We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope(Mephisto)W Survey.Based on the varia...We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope(Mephisto)W Survey.Based on the variable sources light curve libraries from the Sloan Digital Sky Survey(SDSS)Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler,we have simulated the uvgriz multi-band light curves of RR Lyrae stars,quasars and other variable sources for the first year observation of Mephisto W Survey.We have applied the ensemble machine learning algorithm Random Forest Classifier(RFC)to identify RR Lyrae stars and quasars,respectively.We build training and test samples and extract~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification.We find that,our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness,with purity=95.4%and completeness=96.9%for the RR Lyrae RFC and purity=91.4%and completeness=90.2%for the quasar RFC.We have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.展开更多
基金funded by the National Natural Science Foundation of China(NSFC)Nos.11803029,11833006 and 12173034the National Training Program of Innovation and Entrepreneurship for Undergraduates of China No.201910673001,Yunnan University grant C176220100007+8 种基金the National Key R&D Program of China No.2019YFA0405500the science research grants from the China Manned Space Project with Nos.CMS-CSST-2021-A09,CMS-CSST-2021-A08 and CMS-CSST2021-B03Funding for SDSS-Ⅲhas been provided by the Alfred P.Sloan Foundation,the Participating Institutions,the National Science Foundation,and the U.S.Department of Energy Office of ScienceThe national facility capability for Sky Mapper has been funded through ARC LIEF grant LE130100104 from the Australian Research CouncilDevelopment and support of the Sky Mapper node of the ASVO has been funded in part by Astronomy Australia Limited(AAL)the Australian Government through the Commonwealth’s Education Investment Fund(EIF)National Collaborative Research Infrastructure Strategy(NCRIS)the National e Research Collaboration Tools and Resources(Ne CTAR)the Australian National Data Service Projects(ANDS)。
文摘We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope(Mephisto)W Survey.Based on the variable sources light curve libraries from the Sloan Digital Sky Survey(SDSS)Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler,we have simulated the uvgriz multi-band light curves of RR Lyrae stars,quasars and other variable sources for the first year observation of Mephisto W Survey.We have applied the ensemble machine learning algorithm Random Forest Classifier(RFC)to identify RR Lyrae stars and quasars,respectively.We build training and test samples and extract~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification.We find that,our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness,with purity=95.4%and completeness=96.9%for the RR Lyrae RFC and purity=91.4%and completeness=90.2%for the quasar RFC.We have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.
