Characterization of Dwarf Novae Using SDSS Colors

TL;DR

This paper introduces neural network methods to estimate orbital periods and classify dwarf novae from SDSS colors, enabling analysis of their distribution and properties with improved accuracy and efficiency.

Contribution

It presents novel neural network techniques for estimating orbital periods and classifying dwarf novae based on SDSS data, enhancing analysis of their characteristics.

Findings

Estimated orbital periods with 22% accuracy for typical systems

Efficient classification into three dwarf nova types using neural networks

Found a flatter period distribution and shorter minimum period in the sample

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.