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	Author	HAWC Collaboration (Alfaro, R. et al); Salesa Greus, F.
	Title	Gamma/hadron separation with the HAWC observatory			Type	Journal Article
	Year	2022	Publication	Nuclear Instruments & Methods in Physics Research A	Abbreviated Journal	Nucl. Instrum. Methods Phys. Res. A
	Volume	1039	Issue		Pages	166984 - 13pp
	Keywords	High energy; Crab Nebula; G/H separation; Machine Learning
	Abstract	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.
	Address	[Alfaro, R.; Angeles Camacho, J. R.; Avila Rojas, D.; Belmont-Moreno, E.; Espinoza, C.; Garcia, D.; Hernandez, S.; Leon Vargas, H.; Sandoval, A.; Serna-Franco, J.] Univ Nacl Autonoma Mexico, Inst Fis, Mexico City, DF, Mexico, Email: tcapistran@astro.unam.mx;
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0168-9002	ISBN		Medium
	Area		Expedition		Conference
	Notes	WOS:000861747900006			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	5371
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