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

Abstract

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.

Authors:
ORCiD logo [1];  [2]
  1. Stanford Univ., CA (United States). Inst. for Computational and Mathematical Engineering. Kavli Inst. for Particle Astrophysics and Cosmology
  2. 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)
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States); Stanford Univ., CA (United States)
Sponsoring Org.:
USDOE; National Science Foundation (NSF)
OSTI Identifier:
1490420
Grant/Contract Number:  
AC02-76SF00515; 1258333
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
The Astrophysical Journal (Online)
Additional Journal Information:
Journal Volume: 868; Journal Issue: 1; Journal ID: ISSN 1538-4357
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; observational methods; image processing techniques; photometric techniques

Citation Formats

Thomas, David, and Kahn, Steven M. Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning. United States: N. p., 2018. Web. doi:10.3847/1538-4357/aae7cf.
Thomas, David, & Kahn, Steven M. Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning. United States. doi:10.3847/1538-4357/aae7cf.
Thomas, David, and Kahn, Steven M. Fri . "Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning". United States. doi:10.3847/1538-4357/aae7cf.
@article{osti_1490420,
title = {Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning},
author = {Thomas, David and Kahn, Steven M.},
abstractNote = {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.},
doi = {10.3847/1538-4357/aae7cf},
journal = {The Astrophysical Journal (Online)},
issn = {1538-4357},
number = 1,
volume = 868,
place = {United States},
year = {2018},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on November 16, 2019
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