGC 253. We further find that these column densities cannot be reproduced if it is assumed that the emission arises from the PDR regions at the edge of the clouds. Instead; we find that the C2H abundance remains high even in the high visual extinction interior of these clouds and that this is most likely caused by a high cosmic-ray ionization rate.

This paper also describes the spectral modelling formalism we use in SpectralRadex

Authors: Holdship, Jonathan; Viti, Serena; Haworth, Thomas; Ilee, John
Arxiv: https://arxiv.org/abs/2106.14789
DOI: https://doi.org/10.1051/0004-6361/202140357
GitHub: https://github.com/uclchem/Chemulator

Chemical modelling serves two purposes in dynamical models: accounting for the effect of microphysics on the dynamics and providing observable signatures. Ideally, the former must be done as part of the hydrodynamic simulation but this comes with a prohibitive computational cost that leads to many simplifications being used in practice. To counter this, we aimed to produce a statistical emulator that replicates a full chemical model capable of solving the temperature and abundances of a gas through time. This emulator should suffer only a minor loss of accuracy when compared to a full chemical solver and would have a fraction of the computational cost allowing it to be included in a dynamical model.

To achieve this, the gas-grain chemical code UCLCHEM was updated to include heating and cooling processes, and a large dataset of model outputs from possible starting conditions was produced. A neural network was then trained to map directly from inputs to outputs. Chemulator replicates the outputs of UCLCHEM with an overall mean squared error (MSE) of 1.7 × 10−4 for a single time step of 1000 yr, and it is shown to be stable over 1000 iterations with an MSE of 0.003 on the log-scaled temperature after one time step and 0.006 after 1000 time steps. Chemulator was found to be approximately 50 000 times faster than the time-dependent model it emulates but can introduce a significant error to some models.

Authors: Holdship, J.; Jeffrey, N.; Makrymallis, A.; Viti, S.; Yates, J.
Arxiv: https://arxiv.org/abs/1809.06245
DOI: https://doi.org/10.3847/1538-4357/aae1fa

Grain surface chemistry and its treatment in gas-grain chemical models is an area of large uncertainty. While laboratory experiments are making progress, there is still much that is unknown about grain surface chemistry. Further, the results and parameters produced by experiments are often not easily translated to the rate equation approach most commonly used in astrochemical modeling. It is possible that statistical methods can reduce the uncertainty in grain surface chemical networks. In this work, a simple model of grain surface chemistry in a molecular cloud is developed and a Bayesian inference of the reactions rates is performed through Markov Chain Monte Carlo sampling. Using observational data of the solid state abundances of major chemical species in molecular clouds, the posterior distributions for the rates of seven reactions producing CO, CO2, CH3OH, and H2O are calculated in a form that is suitable for rate equation models. This represents a vital first step in the development of a method to infer reaction rates from observations of chemical abundances in astrophysical environments.