J/A+A/702/A148 Estimating [Fe/H] of RR Lyrae using Gaia DR3 (Monti+, 2025)
Unified deep learning approach for estimating the metallicities of RR Lyrae
stars using light curves from Gaia Data Release 3.
Monti L., Muraveva T., Garofalo A., Clementini G., Valentini M.L.
<Astron. Astrophys. 702, A148 (2025)>
=2025A&A...702A.148M 2025A&A...702A.148M (SIMBAD/NED BibCode)
ADC_Keywords: Stars, variable ; Abundances, [Fe/H] ; Optical
Keywords: methods: data analysis - stars: abundances -
variable stars: RR Lyrae
Abstract:
RR Lyrae stars (RRLs) are old population pulsating variables that
serve as useful metallicity tracers due to the correlation between
their metal abundances and the shape of their light curves. With the
advent of ESA's Gaia mission Data Release 3 (DR3), which provides
light curves for approximately 270000 RRLs, it has become crucial to
develop a machine learning technique that allows for the estimation of
metallicities for large samples of RRLs directly from their light
curves.
This work extends our previous methodological paper on RRab stars by
developing and validating a unified Deep Learning (DL) framework
capable of accurately estimating metallicities for both fundamental
mode (RRab) and first-overtone (RRc) pulsators using their Gaia DR3
G-band light curves. Our goal is to create a single, consistent model
to produce a large, homogeneous metallicity catalogue.
We employed a Gated Recurrent Units (GRUs)-based neural network
architecture optimised for time-series extrinsic regression. The
framework incorporates a rigorous preprocessing pipeline (including
phase folding, smoothing, and sample weighting) and is trained using
Gaia DR3 G-band light curves and photometric metallicities of RRLs,
available in the literature. The model architecture and training
implicitly handle the morphological differences between RRab and RRc
light curves.
Our unified GRU model achieves high predictive accuracy. It
successfully confirms the high precision for RRab stars reported in
our previous work (RMSE=0.0765dex, R2=0.9401) and, crucially,
demonstrates even stronger performance for the more challenging RRc
stars (RMSE=0.0720dex, R2=0.9625). This represents a significant
improvement over previous DL benchmarks. We also present a key
finding: a clear positive correlation between the number of
photometric data points in a light curve and the precision of the
final metallicity estimate, quantifying the value of well-sampled
observations.
Crucially, we demonstrate that prediction accuracy scales with the
number of photometric epochs, establishing that this framework is
poised to deliver unprecedented precision with richer future datasets.
The enhanced light curves from Gaia DR4 and the Vera C. Rubin
Observatory will enable this methodology to produce metallicity
catalogues of unprecedented scale and fidelity, paving the way for
next-generation studies in Galactic archaeology and chemo-dynamics.
Description:
Parameters of 6002 RRab (a) and 6613 RRc (b) stars from the Gaia DR3
catalogue (Clementini et al., 2023A&A...674A..18C 2023A&A...674A..18C, Cat. J/A+A/674/A18)
selected as discussed in Sect.2.1.
Notes. Column (1) identification number; Column (2) Gaia DR3
source_id; Column (3) Pulsation period (days); Column (4) Amplitude in
the G band (mag); Column (5) Number of epochs in the G band; Columns
(6) and (7) Photometric metallicity and errors from Muraveva et al.
(2025MNRAS.536.2749M 2025MNRAS.536.2749M).
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
tablea1.dat 98 12615 Parameters of 6002 RRab stars and 6613 RRc stars
from the Gaia DR3 catalogue
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See also:
I/355 : Gaia DR3 Part 1. Main source (Gaia Collaboration, 2022)
J/A+A/674/A18 : Gaia DR3. The RR Lyrae sample (Clementini+, 2023)
Byte-by-byte Description of file: tablea1.dat
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Bytes Format Units Label Explanations
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1- 4 A4 --- RRtype [RRab RRc] RR Lyrae type
6- 24 I19 --- GaiaDR3 Gaia DR3 source_id
26- 43 F18.16 d Per Period
45- 54 F10.8 mag AmpG Amplitude in G band
56- 58 I3 --- Nepochs Number of epochs
60- 78 F19.16 --- [Fe/H] Photometric metallicity
80- 98 F19.17 --- e_[Fe/H] Uncertainty in [Fe/H]
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Acknowledgements:
Lorenzo Monti, lorenzo.monti(at)inaf.it
(End) Lorenzo Monti [INAF - OAS Bologna], Patricia Vannier [CDS] 10-Sep-2025