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    Searching for pulsars using image pattern recognition

    245638_195092.pdf (1.283Mb)
    Access Status
    Open access
    Authors
    Zhu, W.
    Berndsen, A.
    Madsen, E.
    Tan, M.
    Stairs, I.
    Brazier, A.
    Lazarus, P.
    Lynch, R.
    Scholz, P.
    Stovall, K.
    Ransom, S.
    Banaszak, S.
    Biwer, C.
    Cohen, S.
    Dartez, L.
    Flanigan, J.
    Lunsford, G.
    Martinez, J.
    Mata, A.
    Rohr, M.
    Walker, A.
    Allen, B.
    Bhat, Ramesh
    Bogdanova, S.
    Camilo, F.
    Chatterjee, S.
    Cordes, J.
    Crawford, F.
    Deneva, J.
    Desvignes, G.
    Ferdman, R.
    Freire, P.
    Hessels, J.
    Jenet, F.
    Kaplan, D.
    Kaspi, V.
    Knispel, B.
    Lee, K.
    van Leeuwen, J.
    Lyne, A.
    McLaughlin, M.
    Siemens, X.
    Spitler, L.
    Venkataraman, A.
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Zhu, W. and Berndsen, A. and Madsen, E. and Tan, M. and Stairs, I. and Brazier, A. and Lazarus, P. et al. 2014. Searching for pulsars using image pattern recognition. The Astrophysical Journal. 781 (2): pp. 117-129.
    Source Title
    The Astrophysical Journal
    DOI
    10.1088/0004-637X/781/2/117
    ISSN
    0004-637X
    School
    Curtin Institute of Radio Astronomy (Physics)
    Remarks

    © 2014. The American Astronomical Society. All rights reserved.

    URI
    http://hdl.handle.net/20.500.11937/17779
    Collection
    • Curtin Research Publications
    Abstract

    In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful datamining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by 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 interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ~9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI’s performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 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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