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Characterization of Dwarf Novae Using SDSS Colors
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 sigma 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,
arXiv:1003.0945), 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.Comment: 70 pages, 7 figures, Accepted for publication in PASJ, minor
correction
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