An Automated Bolide Detection Pipeline for GOES GLM. (arXiv:2106.09189v1 [astro-ph.EP])
<a href="http://arxiv.org/find/astro-ph/1/au:+Smith_J/0/1/0/all/0/1">Jeffrey C. Smith</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Morris_R/0/1/0/all/0/1">Robert L. Morris</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Rumpf_C/0/1/0/all/0/1">Clemens Rumpf</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Longenbaugh_R/0/1/0/all/0/1">Randolph Longenbaugh</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+McCurdy_N/0/1/0/all/0/1">Nina McCurdy</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Henze_C/0/1/0/all/0/1">Christopher Henze</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Dotson_J/0/1/0/all/0/1">Jessie Dotson</a>
The Geostationary Lightning Mapper (GLM) instrument onboard the GOES 16 and
17 satellites has been shown to be capable of detecting bolides (bright
meteors) in Earth’s atmosphere. Due to its large, continuous field of view and
immediate public data availability, GLM provides a unique opportunity to detect
a large variety of bolides, including those in the 0.1 to 3 m diameter range
and complements current ground-based bolide detection systems, which are
typically sensitive to smaller events. We present a machine learning-based
bolide detection and light curve generation pipeline being developed at NASA
Ames Research Center as part of NASA’s Asteroid Threat Assessment Project
(ATAP). The ultimate goal is to generate a large catalog of calibrated bolide
lightcurves to provide an unprecedented data set which will be used to inform
meteor entry models on how incoming bodies interact with the Earth’s atmosphere
and to infer the pre-entry properties of the impacting bodies. The data set
will also be useful for other asteroidal studies. This paper reports on the
progress of the first part of this ultimate goal, namely, the automated bolide
detection pipeline. Development of the training set, ML model training and
iterative improvements in detection performance are presented. The pipeline
runs in an automated fashion and bolide lightcurves along with other measured
properties are promptly published on a NASA hosted publicly accessible website,
https://neo-bolide.ndc.nasa.gov.
The Geostationary Lightning Mapper (GLM) instrument onboard the GOES 16 and
17 satellites has been shown to be capable of detecting bolides (bright
meteors) in Earth’s atmosphere. Due to its large, continuous field of view and
immediate public data availability, GLM provides a unique opportunity to detect
a large variety of bolides, including those in the 0.1 to 3 m diameter range
and complements current ground-based bolide detection systems, which are
typically sensitive to smaller events. We present a machine learning-based
bolide detection and light curve generation pipeline being developed at NASA
Ames Research Center as part of NASA’s Asteroid Threat Assessment Project
(ATAP). The ultimate goal is to generate a large catalog of calibrated bolide
lightcurves to provide an unprecedented data set which will be used to inform
meteor entry models on how incoming bodies interact with the Earth’s atmosphere
and to infer the pre-entry properties of the impacting bodies. The data set
will also be useful for other asteroidal studies. This paper reports on the
progress of the first part of this ultimate goal, namely, the automated bolide
detection pipeline. Development of the training set, ML model training and
iterative improvements in detection performance are presented. The pipeline
runs in an automated fashion and bolide lightcurves along with other measured
properties are promptly published on a NASA hosted publicly accessible website,
https://neo-bolide.ndc.nasa.gov.
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