Issue |
A&A
Volume 674, June 2023
|
|
---|---|---|
Article Number | A175 | |
Number of page(s) | 26 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202346345 | |
Published online | 20 June 2023 |
Spectral classification of young stars using conditional invertible neural networks
I. Introducing and validating the method
1
Universität Heidelberg, Zentrum für Astronomie, Institut für Theoretische Astrophysik,
Albert-Ueberle-Straße 2,
69120
Heidelberg, Germany
e-mail: kang@uni-heidelberg.de
2
Universität Heidelberg, Interdisziplinäres Zentrum für Wissenschaftliches Rechnen,
Im Neuenheimer Feld 205,
69120
Heidelberg, Germany
3
European Southern Observatory,
Karl-Schwarzschild-Str. 2,
85748
Garching bei München, Germany
4
Universitäts-Sternwarte, Ludwig-Maximilians-Universität,
Scheinerstrasse 1,
81679
München, Germany
5
Alma Mater Studiorum Università di Bologna, Dipartimento di Fisica e Astronomia (DIFA),
Via Gobetti 93/2,
40129
Bologna, Italy
6
INAF – Osservatorio Astrofisico di Arcetri,
Largo E. Fermi 5,
50125
Firenze, Italy
7
Université Paris Cité, Université Paris-Saclay, CEA, CNRS, AIM,
91191
Gif-sur-Yvette, France
8
INAF – Istituto di Astrofisica e Planetologia Spaziali,
Via Fosso del Cavaliere 100,
00133
Roma, Italy
Received:
7
March
2023
Accepted:
10
April
2023
Aims. We introduce a new deep-learning tool that estimates stellar parameters (e.g. effective temperature, surface gravity, and extinction) of young low-mass stars by coupling the Phoenix stellar atmosphere model with a conditional invertible neural network (cINN). Our networks allow us to infer the posterior distribution of each stellar parameter from the optical spectrum.
Methods. We discuss cINNs trained on three different Phoenix grids: Settl, NextGen, and Dusty. We evaluate the performance of these cINNs on unlearned Phoenix synthetic spectra and on the spectra of 36 class III template stars with well-characterised stellar parameters.
Results. We confirm that the cINNs estimate the considered stellar parameters almost perfectly when tested on unlearned Phoenix synthetic spectra. Applying our networks to class III stars, we find good agreement with deviations of 5–10% at most. The cINNs perform slightly better for earlier-type stars than for later-type stars such as late M-type stars, but we conclude that estimates of effective temperature and surface gravity are reliable for all spectral types within the training range of the network.
Conclusions. Our networks are time-efficient tools that are applicable to large numbers of observations. Among the three networks, we recommend using the cINN trained on the Settl library (Settl-Net) because it provides the best performance across the widest range of temperature and gravity.
Key words: methods: statistical / stars: late-type / stars: pre-main sequence
© The Authors 2023
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article is published in open access under the Subscribe to Open model. Subscribe to A&A to support open access publication.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.