Towards the prediction of molecular parameters from astronomical emission lines using Neural Networks
Artikel i vetenskaplig tidskrift, 2021

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.

Molecular astronomy

MADCUBA

Machine learning

Neural networks

ALCHEMI

Molecular parameters

Författare

Alejandro Barrientos

Universidad Técnica Federico Santa María

Atacama Large Millimeter-submillimeter Array (ALMA)

Jonathan Holdship

University College London (UCL)

Universiteit Leiden

Mauricio Solar

Universidad Técnica Federico Santa María

S. Martin

Universidad Técnica Federico Santa María

European Southern Observatory Santiago

Víctor M. Rivilla

Osservatorio Astrofisico di Arcetri

Centro de Astrobiologia (CAB)

Serena Viti

Universiteit Leiden

University College London (UCL)

J. G. Mangum

National Radio Astronomy Observatory

N. Harada

The Graduate University for Advanced Studies (SOKENDAI)

Academia Sinica

National Astronomical Observatory of Japan

K. Sakamoto

Academia Sinica

Sebastien Muller

Chalmers, Rymd-, geo- och miljövetenskap, Onsala rymdobservatorium, Observationssupport

Kunihiko Tanaka

Keio University

Yuki Yoshimura

University of Tokyo

Kouichiro Nakanishi

The Graduate University for Advanced Studies (SOKENDAI)

National Astronomical Observatory of Japan

R. Herrero-Illana

Institut de Ciències de l'Espai (ICE) - CSIC

European Southern Observatory Santiago

S. Muhle

Universität Bonn

Rebeca Aladro

Max-Planck-Gesellschaft

Susanne Aalto

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik

C. Henkel

Max-Planck-Gesellschaft

King Abdulaziz University

Pedro Humire

Max-Planck-Gesellschaft

Experimental Astronomy

0922-6435 (ISSN) 1572-9508 (eISSN)

Vol. In Press

Ämneskategorier

Datorteknik

Bioinformatik (beräkningsbiologi)

Signalbehandling

DOI

10.1007/s10686-021-09786-w

Mer information

Senast uppdaterat

2021-09-30