Speaker
Description
Neutrinos emitted from past supernovae are known as Supernova Relic Neutrinos (SRNs). Since the prediction of SRN flux relies on astrophysical inputs such as the supernova rate and cosmic star formation history, the detection of SRNs is expected to provide complementary information to refine these models. KamLAND, a 1-kiloton liquid scintillator, detects electron antineutrinos via inverse beta decay using delayed-coincidence method. Due to its high sensitivity in the $\sim 10\,$MeV energy region, KamLAND has a significant advantage in SRN observation. The dominant background in this energy region arises from neutral current interaction of atmospheric neutrinos. To reduce this background, we are developing a background reduction method using a deep neural network, which exploits differences in the spatiotemporal dispersion of hit information in the photomultiplier tubes. In this presentation, we report on the progress of this background reduction method for SRN search. Additionally, we present the latest status of pre-supernova neutrino alarm system and the ongoing search of neutrinos from primordial black holes, a dark matter candidate and a possible neutrino emitter.
| Collaboration(s) | KamLAND collaboration |
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