Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published December 2017 | Submitted
Book Section - Chapter Open

Deep-learnt classification of light curves

Abstract

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.

Additional Information

© 2017 IEEE. This work, and CRTS survey, was supported in part by the NSF grants AST-0909182, AST-1313422, AST-1413600, and AST-1518308, and by the Ajax Foundation. KS thanks IIT Gandhinagar and the Caltech SURF program.

Attached Files

Submitted - 1709.06257.pdf

Files

1709.06257.pdf
Files (993.0 kB)
Name Size Download all
md5:ba54ab2511d1e0df42a6c3c46a9833b7
993.0 kB Preview Download

Additional details

Created:
August 22, 2023
Modified:
October 18, 2023