Gamma/Hadron Separation with the HAWC Observatory. (arXiv:2205.12188v1 [astro-ph.HE])
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The High Altitude Water Cherenkov (HAWC) gamma-ray observatory observes
atmospheric showers produced by incident gamma rays and cosmic rays with energy
from 300 GeV to more than 100 TeV. A crucial phase in analyzing gamma-ray
sources using ground-based gamma-ray detectors like HAWC is to identify the
showers produced by gamma rays or hadrons. The HAWC observatory records roughly
25,000 events per second, with hadrons representing the vast majority
($>99.9%$) of these events. The standard gamma/hadron separation technique in
HAWC uses a simple rectangular cut involving only two parameters. This work
describes the implementation of more sophisticated gamma/hadron separation
techniques, via machine learning methods (boosted decision trees and neural
networks), and summarizes the resulting improvements in gamma/hadron separation
obtained in HAWC.

The High Altitude Water Cherenkov (HAWC) gamma-ray observatory observes
atmospheric showers produced by incident gamma rays and cosmic rays with energy
from 300 GeV to more than 100 TeV. A crucial phase in analyzing gamma-ray
sources using ground-based gamma-ray detectors like HAWC is to identify the
showers produced by gamma rays or hadrons. The HAWC observatory records roughly
25,000 events per second, with hadrons representing the vast majority
($>99.9%$) of these events. The standard gamma/hadron separation technique in
HAWC uses a simple rectangular cut involving only two parameters. This work
describes the implementation of more sophisticated gamma/hadron separation
techniques, via machine learning methods (boosted decision trees and neural
networks), and summarizes the resulting improvements in gamma/hadron separation
obtained in HAWC.

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