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The Gaia -ESO Surve...
The Gaia -ESO Survey : Chemical evolution of Mg and Al in the Milky Way with machine learning
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- Ambrosch, M. (författare)
- Vilnius University
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- Guiglion, G. (författare)
- Leibniz Institute for Astrophysics Potsdam (AIP),Max Planck Institute for Astronomy
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- Mikolaitis, S. (författare)
- Vilnius University
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- Chiappini, C. (författare)
- Leibniz Institute for Astrophysics Potsdam (AIP)
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- Tautvaisiene, G. (författare)
- Vilnius University
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- Nepal, S. (författare)
- Leibniz Institute for Astrophysics Potsdam (AIP),University of Potsdam
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- Gilmore, G. (författare)
- University of Cambridge
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- Randich, S. (författare)
- INAF - Osservatorio Astrofisico di Arcetri
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- Bensby, T. (författare)
- Lund University,Lunds universitet,Astronomi - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Astrofysik,Fysiska institutionen,Institutioner vid LTH,Lunds Tekniska Högskola,Lund Observatory - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science,Astrophysics,Department of Physics,Departments at LTH,Faculty of Engineering, LTH
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- Bayo, A. (författare)
- University of Valparaíso,European Southern Observatory
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- Bergemann, M. (författare)
- Max Planck Institute for Astronomy,Niels Bohr Institute
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- Morbidelli, L. (författare)
- INAF - Osservatorio Astrofisico di Arcetri
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- Pancino, E. (författare)
- INAF - Osservatorio Astrofisico di Arcetri
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- Sacco, G. G. (författare)
- INAF - Osservatorio Astrofisico di Arcetri
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- Smiljanic, R. (författare)
- Nicolaus Copernicus Astronomical Center of the Polish Academy of Sciences
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- Zaggia, S. (författare)
- INAF-Osservatorio Astronomico di Roma
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- Jofré, P. (författare)
- Diego Portales University
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- Jiménez-Esteban, F. M. (författare)
- CSIC Centro de Astrobiologia (INTA)
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(creator_code:org_t)
- 2023-03-27
- 2023
- Engelska.
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 672
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Context. To take full advantage of upcoming large-scale spectroscopic surveys, it will be necessary to parameterize millions of stellar spectra in an efficient way. Machine learning methods, especially convolutional neural networks (CNNs), will be among the main tools geared at achieving this task. Aims. We aim to prepare the groundwork for machine learning techniques for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that CNNs can predict accurate stellar labels from relevant spectral features in a physically meaningful way. The predicted labels can be used to investigate properties of the Milky Way galaxy. Methods. We built a neural network and trained it on GIRAFFE spectra with their associated stellar labels from the sixth internal Gaia-ESO data release. Our network architecture contains several convolutional layers that allow the network to identify absorption features in the input spectra. The internal uncertainty was estimated from multiple network models. We used the t-distributed stochastic neighbor embedding tool to remove bad spectra from our training sample. Results. Our neural network is able to predict the atmospheric parameters Teff and log(g) as well as the chemical abundances [Mg/Fe], [Al/Fe], and [Fe/H] for 36 904 stellar spectra. The training precision is 37 K for Teff, 0.06 dex for log(g), 0.05 dex for [Mg/Fe], 0.08 dex for [Al/Fe], and 0.04 dex for [Fe/H]. Network gradients reveal that the network is inferring the labels in a physically meaningful way from spectral features. We validated our methodology using benchmark stars and recovered the properties of different stellar populations in the Milky Way galaxy. Conclusions. Such a study provides very good insights into the application of machine learning for the analysis of large-scale spectroscopic surveys, such as WEAVE and 4MOST Milky Way disk and bulge low- and high-resolution (4MIDABLE-LR and -HR). The community will have to put substantial efforts into building proactive training sets for machine learning methods to minimize any possible systematics.
Ämnesord
- NATURVETENSKAP -- Fysik -- Astronomi, astrofysik och kosmologi (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences -- Astronomy, Astrophysics and Cosmology (hsv//eng)
Nyckelord
- Galaxy: abundances
- Galaxy: stellar content
- Methods: data analysis
- Stars: abundances
- Techniques: spectroscopic
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Till lärosätets databas
- Av författaren/redakt...
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Ambrosch, M.
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Guiglion, G.
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Mikolaitis, S.
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Chiappini, C.
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Tautvaisiene, G.
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Nepal, S.
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visa fler...
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Gilmore, G.
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Randich, S.
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Bensby, T.
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Bayo, A.
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Bergemann, M.
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Morbidelli, L.
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Pancino, E.
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Sacco, G. G.
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Smiljanic, R.
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Zaggia, S.
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Jofré, P.
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Jiménez-Esteban, ...
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visa färre...
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- NATURVETENSKAP
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Lunds universitet