Towards the prediction of molecular parameters from astronomical emission lines using Neural Networks
Journal article, 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.

MADCUBA

Molecular parameters

Neural networks

Machine learning

Molecular astronomy

ALCHEMI

Author

Alejandro Barrientos

Universidad Técnica Federico Santa María

Atacama Large Millimeter-submillimeter Array (ALMA)

Jonathan Holdship

Leiden University

University College London (UCL)

Mauricio Solar

Universidad Técnica Federico Santa María

S. Martin

Universidad Técnica Federico Santa María

European Southern Observatory Santiago

Víctor M. Rivilla

Centro de Astrobiologia (CAB)

Arcetri Astrophysical Observatory

Serena Viti

University College London (UCL)

Leiden University

J. G. Mangum

National Radio Astronomy Observatory

N. Harada

National Astronomical Observatory of Japan

The Graduate University for Advanced Studies (SOKENDAI)

Academia Sinica

K. Sakamoto

Academia Sinica

Sebastien Muller

Chalmers, Space, Earth and Environment, Onsala Space Observatory

Kunihiko Tanaka

Keio University

Yuki Yoshimura

University of Tokyo

Kouichiro Nakanishi

National Astronomical Observatory of Japan

The Graduate University for Advanced Studies (SOKENDAI)

R. Herrero-Illana

Spanish National Research Council (CSIC)

European Southern Observatory Santiago

S. Muhle

University of Bonn

Rebeca Aladro

Max Planck Society

Susanne Aalto

Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics

C. Henkel

Max Planck Society

King Abdulaziz University

Pedro Humire

Max Planck Society

Experimental Astronomy

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

Vol. 52 1-2 157-182

Subject Categories (SSIF 2011)

Computer Engineering

Bioinformatics (Computational Biology)

Signal Processing

DOI

10.1007/s10686-021-09786-w

More information

Latest update

3/9/2025 1