TY  - JOUR
AU  - HAWC Collaboration (Alfaro, R. et al
AU  - Salesa Greus, F.
PY  - 2022
DA  - 2022//
TI  - Gamma/hadron separation with the HAWC observatory
T2  - Nucl. Instrum. Methods Phys. Res. A
JO  - Nuclear Instruments & Methods in Physics Research A
SP  - 166984 - 13pp
VL  - 1039
PB  - Elsevier
KW  - High energy
KW  - Crab Nebula
KW  - G/H separation
KW  - Machine Learning
AB  - 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.
SN  - 0168-9002
UR  - https://arxiv.org/abs/2205.12188
UR  - https://doi.org/10.1016/j.nima.2022.166984
DO  - 10.1016/j.nima.2022.166984
LA  - English
N1  - WOS:000861747900006
ID  - HAWCCollaborationAlfaro+SalesaGreus2022
ER  -