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Title: Automated Blazar Light Curves Using Machine Learning

Abstract

Every night in a remote clearing called Fenton Hill high in the Jemez Mountains of central New Mexico, a bank of robotically controlled telescopes tilt their lenses to the sky for another round of observation through digital imaging. Los Alamos National Laboratory’s Thinking Telescopes project is watching for celestial transients including high-power cosmic flashes called, and like all science, it can be messy work. To keep the project clicking along, Los Alamos scientists routinely install equipment upgrades, maintain the site, and refine the sophisticated machinelearning computer programs that process those images and extract useful data from them. Each week the system amasses 100,000 digital images of the heavens, some of which are compromised by clouds, wind gusts, focus problems, and so on. For a graduate student at the Lab taking a year’s break between master’s and Ph.D. studies, working with state-of-the-art autonomous telescopes that can make fundamental discoveries feels light years beyond the classroom.

Authors:
 [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE; National Aeronautic and Space Administration (NASA)
OSTI Identifier:
1373503
Report Number(s):
LA-UR-17-25456
DOE Contract Number:
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; 97 MATHEMATICS AND COMPUTING

Citation Formats

Johnson, Spencer James. Automated Blazar Light Curves Using Machine Learning. United States: N. p., 2017. Web. doi:10.2172/1373503.
Johnson, Spencer James. Automated Blazar Light Curves Using Machine Learning. United States. doi:10.2172/1373503.
Johnson, Spencer James. Thu . "Automated Blazar Light Curves Using Machine Learning". United States. doi:10.2172/1373503. https://www.osti.gov/servlets/purl/1373503.
@article{osti_1373503,
title = {Automated Blazar Light Curves Using Machine Learning},
author = {Johnson, Spencer James},
abstractNote = {Every night in a remote clearing called Fenton Hill high in the Jemez Mountains of central New Mexico, a bank of robotically controlled telescopes tilt their lenses to the sky for another round of observation through digital imaging. Los Alamos National Laboratory’s Thinking Telescopes project is watching for celestial transients including high-power cosmic flashes called, and like all science, it can be messy work. To keep the project clicking along, Los Alamos scientists routinely install equipment upgrades, maintain the site, and refine the sophisticated machinelearning computer programs that process those images and extract useful data from them. Each week the system amasses 100,000 digital images of the heavens, some of which are compromised by clouds, wind gusts, focus problems, and so on. For a graduate student at the Lab taking a year’s break between master’s and Ph.D. studies, working with state-of-the-art autonomous telescopes that can make fundamental discoveries feels light years beyond the classroom.},
doi = {10.2172/1373503},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jul 27 00:00:00 EDT 2017},
month = {Thu Jul 27 00:00:00 EDT 2017}
}

Technical Report:

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