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ID 46725
本文ファイル
著者
Kato, Taichi
Maehara, Hiroyuki
Uemura, Makoto 宇宙科学センター 広大研究者総覧
キーワード
methods: statistical — stars: novae, cataclysmic variables — stars: dwarf novae — stars: evolution — surveys
NDC
天文学・宇宙科学
抄録(英)
We have developed a method for estimating the orbital periods of dwarf novae from the Sloan Digital Sky Survey (SDSS) colors in quiescence using an artificial neural network. For typical objects below the period gap with sufficient photometric accuracy, we were able to estimate the orbital periods with an accuracy to a 1 σ error of 22 %. The error of estimation is worse for systems with longer orbital periods. We have also developed a neural-network-based method for categorical classification. This method has proven to be efficient in classifying objects into three categories (WZ Sge type, SU UMa type and SS Cyg/Z Cam type) and works for very faint objects to a limit of g=21. Using this method, we have investigated the distribution of the orbital periods of dwarf novae from a modern transient survey (Catalina Real-Time Survey). Using Bayesian analysis developed by Uemura et al. (2010), we have found that the present sample tends to give a flatter distribution toward the shortest period and a shorter estimate of the period minimum, which may have resulted from the uncertainties in the neural network analysis and photometric errors. We also provide estimated orbital periods, estimated classifications and supplementary information on known dwarf novae with quiescent SDSS photometry.
掲載誌名
Publications of the Astronomical Society of Japan
64巻
3号
開始ページ
63-1
終了ページ
63-63
出版年月日
2012-06-25
出版者
Astronomical Society of Japan
ISSN
0004-6264
NCID
出版者DOI
言語
英語
NII資源タイプ
学術雑誌論文
広大資料タイプ
学術雑誌論文
DCMIタイプ
text
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application/pdf
著者版フラグ
author
権利情報
Copyright (c) 2012 The Astronomical Society of Japan
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
関連情報URL
部局名
宇宙科学センター