D'Isanto, A. and Cavuoti, S. and Brescia, M. and Donalek, C. and Longo, G. and Riccio, G. and Djorgovski, S. G. (2016) An analysis of feature relevance in the classification of astronomical transients with machine learning methods. Monthly Notices of the Royal Astronomical Society, 457 (3). pp. 3119-3132. ISSN 0035-8711. doi:10.1093/mnras/stw157. https://resolver.caltech.edu/CaltechAUTHORS:20160509-092928454
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Abstract
The exploitation of present and future synoptic (multiband and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MultiLayer Perceptron with Quasi Newton Algorithm and K-Nearest Neighbours, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extragalactic objects and identification of supernovae.
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| Additional Information: | © 2016 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. Accepted 2016 January 15. Received 2016 January 15. In original form 2015 October 30. First published online February 20, 2016. The authors wish to thank the anonymous referee for the very helpful comments and suggestions which contributed to optimize the manuscript. The authors wish to acknowledge financial support from the Italian Ministry of University and Research through the grant PRIN-MIUR ‘Cosmology with Euclid’ and from the Keck Institute for Space Studies who sponsored the working group on TDA. MB and SC acknowledge financial contribution from the agreement ASI/INAF I/023/12/1. Authors also wish to thank K. Polsterer for useful discussions. This work made use of the CRTS public archive data, of the CTSCS service and of the DAMEWARE infrastructure. CD and SGD acknowledge a partial support from the NSF grants AST-1413600 and AST-1313422. We are thankful to A. J. Drake, A. A. Mahabal, and M. J. Graham for their key contributions in the CRTS project. | ||||||||||||||
| Group: | Keck Institute for Space Studies | ||||||||||||||
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| Subject Keywords: | methods: data analysis novae, cataclysmic variables supernovae: general stars: variables: general stars: variables: RR Lyrae | ||||||||||||||
| Issue or Number: | 3 | ||||||||||||||
| DOI: | 10.1093/mnras/stw157 | ||||||||||||||
| Record Number: | CaltechAUTHORS:20160509-092928454 | ||||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20160509-092928454 | ||||||||||||||
| Official Citation: | A. D'Isanto, S. Cavuoti, M. Brescia, C. Donalek, G. Longo, G. Riccio, and S. G. Djorgovski An analysis of feature relevance in the classification of astronomical transients with machine learning methods MNRAS (April 11, 2016) Vol. 457 3119-3132 doi:10.1093/mnras/stw157 First published online February 20, 2016 | ||||||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||
| ID Code: | 66731 | ||||||||||||||
| Collection: | CaltechAUTHORS | ||||||||||||||
| Deposited By: | Ruth Sustaita | ||||||||||||||
| Deposited On: | 09 May 2016 17:04 | ||||||||||||||
| Last Modified: | 11 Nov 2021 00:02 |
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