Learning novel representations of variable sources from multi-modal Gaia data via autoencoders
P. Huijse, J. De Ridder, L. Eyer, L. Rimoldini, B. Holl, N. Chornay, J. Roquette, K. Nienartowicz, G. Jévardat de Fombelle, D. J. Fritzewski, A. Kemp, Vincent Vanlaer, M. Vanrespaille, H. Wang, M. I. Carnerero, C. M. Raiteri, Gabor Marton, M. Madarász, G. Clementini, P. Gavras · Astronomy and Astrophysics · 2025
Context. Gaia Data Release 3 (DR3) has published for the first time epoch photometry, BP/RP (XP) low-resolution mean spectra, and supervised classification results for millions of variable sources. This extensive dataset offers a unique opportunity to study the variability of these objects by combining multiple Gaia data products. Aims. In preparation for DR4, we propose and evaluate a machine learning methodology capable of ingesting multiple Gaia data products to achieve an unsupervised classification of stellar and quasar variability. Methods. A dataset of 4 million Gaia DR3 sources was used to train three variational autoencoders (VAEs), which are artificial neural networks (ANNs) designed for data compression and generation. One VAE was trained on Gaia XP low-resolution spectra, another on a novel approach based on the distribution of magnitude differences in the Gaia G band, and the third on folded Gaia G band light curves. Each Gaia source was compressed into 15 numbers, representing the coordinates in a 15-dimensional latent space generated by combining the outputs of these three models. Results. The learned latent representation produced by the ANN effectively distinguishes between the main variability classes present in Gaia DR3, as demonstrated through both supervised and unsupervised classification analysis of the latent space. The results highlight a strong synergy between light curves and low-resolution spectral data, emphasising the benefits of combining the different Gaia data products. A 2D projection of the latent variables revealed numerous overdensities, most of which strongly correlate with astrophysical properties, showing the potential of this latent space for astrophysical discovery. Conclusions. We show that the properties of our novel latent representation make it highly valuable for variability analysis tasks, including classification, clustering, and outlier detection.