Astronomy and Astrophysics – Astrophysics – Solar and Stellar Astrophysics
Scientific paper
2011-11-18
Astronomy and Astrophysics
Astrophysics
Solar and Stellar Astrophysics
69 pages, 7 figures, Accepted for publication in PASJ
Scientific paper
We have developed a method for estimating orbital periods of dwarf novae from SDSS colors in quiescence using an artificial neural network. For typical objects below the period gap with good photometric accuracy, we could estimate orbital periods to a 1-sigma error of 22%. The error of estimation is worse for systems with longer periods. We have also developed a neural network-based method for categorical classification. This method has been 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 down to g=21. Using these methods, we have investigated the distribution of orbital periods of dwarf novae from a modern transient survey (Catalina Real-Time Survey). Using Bayesian analysis developed by Uemura et al. (2010, arXiv:1003.0945), the present sample tends to give a flatter distribution toward the shortest period and a shorter estimate of the period minimum, which may have been resulted from the uncertainties in the neural network analysis and photometric errors. We also provide estimated orbital periods, estimated classifications and supplementary information for known dwarf novae with quiescent SDSS photometry.
Kato Taichi
Maehara Hiroyuki
Uemura Makoto
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