The High-Altitude Water Cherenkov (HAWC) Observatory is used for detecting TeV gamma rays. HAWC is operating at 4,100 meters above level sea on the slope of the Sierra Negra Volcano in the State of Puebla, Mexico, and consists of an array of 300 water Cherenkov detectors (WCDs) covering an area of 22,000 m 2 . Each WCD is equipped with four photomultiplier tubes (PMTs) to detect Cherenkov emission in the water from secondary particles of extensive air-shower (EAS) that are produced in the interactions of primary particles (gamma rays or charged cosmic rays) in the atmosphere. HAWC is able to reconstruct the EAS in the 0.5 to 100 TeV energy range. In order to improve the core determination for events with high energy (> 10 TeV) when the events arrive outside of the HAWC array, the Outrigger upgrade project is adding 350 small WCDs around the main array. These outrigger tanks each have one PMT in a 1.5 meter diameter cylindrical polyethylene tank, covering a total area four times larger than that of the HAWC array. In this work we present leak light testing to identify the stability of the detector and an analysis of deposited charges to understand the detector performance.Stability and behavior of the array of outriggers in the HAWC observatory T. Capistrán
The High-Altitude Water Cherenkov experiment (HAWC) observatory is located 4100 meters above sea level. HAWC is able to detect secondary particles from extensive air showers (EAS) initiated in the interaction of a primary particle (either a gamma or a charged cosmic ray) with the upper atmosphere. Because an overwhelming majority of EAS events are triggered by cosmic rays, background noise suppression plays an important role in the data analysis process of the HAWC observatory. Currently, HAWC uses cuts on two parameters (whose values depend on the spatial distribution and luminosity of an event) to separate gamma-ray events from background hadronic showers. In this work, a search for additional gamma-hadron separation parameters was conducted to improve the efficiency of the HAWC background suppression technique. The bestperforming parameters were integrated to a feed-foward Multilayer Perceptron Neural Network (MLP-NN), along with the traditional parameters. Various iterations of MLP-NN's were trained on Monte Carlo data, and tested on Crab data. Preliminary results show that the addition of new parameters can improve the significance of the point source at high-energies (~TeV), at the expense of slightly worse performance in conventional low-energy bins (~GeV). Further work is underway to improve the efficiency of the neural network at low energies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.