A Neural Network - Based Tool for Rapid Orbital Solution of Contact Binary Systems
Abstract
With the rapid growth of large-scale time-domain surveys, vast amounts of photometric and spectroscopic data are continuously being released. A substantial number of contact binaries have been identified, accompanied by both light curves and radial velocity observations. Traditional modeling of these datasets relies on computationally intensive physical models, which limits their applicability to large-scale analyses. To this end, this paper proposes the use of a fully connected neural network and a SIREN (Sitzmann et al. 2020) model to rapidly generate radial velocity curves. After training, the models can rapidly generate radial velocity curves with high accuracy, significantly reducing computational costs and enabling efficient large-scale studies. This method was further integrated with the light curve computation framework developed by Li et al. (2025), allowing simultaneous fitting of radial velocities and light curves. To assess the reliability of the proposed model, synthetic observational datasets were constructed and used for parameter estimation. The deviations between the derived parameters and the ground-truth values were analyzed quantitatively, enabling a comprehensive evaluation of the model's accuracy.
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BibTeX
@misc{wang2026neural,
title = {{A Neural Network - Based Tool for Rapid Orbital Solution of Contact Binary Systems}},
author = {Li-Heng Wang and Kai Li and Ya-Ni Guo},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23410},
eprint = {2610.23410},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23410},
}
RIS
TY - UNPB
TI - A Neural Network - Based Tool for Rapid Orbital Solution of Contact Binary Systems
AU - Li-Heng Wang
AU - Kai Li
AU - Ya-Ni Guo
PY - 2026
DA - 2026/10/16
PB - arXiv
DO - 10.48550/arxiv.2610.23410
UR - https://arxiv.org/abs/2610.23410
AB - With the rapid growth of large-scale time-domain surveys, vast amounts of photometric and spectroscopic data are continuously being released. A substantial number of contact binaries have been identified, accompanied by both light curves and radial velocity observations. Traditional modeling of these datasets relies on computationally intensive physical models, which limits their applicability to large-scale analyses. To this end, this paper proposes the use of a fully connected neural network and a SIREN (Sitzmann et al. 2020) model to rapidly generate radial velocity curves. After training, the models can rapidly generate radial velocity curves with high accuracy, significantly reducing computational costs and enabling efficient large-scale studies. This method was further integrated with the light curve computation framework developed by Li et al. (2025), allowing simultaneous fitting of radial velocities and light curves. To assess the reliability of the proposed model, synthetic observational datasets were constructed and used for parameter estimation. The deviations between the derived parameters and the ground-truth values were analyzed quantitatively, enabling a comprehensive evaluation of the model's accuracy.
ER -