Issue |
A&A
Volume 690, October 2024
|
|
---|---|---|
Article Number | A198 | |
Number of page(s) | 18 | |
Section | Extragalactic astronomy | |
DOI | https://doi.org/10.1051/0004-6361/202449979 | |
Published online | 10 October 2024 |
Retrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning
1
INAF-Osservatorio Astronomico di Brera, via Brera 28, 20121 Milano, Italy
2
INAF – Osservatorio Astronomico di Capodimonte, Via Moiariello 16, 80131 Napoli, Italy
3
Università degli studi di Milano-Bicocca, Piazza della scienza, 20125 Milano, Italy
4
Centro de Astrobiología (CAB), CSIC-INTA, Ctra. de Ajalvir km 4, Torrejón de Ardoz, 28850 Madrid, Spain
5
INAF – Osservatorio Astrofisico di Arcetri, Largo Enrico Fermi 5, 50125 Firenze, Italy
6
Departamento de Física Teórica, Universidad Autónoma de Madrid, 28049 Madrid, Spain
7
Instituto de Física de Partículas y del Cosmos (IPARCOS), Universidad Complutense de Madrid, 28040 Madrid, Spain
8
Dept. Physics, University of Oxford, Keble Road, Oxford OX1 3RH, UK
9
INAF–Osservatorio Astronomico di Padova, vicolo dell’Osservatorio 5, 35122 Padova, Italy
10
Instituto de Astrofísica de Canarias, IAC, Vía Láctea s/n, 38205 La Laguna (S.C. Tenerife), Spain
11
Departamento de Astrofísica, Universidad de La Laguna, 38206, La Laguna (S.C. Tenerife), Spain
12
Isaac Newton Group of Telescopes, ING, 38700 La Palma (S.C. Tenerife), Spain
13
INAF – Osservatorio di Astrofisica e Scienza dello Spazio, Via P. Gobetti 93/3, 40129 Bologna, Italy
14
School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT, UK
15
INAF – IASF Milano, Via Alfonso Corti 12, 20133 Milano, Italy
16
Dipartimento di Fisica e Astronomia “G. Galilei”, Università di Padova, vicolo dell’Osservatorio 3, 35122 Padova, Italy
17
RAL, Space, Science and Technology Facilities Council, Harwell, Didcot OX11 0QX, UK
18
Instituto de Astrofísica de Andalucía (CSIC), PO Box 3004 18080 Granada, Spain
19
Instituto de Astronomía y Ciencias Planetarias de Atacama (INCT), Universidad de Atacama, Copayapu 485, Copiapó, Chile
20
Kapteyn Astronomical Institute, Rijksuniversiteit Groningen, Landleven 12, 9747 AD Groningen, The Netherlands
21
Dipartimento di Fisica “E.R. Caianiello”, Università degli studi di Salerno, Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy
22
Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
23
Università di Bologna – Department of Physics and Astronomy, Via Gobetti 93/2, 40129 Bologna, Italy
Received:
14
March
2024
Accepted:
7
June
2024
Context. The William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph that allows us to collect about one thousand spectra over a 3 square degree field in one observation. The WEAVE Stellar Population Survey (WEAVE-StePS) in the next 5 years will exploit this new instrument to obtain high-S/N spectra for a magnitude-limited (IAB = 20.5) sample of ∼25 000 galaxies at moderate redshifts (z ≥ 0.3), providing insights into galaxy evolution in this as yet unexplored redshift range.
Aims. We aim to test novel techniques for retrieving the key physical parameters of galaxies from WEAVE-StePS spectra using both photometric and spectroscopic (spectral indices) information for a range of noise levels and redshift values.
Methods. We simulated ∼105 000 galaxy spectra assuming star formation histories with an exponentially declining star formation rate, covering a wide range of ages, stellar metallicities, specific star formation rates (sSFRs), and dust extinction values. We considered three redshifts (i.e. z = 0.3, 0.55, and 0.7), covering the redshift range that WEAVE-StePS will observe. We then evaluated the ability of the random forest and K-nearest neighbour algorithms to correctly predict the average age, metallicity, sSFR, dust attenuation, and time since the bulk of formation, assuming no measurement errors. We also checked how much the predictive ability deteriorates for different noise levels, with S/NI,obs = 10, 20, and 30, and at different redshifts. Finally, the retrieved sSFR was used to classify galaxies as part of the blue cloud, green valley, or red sequence.
Results. We find that both the random forest and K-nearest neighbour algorithms accurately estimate the mass-weighted ages, u-band-weighted ages, and metallicities with low bias. The dispersion varies from 0.08–0.16 dex for age and 0.11–0.25 dex for metallicity, depending on the redshift and noise level. For dust attenuation, we find a similarly low bias and dispersion. For the sSFR, we find a very good constraining power for star-forming galaxies, log sSFR ≳ −11, where the bias is ∼0.01 dex and the dispersion is ∼0.10 dex. However, for more quiescent galaxies, with log sSFR ≲ −11, we find a higher bias, ranging from 0.61 to 0.86 dex, and a higher dispersion, ∼0.4 dex, depending on the noise level and redshift. In general, we find that the random forest algorithm outperforms the K-nearest neighbours. Finally, we find that the classification of galaxies as members of the green valley is successful across the different redshifts and S/Ns.
Conclusions. We demonstrate that machine learning algorithms can accurately estimate the physical parameters of simulated galaxies for a WEAVE-StePS-like dataset, even at relatively low S/NI, obs = 10 per Å spectra with available ancillary photometric information. A more traditional approach, Bayesian inference, yields comparable results. The main advantage of using a machine learning algorithm is that, once trained, it requires considerably less time than other methods.
Key words: galaxies: evolution / galaxies: formation / galaxies: general / galaxies: stellar content
© The Authors 2024
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.
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