Towards the prediction of molecular parameters from astronomical emission lines using Neural Networks
Journal article, 2021
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
Published in
Experimental Astronomy
0922-6435 (ISSN) 1572-9508 (eISSN)
Vol. 52 Issue 1-2 p. 157-182Categorizing
Subject Categories (SSIF 2011)
Computer Engineering
Bioinformatics (Computational Biology)
Signal Processing
Identifiers
DOI
10.1007/s10686-021-09786-w