The Mini-SiTian(MST)project is a pathfinder for China's next-generation large-scale time-domain survey,SiTian,aimed at discovering variable stars,transients,and explosive events.MST generates hundreds of thousands...The Mini-SiTian(MST)project is a pathfinder for China's next-generation large-scale time-domain survey,SiTian,aimed at discovering variable stars,transients,and explosive events.MST generates hundreds of thousands of transient alerts every night,approximately 99%of which are false alarms,posing a significant challenge to its scientific goals.To mitigate the impact of false positives,we propose a deep learning–based solution and systematically evaluate 13 convolutional neural networks.The results show that ResNet achieves exceptional specificity(99.70%),EfficientNet achieves the highest recall rate(98.68%),and DenseNet provides balanced performance with a recall rate of 94.55%and specificity of 98.66%.Leveraging these complementary strengths,we developed a bagging-based ensemble classifier that integrates ResNet18,DenseNet121,and EfficientNet_B0 using a soft voting strategy.This classifier achieved the best AUC value(0.9961)among all models,with a recall rate of95.37%and specificity of 99.25%.It has now been successfully deployed in the MST real-time data processing pipeline.Validation using 5000 practically processed samples with a classification threshold of 0.798 showed that the classifier achieved 88.31%accuracy,91.89%recall rate,and 99.82%specificity,confirming its effectiveness and robustness under real application conditions.展开更多
We assess the detectability of tidal disruption events(TDEs)using mock observations from the Mini-SiTian Array.We select 100 host galaxy samples from a simulated galaxy catalog based on specific criteria such as redsh...We assess the detectability of tidal disruption events(TDEs)using mock observations from the Mini-SiTian Array.We select 100 host galaxy samples from a simulated galaxy catalog based on specific criteria such as redshift,black hole mass,and event rate.Taking into account the site conditions and survey strategy,we simulate observations over a 440 deg^(2)field.The results indicate that 0.53±0.73 TDEs can be detected per year when observing in both g and r bands with 300 s exposures every 3 days.Applying this method to the SiTian project,we expect to discover approximately 204 TDEs annually,heralding a new era in TDE science.展开更多
基金supported by the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)the National Natural Science Foundation of China under grant Nos.12273076,12133001,12422303 and12261141690。
文摘The Mini-SiTian(MST)project is a pathfinder for China's next-generation large-scale time-domain survey,SiTian,aimed at discovering variable stars,transients,and explosive events.MST generates hundreds of thousands of transient alerts every night,approximately 99%of which are false alarms,posing a significant challenge to its scientific goals.To mitigate the impact of false positives,we propose a deep learning–based solution and systematically evaluate 13 convolutional neural networks.The results show that ResNet achieves exceptional specificity(99.70%),EfficientNet achieves the highest recall rate(98.68%),and DenseNet provides balanced performance with a recall rate of 94.55%and specificity of 98.66%.Leveraging these complementary strengths,we developed a bagging-based ensemble classifier that integrates ResNet18,DenseNet121,and EfficientNet_B0 using a soft voting strategy.This classifier achieved the best AUC value(0.9961)among all models,with a recall rate of95.37%and specificity of 99.25%.It has now been successfully deployed in the MST real-time data processing pipeline.Validation using 5000 practically processed samples with a classification threshold of 0.798 showed that the classifier achieved 88.31%accuracy,91.89%recall rate,and 99.82%specificity,confirming its effectiveness and robustness under real application conditions.
基金the support of the staff at the Xinglong Observatorysupported by the National Key R&D Program of China(grant No.2023YFA1609700)+10 种基金supported by the National Natural Science Foundation of China(NSFCgrant Nos.12090040,12090041,12403022 and 12273057)supported by the Strategic Priority Research Program of Chinese Academy of Sciences(grant Nos.XDB0550000,XDB0550100and XDB0550102)supported by the National Key R&D Program of China(grant No.2023YFA1608304)the supports from NSFC(grant Nos.12422303,12261141690)the National Key Basic R&D Program of China via2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)the supports of the NSFC(grant No.12403024)the Postdoctoral Fellowship Program of CPSF under grant No.GZB20240731the Young Data Scientist Project of the National Astronomical Data Centerthe China Post-doctoral Science Foundation(No.2023M743447)。
文摘We assess the detectability of tidal disruption events(TDEs)using mock observations from the Mini-SiTian Array.We select 100 host galaxy samples from a simulated galaxy catalog based on specific criteria such as redshift,black hole mass,and event rate.Taking into account the site conditions and survey strategy,we simulate observations over a 440 deg^(2)field.The results indicate that 0.53±0.73 TDEs can be detected per year when observing in both g and r bands with 300 s exposures every 3 days.Applying this method to the SiTian project,we expect to discover approximately 204 TDEs annually,heralding a new era in TDE science.
