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Background Rejection using Convolutional Neural Networks

Published online by Cambridge University Press:  29 January 2019

Adam Zadrożny  and
Beata Goźlińska
Adam Zadrożny
Affiliation:
Center for Gravitational Wave Astronomy, University of Texas Rio Grande Valley Cavalry 105, One West University Blvd., Brownsville, Texas 78520, USA email: [email protected]
Beata Goźlińska
Affiliation:
Faculty of Physics, University of Warsaw, Krakowskie Przedmiecie 26/28, 00-927 Warszawa, Poland email: [email protected], [email protected]
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Abstract

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The paper presents a proof of concept method of background rejection based on convolutional neural networks (CNN). The method was tested on simulated data and achieved very high accuracy (100%). What is more, method based on CNN is very fast and could be easily applied to wide field surveys. Since early stage results suggest method is very accurate and robust, it could be helpful in creating very low-latency pipelines for EM Follow-up purposes, which will be needed in LIGO-Virgo O3 EM Follow-up.

Keywords

Convolutional Neural NetworksElectromagnetic Follow-upBackground Rejection
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 13 , Symposium S338: Gravitational Wave Astrophysics: Early Results from Gravitational Wave Searches and Electromagnetic Counterparts , October 2017 , pp. 37 - 39
Copyright
Copyright © International Astronomical Union 2019 

References

Abbott, B. P. et al. 2017, Phys. Rev. Lett. 119, 161101Google Scholar
Aniyan, A. K. & Thorat, K., 2017, ApJS 230, 20Google Scholar
Bailey, S. et al. 2008, Astronomische Nachrichten 329 292Google Scholar
Bernlhr, K. et al. 2013, Astroparticle Physics 43 171Google Scholar
Díaz, M. C. et al. 2017, ApJL 848, L29Google Scholar
van Dokkum, P. G. 2001, PASP 113, 1420Google Scholar