Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning

Thomas, David; Kahn, Steven M.

doi:10.3847/1538-4357/aae7cf

Title: Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning

Journal Article · 15 November 2018 · The Astrophysical Journal (Online)

DOI: https://doi.org/10.3847/1538-4357/aae7cf · OSTI ID:1490420

^[1]; Kahn, Steven M. ^[2]

Stanford Univ., CA (United States). Inst. for Computational and Mathematical Engineering. Kavli Inst. for Particle Astrophysics and Cosmology
Stanford Univ., CA (United States). Kavli Inst. for Particle Astrophysics and Cosmology. Dept. of Physics; SLAC National Accelerator Lab., Menlo Park, CA (United States); LSST Project Office, Tucson, AZ (United States)

We present here a method that enables wide-field ground-based telescopes to scan the sky for subsecond stellar variability. The method has operational and image processing components. The operational component takes star trail images. Each trail serves as a light curve for its corresponding source and facilitates subexposure photometry. We train a deep neural network to identify stellar variability in wide-field star trail images. We use the Large Synoptic Survey Telescope Photon Simulator to generate simulated star trail images and include transient bursts as a proxy for variability. The network identifies transient bursts on timescales down to 10 ms. We argue that there are multiple fields of astrophysics that can be advanced by the unique combination of time resolution and observing throughput that our method offers.

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Research Organization:: SLAC National Accelerator Lab., Menlo Park, CA (United States); Stanford Univ., CA (United States)

Sponsoring Organization:: National Science Foundation (NSF) (United States); USDOE

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 1490420

Journal Information:: The Astrophysical Journal (Online), Journal Name: The Astrophysical Journal (Online) Journal Issue: 1 Vol. 868; ISSN 1538-4357

Publisher:: Institute of Physics (IOP)Copyright Statement

Country of Publication:: United States

Language:: English

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Title: Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning

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