| 1. |
- Barrientos, Alejandro, et al.
(author)
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Towards the prediction of molecular parameters from astronomical emission lines using Neural Networks
- 2021
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In: Experimental Astronomy. - : Springer Science and Business Media LLC. - 0922-6435 .- 1572-9508. ; 52:1-2, s. 157-182
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Journal article (peer-reviewed)abstract
- Molecular astronomy is a field that is blooming in the era of large observatories such as the Atacama Large Millimeter/Submillimeter Array (ALMA). With modern, sensitive, and high spectral resolution radio telescopes like ALMA and the Square Kilometer Array, the size of the data cubes is rapidly escalating, generating a need for powerful automatic analysis tools. This work introduces MolPred, a pilot study to perform predictions of molecular parameters such as excitation temperature (Tex) and column density (log(N)) from input spectra by the use of neural networks. We used as test cases the spectra of CO, HCO+, SiO and CH3CN between 80 and 400 GHz. Training spectra were generated with MADCUBA, a state-of-the-art spectral analysis tool. Our algorithm was designed to allow the generation of predictions for multiple molecules in parallel. Using neural networks, we can predict the column density and excitation temperature of these molecules with a mean absolute error of 8.5% for CO, 4.1% for HCO+, 1.5% for SiO and 1.6% for CH3CN. The prediction accuracy depends on the noise level, line saturation, and number of transitions. We performed predictions upon real ALMA data. The values predicted by our neural network for this real data differ by 13% from the MADCUBA values on average. Current limitations of our tool include not considering linewidth, source size, multiple velocity components, and line blending.
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| 2. |
- Behrens, E., et al.
(author)
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Tracing Interstellar Heating: An ALCHEMI Measurement of the HCN Isomers in NGC 253
- 2022
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In: Astrophysical Journal. - : American Astronomical Society. - 1538-4357 .- 0004-637X. ; 939:2
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Journal article (peer-reviewed)abstract
- 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 ∼ 105.5 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.
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