Modeling lightcurves for improved classification of astronomical objects

Julian Faraway, Ashish Mahabal, Jiayang Sun, Xiao-Feng Wang, Yi G. Wang, Lingsong Zhang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
234 Downloads (Pure)

Abstract

Many synoptic surveys are observing large parts of the sky multiple times. The resulting time series of light measurements, called lightcurves, provide a wonderful window to the dynamic nature of the Universe. However, there are many significant challenges in analyzing these lightcurves. We describe a modeling-based approach using Gaussian process regression for generating critical measures for the classification of such lightcurves. This method has key advantages over other popular nonparametric regression methods in its ability to deal with censoring, a mixture of sparsely and densely sampled curves, the presence of annual gaps caused by objects not being visible throughout the year from a given position on Earth and known but variable measurement errors. We demonstrate that our approach performs better by showing it has a higher correct classification rate than past methods popular in astronomy. Finally, we provide future directions for use in sky-surveys that are getting even bigger by the day.
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalStatistical Analysis and Data Mining
Volume9
Issue number1
Early online date1 Feb 2016
DOIs
Publication statusPublished - 9 Feb 2016

Keywords

  • Classification
  • Feature selection
  • Gaussian process regression
  • Irregular sampling
  • Missing data

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