Deep Bayesian spectral learning for the discovery of hot subdwarf binaries in LAMOST

Context . Hot subdwarf binaries provide stringent constraints on envelope stripping and binary mass transfer. However, robust identification of these systems in large spectroscopic surveys is hindered by variable data quality and the subtle signatures of their companions. Aims . We aim to develop a robust automated framework for identifying binaries comprising a hot subdwarf and a main-sequence star within the diverse datasets of the LAMOST low-resolution survey, utilizing deep learning to overcome challenges posed by complex noise and class imbalance. Methods . We propose a Bayesian hybrid model that combines convolutional neural networks for local feature extraction with a Bayesian transformer encoder to model long-range dependencies. This architecture incorporates variational inference to improve classification accuracy and facilitate robust candidate identification. Results . The framework attains an accuracy of 95.3% on the test set, with a precision of 97.2% for the binary class. Applying the model to the LAMOST dataset yields 1161 binary candidates. Further certification via spectral energy distribution fitting confirms 968 binaries among the 1001 objects with reliable fits. Multi-epoch radial velocity measurements of 352 candidates identified 119 systems with significant variability. Conclusions . A subset of the candidates show no significant radial velocity changes over baselines exceeding 1000 days despite exhibiting infrared excess, consistent with the behavior of long-period binaries. The proposed Bayesian framework effectively quantifies predictive uncertainty to filter out data artifacts, yielding a sample suitable for constraining evolutionary pathways such as common envelope ejection and stable Roche lobe overflow.