Deep-learnt classification of light curves

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  • Ashish Mahabal
  • Fabian Gieseke
  • Akshay Sadananda Uppinakudru Pai
  • S G Djorgovski
  • A J Drake
  • M J Graham
  • CSS/CRTS/PTF Teams
Astronomy light curves are sparse, gappy, and heteroscedastic. As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves. A common approach is to derive statistical features from the time series and to use machine learning methods, generally supervised, to separate objects into a few of the standard classes. In this work, we transform the time series to two-dimensional light curve representations in order to classify them using modern deep learning techniques. In particular, we show that convolutional neural networks based classifiers work well for broad characterization and classification. We use labeled datasets of periodic variables from CRTS survey and show how this opens doors for a quick classification of diverse classes with several possible exciting extensions.
Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings
Number of pages8
PublisherIEEE
Publication date2017
Pages1-8
ISBN (Electronic)978-1-5386-2726-6
DOIs
Publication statusPublished - 2017
Event2017 IEEE Symposium Series on Computational Intelligence (SSCI) - Honolulu, United States
Duration: 27 Nov 20171 Dec 2017

Conference

Conference2017 IEEE Symposium Series on Computational Intelligence (SSCI)
LandUnited States
ByHonolulu
Periode27/11/201701/12/2017

    Research areas

  • graphics processing units, least squares approximations, optimisation, parallel processing, regression analysis, sensitivity analysis, input dimensions, linear regression models, massively-parallel best subset selection, optimal feature subsets, optimal subset, ordinary least-squares regression, subset selection, Computational modeling, Graphics processing units, Instruction sets, Optimization, Runtime, Task analysis, Training

ID: 195160567