Dwarf novae characterization using SDSS colors

For typical objects below the period gap with sufficient photometric accuracy, we were able to estimate the orbital periods with accuracy to a 1{sigma} error of 22%. The error of the 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=21mag. Using this method, we have investigated the distribution of the orbital periods of dwarf novae from a modern transient survey (Catalina Real-Time Survey).