Towards the prediction of molecular parameters from astronomical emission lines using Neural Networks
Artikel i vetenskaplig tidskrift, 2021
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
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. 52 1-2 157-182Ämneskategorier
Datorteknik
Bioinformatik (beräkningsbiologi)
Signalbehandling
DOI
10.1007/s10686-021-09786-w