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Using Autoencoders And Deep Transfer Learning To Determine The Stellar Parameters Of 286 CARMENES M Dwarfs

By Keith Cowing

Status Report

astro-ph.SR

May 15, 2024

Filed under AI, astro-ph.SR, autoencoder, CARMENES, data, Deep Learning, M dwarf, PHOENIX-ACES, Spectroscopy

Using Autoencoders And Deep Transfer Learning To Determine The Stellar Parameters Of 286 CARMENES M Dwarfs — Analysis of the stellar parameters derived with our methodology. The dots are colour-coded according to the estimated metallicity. The size of the dots is proportional to the estimated projected rotational velocity. The top left panel shows a Kiel diagram, with the red, black, and blue dashed lines corresponding to 5 Gyr PARSEC isochrones with [M/H] = −0.4, 0.0 and 0.1 dex, respectively. Empty squares represent the stars reported to have a behaviour akin to subdwarfs both in Mar21 and Schw19 (same for bottom left panel). Top right: black and grey dashed lines correspond to solar metallicity PARSEC isochrones for 5 and 0.1 Gyr, respectively. Black and grey dotted lines correspond to solar metallicity Baraffe et al. (2015) isochrones for 5 and 0.1 Gyr, respectively. Bottom left: triangles represent stars identified as Hα active in Schöfer et al. (2019). Empty stars depict members of the thick disc Galactic population (Cortés-Contreras et al., in prep.). Bottom right: plus symbols correspond to stars identified as members of the young disc Galactic population by Cortés-Contreras et al. (in prep.). Empty circles represent stars with a possible membership in a young stellar associaton, as explained in Section 4.1. — astro-ph.SR

Deep learning (DL) techniques are a promising approach among the set of methods used in the ever-challenging determination of stellar parameters in M dwarfs.

In this context, transfer learning could play an important role in mitigating uncertainties in the results due to the synthetic gap (i.e. difference in feature distributions between observed and synthetic data).

We propose a feature-based deep transfer learning (DTL) approach based on autoencoders to determine stellar parameters from high-resolution spectra. Using this methodology, we provide new estimations for the effective temperature, surface gravity, metallicity, and projected rotational velocity for 286 M dwarfs observed by the CARMENES survey. Using autoencoder architectures, we projected synthetic PHOENIX-ACES spectra and observed CARMENES spectra onto a new feature space of lower dimensionality in which the differences between the two domains are reduced.

We used this low-dimensional new feature space as input for a convolutional neural network to obtain the stellar parameter determinations. We performed an extensive analysis of our estimated stellar parameters, ranging from 3050 to 4300 K, 4.7 to 5.1 d_ex, and -0.53 to 0.25 d_ex for T_eff, l_ogg, and [Fe/H], respectively. Our results are broadly consistent with those of recent studies using CARMENES data, with a systematic deviation in our Teff scale towards hotter values for estimations above 3750 K.

Furthermore, our methodology mitigates the deviations in metallicity found in previous DL techniques due to the synthetic gap. We consolidated a DTL-based methodology to determine stellar parameters in M dwarfs from synthetic spectra, with no need for high-quality measurements involved in the knowledge transfer. These results suggest the great potential of DTL to mitigate the differences in feature distributions between the observations and the PHOENIX-ACES spectra.

P. Mas-Buitrago, A. González-Marcos, E. Solano, V. M. Passegger, M. Cortés-Contreras, J. Ordieres-Meré, A. Bello-García, J. A. Caballero, A. Schweitzer, H. M. Tabernero, D. Montes, C. Cifuentes

Comments: Accepted in A&A
Subjects: Solar and Stellar Astrophysics (astro-ph.SR); Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2405.08703 [astro-ph.SR] (or arXiv:2405.08703v1 [astro-ph.SR] for this version)
https://doi.org/10.48550/arXiv.2405.08703
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Submission history
From: Pedro Mas Buitrago Mr
[v1] Tue, 14 May 2024 15:42:27 UTC (3,141 KB)
https://arxiv.org/abs/2405.08703
Astrobiology

Keith Cowing

Explorers Club Fellow, ex-NASA Space Station Payload manager/space biologist, Away Teams, Journalist, Lapsed climber, Synaesthete, Na’Vi-Jedi-Freman-Buddhist-mix, ASL, Devon Island and Everest Base Camp veteran, (he/him) 🖖🏻

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