On simulated SWGO events, graph neural networks outperform LIC/PINCness observables in gamma/hadron separation and template-based methods in energy resolution.
HAWC: Design, Operation, Reconstruction and Analysis
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abstract
The High-Altitude Water Cherenkov (HAWC) Observatory was completed and began full opera- tion on March 20, 2015. The detector consists of an array of 300 water tanks, each containing 200 ktons of purified water and instrumented with 4 PMTs. Located at an elevation of 4100m a.s.l. near the Sierra Negra volcano in central Mexico, HAWC has a threshold for gamma-ray detection well below 1 TeV and a sensitivity to TeV-scale gamma-ray sources an order of magnitude better than previous air-shower arrays. The detector operates 24 hours/day and observes the overhead sky (2 sr), making it an ideal survey instrument. We describe the configuration of HAWC with an emphasis on how the design was optimized, describe the data acquired, reconstructed and an- alyzed. Finally, we will demonstrate the sensitivity of the detector using the observation of the Crab. This paper serves as a detailed technical description of the foundations of the numerous analyses presented at this meeting by members of the HAWC collaboration.
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2024 1verdicts
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Application of Graph Networks to a wide-field Water-Cherenkov-based Gamma-Ray Observatory
On simulated SWGO events, graph neural networks outperform LIC/PINCness observables in gamma/hadron separation and template-based methods in energy resolution.