Barrientos, A;
Holdship, J;
Solar, M;
Martin, S;
Rivilla, VM;
Viti, S;
Mangum, J;
... Humire, P; + view all
(2021)
Towards the prediction of molecular parameters from astronomical emission lines using Neural Networks.
Experimental Astronomy
, 52
(1-2)
pp. 157-182.
10.1007/s10686-021-09786-w.
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2021-05-14_[INITIALSUBMISSION]_Towards_the_prediction_of_molecular_parameters_from_astronomical_emission_lines__using_Neural_Networks.pdf - Accepted Version Download (1MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Towards the prediction of molecular parameters from astronomical emission lines using Neural Networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10686-021-09786-w |
| Publisher version: | https://doi.org/10.1007/s10686-021-09786-w |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Physical Sciences, Astronomy & Astrophysics, Molecular astronomy, Molecular parameters, Machine learning, Neural networks, MADCUBA, ALCHEMI |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10138751 |
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