Unresolved Binary Systems in the Rubin Era I: An Autoencoder Framework for Binary Identification Applied to 47 Tucanae
Abstract
Binary systems are extremely common and influence many areas of astrophysics, such as stellar evolution and cluster dynamics. In this work, we introduce flexAE, a framework that combines a classical autoencoder architecture with a dedicated classifier component. It is designed to distinguish between single stars and binary systems based on their broadband spectral energy distributions, using all available photometry simultaneously while accounting for observational uncertainties. We demonstrate the capability of flexAE by using it to identify unresolved main sequence binaries in the outskirts of the 47 Tucanae globular cluster (NGC 104) with photometric data from the Vera C. Rubin Observatory. We train the model on a simulated sample of single star and binary systems created with stellar atmosphere models. The model accurately reconstructs the photometric input features and achieves a classification accuracy of 0.85 on the simulated test set. We then apply the trained model to a sample of 1,424 cluster members found in Rubin DP1 with reliable gri photometry, of which 32 are identified by the model as unresolved main sequence binaries. This corresponds to an observed binary fraction of 2.2+0.5−0.3%. We estimate that the intrinsic main sequence binary fraction lies between 3.7+0.8−0.5% and 7.5+1.5−1.1%. The binary fraction stays constant between 18-36 arcmin (5.7-11.4 half-light radii) from the cluster center. We highlight that flexAE can be easily adapted for other use cases. This makes flexAE well-suited for large-scale binary classification in upcoming wide-field surveys, such as Rubin LSST. The code is publicly available at https://github.com/tobiasgeron/flexAE.
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BibTeX
@misc{geron2026unresolved,
title = {{Unresolved Binary Systems in the Rubin Era I: An Autoencoder Framework for Binary Identification Applied to 47 Tucanae}},
author = {Tobias Géron and Alexander Laroche and Joshua S. Speagle and Maria R. Drout},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.05616},
eprint = {2609.05616},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.05616},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Unresolved Binary Systems in the Rubin Era I: An Autoencoder Framework for Binary Identification Applied to 47 Tucanae
AU - Tobias Géron
AU - Alexander Laroche
AU - Joshua S. Speagle
AU - Maria R. Drout
PY - 2026
DA - 2026/09/04
PB - arXiv
DO - 10.48550/arxiv.2609.05616
UR - https://arxiv.org/abs/2609.05616
AB - Binary systems are extremely common and influence many areas of astrophysics, such as stellar evolution and cluster dynamics. In this work, we introduce flexAE, a framework that combines a classical autoencoder architecture with a dedicated classifier component. It is designed to distinguish between single stars and binary systems based on their broadband spectral energy distributions, using all available photometry simultaneously while accounting for observational uncertainties. We demonstrate the capability of flexAE by using it to identify unresolved main sequence binaries in the outskirts of the 47 Tucanae globular cluster (NGC 104) with photometric data from the Vera C. Rubin Observatory. We train the model on a simulated sample of single star and binary systems created with stellar atmosphere models. The model accurately reconstructs the photometric input features and achieves a classification accuracy of 0.85 on the simulated test set. We then apply the trained model to a sample of 1,424 cluster members found in Rubin DP1 with reliable gri photometry, of which 32 are identified by the model as unresolved main sequence binaries. This corresponds to an observed binary fraction of 2.2^+0.5_−0.3%. We estimate that the intrinsic main sequence binary fraction lies between 3.7^+0.8_−0.5% and 7.5^+1.5_−1.1%. The binary fraction stays constant between 18-36 arcmin (5.7-11.4 half-light radii) from the cluster center. We highlight that flexAE can be easily adapted for other use cases. This makes flexAE well-suited for large-scale binary classification in upcoming wide-field surveys, such as Rubin LSST. The code is publicly available at https://github.com/tobiasgeron/flexAE.
ER -