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Register a new userStudy of wavelength-shifting chemicals for use in large-scale water Cherenkov detectors
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Cherenkov detectors employ various methods to maximize light collection at the photomultiplier tubes (PMTs). These generally involve the use of highly reflective materials lining the interior of the detector, reflective materials around the PMTs, or wavelength-shifting sheets around the PMTs. Recently, the use of water-soluble wavelength-shifters has been explored to increase the measurable light yield of Cherenkov radiation in water. These wave-shifting chemicals are capable of absorbing light in the ultravoilet and re-emitting the light in a range detectable by PMTs. Using a 250 L water Cherenkov detector, we have characterized the increase in light yield from three compounds in water: 4-Methylumbelliferone, Carbostyril-124, and Amino-G Salt. We report the gain in PMT response at a concentration of 1 ppm as: 1.88 $pm$ 0.02 for 4-Methylumbelliferone, stable to within 0.5% over 50 days, 1.37 $pm$ 0.03 for Carbostyril-124, and 1.20 $pm$ 0.02 for Amino-G Salt. The response of 4-Methylumbelliferone was modeled, resulting in a simulated gain within 9% of the experimental gain at 1 ppm concentration. Finally, we report an increase in neutron detection performance of a large-scale (3.5 kL) gadolinium-doped water Cherenkov detector at a 4-Methylumbelliferone concentration of 1 ppm.
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The application of machine learning techniques to the reconstruction of lepton energies in water Cherenkov detectors is discussed and illustrated for TITUS, a proposed intermediate detector for the Hyper-Kamiokande experiment. It is found that applying these techniques leads to an improvement of more than 50% in the energy resolution for all lepton energies compared to an approach based upon lookup tables. Machine learning techniques can be easily applied to different detector configurations and the results are comparable to likelihood-function based techniques that are currently used.
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