Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks

Preprint English OPEN
Hezaveh, Yashar D.; Levasseur, Laurence Perreault; Marshall, Philip J.;
(2017)
  • Related identifiers: doi: 10.1038/nature23463
  • Subject: Astrophysics - Instrumentation and Methods for Astrophysics | Astrophysics - Cosmology and Nongalactic Astrophysics
    arxiv: Astrophysics::Cosmology and Extragalactic Astrophysics

Quantifying image distortions caused by strong gravitational lensing and estimating the corresponding matter distribution in lensing galaxies has been primarily performed by maximum likelihood modeling of observations. This is typically a time and resource-consuming pro... View more
  • References (25)
    25 references, page 1 of 3

    1. Lefor, A. T., Futamase, T. & Akhlaghi, M. A systematic review of strong gravitational lens modeling software. New Astronomy Reviews 57, 1-13 (2013).

    2. LSST Science Collaboration et al. LSST Science Book, Version 2.0. ArXiv e-prints (2009). 0912.0201.

    3. Collett, T. E. The Population of Galaxy-Galaxy Strong Lenses in Forthcoming Optical Imaging Surveys. Astrophys. J. 811, 20 (2015).

    4. Hyva¨rinen, A., Karhunen, J. & Oja, E. Independent Component Analysis. Adaptive and Learning Systems for Signal Processing, Communications and Control Series (Wiley, 2001).

    5. Kormann, R., Schneider, P. & Bartelmann, M. Isothermal elliptical gravitational lens models. Astron. Astrophys. 284, 285-299 (1994).

    6. Russakovsky, O. et al. ImageNet Large Scale Visual Recognition Challenge. ArXiv e-prints (2014). 1409.0575.

    7. Petrillo, C. E. et al. Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks. ArXiv e-prints (2017). 1702.07675.

    8. Jacobs, C., Glazebrook, K., Collett, T., More, A. & McCarthy, C. Finding strong lenses in CFHTLS using convolutional neural networks. ArXiv e-prints (2017). 1704.02744.

    9. Lanusse, F. et al. CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding. ArXiv e-prints (2017). 1703.02642.

    10. Ravanbakhsh, S., Lanusse, F., Mandelbaum, R., Schneider, J. & Poczos, B. Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. ArXiv e-prints (2016). 1609.05796.

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