Towards the prediction of molecular parameters from astronomical emission lines using Neural Networks
Journal article, 2021
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