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.
Molecular astronomy
MADCUBA
Machine learning
Neural networks
ALCHEMI
Molecular parameters
Author
Alejandro Barrientos
Universidad Técnica Federico Santa María
Atacama Large Millimeter-submillimeter Array (ALMA)
Jonathan Holdship
University College London (UCL)
Leiden University
Mauricio Solar
Universidad Técnica Federico Santa María
S. Martin
Universidad Técnica Federico Santa María
European Southern Observatory Santiago
Víctor M. Rivilla
Arcetri Astrophysical Observatory
Centro de Astrobiologia (CAB)
Serena Viti
Leiden University
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, Space, Earth and Environment, Onsala Space Observatory
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
Institute of Space Sciences (ICE) - 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 1-2 157-182Subject Categories
Computer Engineering
Bioinformatics (Computational Biology)
Signal Processing
DOI
10.1007/s10686-021-09786-w