Characterization of Dwarf Novae Using SDSS Colors

Abstract

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.

70 pages, 7 figures, Accepted for publication in PASJ, minor corrections

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• Cataclysmic Variables from the Catalina Real-time Transient Survey
• New Method to Estimate Binary Mass Ratios by Using Superhumps
• Survey of Period Variations of Superhumps in SU UMa-Type Dwarf Novae. IV: The Fourth Year (2011-2012)
• Survey of Period Variations of Superhumps in SU UMa-Type Dwarf Novae. V: The Fifth Year (2012-2013)
• Hunting For Eclipses: High Speed Observations of Cataclysmic Variables
• Classical Novae Masquerading as Dwarf Novae? Outburst Properties of Cataclysmic Variables with ASAS-SN
• Catching a nova X-ray/UV flash in the visible? Early spectroscopy of the extremely slow Nova Velorum 2022 (Gaia22alz)