Deep-learnt classification of light curves
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Deep-learnt classification of light curves. / Mahabal, Ashish; Gieseke, Fabian; Pai, Akshay Sadananda Uppinakudru; Djorgovski, S G ; Drake, A J ; Graham, M J ; CSS/CRTS/PTF Teams.
2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. IEEE, 2017. s. 1-8.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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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
Y1 - 2017
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 -
ID: 195160567