Deep-learnt classification of light curves

TY - GEN

T1 - Deep-learnt classification of light curves

AU - Mahabal, Ashish

AU - Gieseke, Fabian

AU - Pai, Akshay Sadananda Uppinakudru

AU - Djorgovski, S G

AU - Drake, A J

AU - Graham, M J

AU - CSS/CRTS/PTF Teams

PY - 2017/7/1

Y1 - 2017/7/1

N2 - 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.

AB - 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.

KW - graphics processing units

KW - least squares approximations

KW - optimisation

KW - parallel processing

KW - regression analysis

KW - sensitivity analysis

KW - input dimensions

KW - linear regression models

KW - massively-parallel best subset selection

KW - optimal feature subsets

KW - optimal subset

KW - ordinary least-squares regression

KW - subset selection

KW - Computational modeling

KW - Graphics processing units

KW - Instruction sets

KW - Optimization

KW - Runtime

KW - Task analysis

KW - Training

U2 - 10.1109/SSCI.2017.8280984

DO - 10.1109/SSCI.2017.8280984

M3 - Article in proceedings

SP - 1

EP - 8

BT - 2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings

PB - IEEE

T2 - 2017 IEEE Symposium Series on Computational Intelligence (SSCI)

Y2 - 27 November 2017 through 1 December 2017

ER -