Hot subdwarf stars are important celestial objects in the study of stellar physics,but the population remains limited.The LAMOST DR12-V1,released in 2025 March,is currently the world’s largest spectroscopic database,...Hot subdwarf stars are important celestial objects in the study of stellar physics,but the population remains limited.The LAMOST DR12-V1,released in 2025 March,is currently the world’s largest spectroscopic database,holding great potential for the search of hot subdwarf stars.In this study,we propose a two-stage deep learning model called the hot subdwarf network(HsdNet),which integrates multiple advanced techniques,comprising a binary classification model in stage one and a five-class classification model in stage two.HsdNet not only achieves high precision with 94.33%and 94.00%in the binary and the five-class classification stages,respectively,but also quantifies the predicted uncertainty,enhancing the interpretability of the classification results through visualizing the model’s key focus regions.We applied HsdNet to the 601,217 spectra from the LAMOST DR12-V1 database,conducting a two-stage search for hot subdwarf candidates.In stage one,we initially identified candidates using the binary classification model.In stage two,the five-class classification model was used to further refine these candidates.Finally,we confirmed 1008 newly identified hot subdwarf stars.The distribution of their atmospheric parameters is consistent with that of known hot subdwarf stars.These efforts are expected to significantly advance the research on hot subdwarf stars.展开更多
基金the Natural Science Foundation of Shandong Province with grant Nos.ZR2024MA063,ZR2022MA076 and ZR2022MA089Additional support was provided by the science research grants from the China Manned Space Project with grant Nos.CMS-CSST-2021-B05 and CMS-CSST-2021-A08+1 种基金supported by the National Natural Science Foundation of China(NSFC)under grant Nos.11873037 and 11803016Furthermore,the Young Scholars Program of Shandong University,Weihai(2016WHWLJH09)provided support for this research.
文摘Hot subdwarf stars are important celestial objects in the study of stellar physics,but the population remains limited.The LAMOST DR12-V1,released in 2025 March,is currently the world’s largest spectroscopic database,holding great potential for the search of hot subdwarf stars.In this study,we propose a two-stage deep learning model called the hot subdwarf network(HsdNet),which integrates multiple advanced techniques,comprising a binary classification model in stage one and a five-class classification model in stage two.HsdNet not only achieves high precision with 94.33%and 94.00%in the binary and the five-class classification stages,respectively,but also quantifies the predicted uncertainty,enhancing the interpretability of the classification results through visualizing the model’s key focus regions.We applied HsdNet to the 601,217 spectra from the LAMOST DR12-V1 database,conducting a two-stage search for hot subdwarf candidates.In stage one,we initially identified candidates using the binary classification model.In stage two,the five-class classification model was used to further refine these candidates.Finally,we confirmed 1008 newly identified hot subdwarf stars.The distribution of their atmospheric parameters is consistent with that of known hot subdwarf stars.These efforts are expected to significantly advance the research on hot subdwarf stars.
