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arXiv · 1309.0776

Searching for pulsars using image pattern recognition

W. W. Zhu·A. Berndsen·E. C. Madsen·M. Tan·I. H. Stairs·A. Brazier·P. Lazarus·R. Lynch·P. Scholz·K. Stovall·S. M. Ransom·S. Banaszak·C. M. Biwer·S. Cohen·L. P. Dartez·J. Flanigan·G. Lunsford·J. G. Martinez·A. Mata·M. Rohr·A. Walker·B. Allen·N. D. R. Bhat·S. Bogdanov·F. Camilo·S. Chatterjee·J. M. Cordes·F. Crawford·J. S. Deneva·G. Desvignes·R. D. Ferdman·P. C. C. Freire·J. W. T. Hessels·F. A. Jenet·D. L. Kaplan·V. M. Kaspi·B. Knispel·K. J. Lee·J. van Leeuwen·A. G. Lyne·M. A. McLaughlin·X. Siemens·L. G. Spitler·A. Verkataraman
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Abstract

In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys using image pattern recognition with deep neural nets---the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interferences by looking for patterns from candidate. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of up to thousands pixel of image data. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ~9000 neurons. Different from other pulsar selection programs which use pre-designed patterns, the PICS AI teaches itself the salient features of different pulsars from a set of human-labeled candidates through machine learning. The deep neural networks in this AI system grant it superior ability in recognizing various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates different from the training set. In this completely independent test, PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars, to the top 961 (1%) of 90008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The performance of this system can be improved over time as more training data are accumulated. This AI system has been integrated into the PALFA survey pipeline and has discovered six new pulsars to date.

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W. W. Zhu, A. Berndsen, E. C. Madsen, M. Tan, I. H. Stairs, A. Brazier, P. Lazarus, R. Lynch, P. Scholz, K. Stovall, S. M. Ransom, S. Banaszak, C. M. Biwer, S. Cohen, L. P. Dartez, J. Flanigan, G. Lunsford, J. G. Martinez, A. Mata, M. Rohr, A. Walker, B. Allen, N. D. R. Bhat, S. Bogdanov, F. Camilo, S. Chatterjee, J. M. Cordes, F. Crawford, J. S. Deneva, G. Desvignes, R. D. Ferdman, P. C. C. Freire, J. W. T. Hessels, F. A. Jenet, D. L. Kaplan, V. M. Kaspi, B. Knispel, K. J. Lee, J. van Leeuwen, A. G. Lyne, M. A. McLaughlin, X. Siemens, L. G. Spitler, A. Verkataraman. 2013-12-18. Searching for pulsars using image pattern recognition. https://doi.org/10.1088/0004-637x%2F781%2F2%2F117

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