An Intermediate Water Cherenkov Detector at J-PARC

collaborations, Hyper-K

doi:10.7566/jpscp.12.010039

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…A large water tank instrumented with PMTs. This is similar to the TITUS and NuPRISM designs proposed for T2HK [16]. This design could incorporate KM3NeT PMTs as light sensors, thus closely mimicking conditions of the far detector (ORCA).…”

Section: Near Detectormentioning

confidence: 83%

Letter of interest for a neutrino beam from Protvino to KM3NeT/ORCA

et al. 2019

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The Protvino accelerator facility located in the Moscow region, Russia, is in a good position to offer a rich experimental research program in the field of neutrino physics. Of particular interest is the possibility to direct a neutrino beam from Protvino towards the KM3NeT/ORCA detector, which is currently under construction in the Mediterranean Sea 40 km offshore Toulon, France. This proposal is known as P2O. Thanks to its baseline of 2595 km, this experiment would yield an unparalleled sensitivity to matter effects in the Earth, allowing for the determination of the neutrino mass ordering with a high level of certainty after only a few years of running at a modest beam intensity of $$\approx ~90~\hbox {kW}$$≈90kW. With a prolonged exposure ($$\approx 1500\hbox { kW}\,\,\hbox {year}$$≈1500kWyear), a $$2\sigma $$2σ sensitivity to the leptonic CP-violating Dirac phase can be achieved. A second stage of the experiment, comprising a further intensity upgrade of the accelerator complex and a densified version of the ORCA detector (Super-ORCA), would allow for up to a $$6\sigma $$6σ sensitivity to CP violation and a $$10^\circ {-}17^\circ $$10∘-17∘ resolution on the CP phase after 10 years of running with a 450 kW beam, competitive with other planned experiments. The initial composition and energy spectrum of the neutrino beam would need to be monitored by a near detector, to be constructed several hundred meters downstream from the proton beam target. The same neutrino beam and near detector set-up would also allow for neutrino-nucleus cross section measurements to be performed. A short-baseline sterile neutrino search experiment would also be possible.

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Section: Near Detectormentioning

confidence: 83%

Letter of interest for a neutrino beam from Protvino to KM3NeT/ORCA

et al. 2019

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“…The Hyper-Kamiokande (Hyper-K) experiment will use (anti-)ν µ produced from the 1.3MW proton beam at J-PARC to measure the disappearance of (anti-)ν µ and the appearance of (anti-) ν e . The experiment consists of a suite of upgraded T2K near detectors at a baseline of 280 m, a new Intermediate Water Cherenkov Detector (IWCD) [1] at a 1 km baseline and the Hyper-K far detector situated 295 km away. A general overview of Hyper-K sensitivities is given in ref.…”

Section: Introductionmentioning

confidence: 99%

Long-baseline neutrino oscillation sensitivities with Hyper-Kamiokande and impact of Intermediate Water Cherenkov Detector

Zhu¹

2023

Proceedings of Neutrino Oscillation Workshop — PoS(NOW2022)

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Hyper-Kamiokande is a next-generation, long-baseline neutrino experiment under construction in Japan. It will measure neutrino oscillations using the J-PARC neutrino beam which is now serving the T2K experiment. The Hyper-Kamiokande far detector has a fiducial volume 8 times the size of the currently-running Super-Kamiokande detector, and will be instrumented with new photosensors. Combined with an upgraded J-PARC neutrino beam, Hyper-Kamiokande will be able to measure neutrino oscillations with unprecedented statistical precision, enabling the potential discovery of CP-violation and precise measurements of the atmospheric oscillation parameters. The Hyper-K sensitivity for a 10-year exposure shows 5-σ significance for the discovery of CP violation for > 55% of true δ CP values and determination of the sin 2 θ 23 octant for values outside (0.44, 0.58). This proceeding describes the Hyper-K analysis strategy including upgraded near detectors and the newly proposed Intermediate Water Cherenkov Detector (IWCD). The ability of these upgrades to minimize systematic uncertainties will be described, with emphasis on their effect on the neutrino oscillation sensitivity of Hyper-K.

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“…The technology in neutrino long baseline experiments has become sophisticated enough that "systematic uncertainties will dominate over statistical uncertainties when measuring neutrino disappearance" [21]. SK itself has an approximate 7% systematic uncertainty event rate prediction for neutrinos launched from the J-PARC particle accelerator.…”

Section: Water Cherenkov Detectorsmentioning

confidence: 99%

“…Given its increased size, HK requires lower systematic uncertainty to progress in the new physics it seeks to study. For example, HK requires a 3% systematic uncertainty rate to acheive sufficient certainty on CP (charge parity symmetry) violation measurements [21]. Abe et al [22] have shown that the addition of a near detector close to (1km or 2km nearby) the neutrino beam source can dramatically lower the event uncertainty on the far detector.…”

Section: Water Cherenkov Detectorsmentioning

confidence: 99%

Using Machine Learning to Improve Neutron Tagging Efficiency in Water Cherenkov Detectors

Stubbs¹

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When an anti-neutrino collides with a proton in the atomic nucleus, it yields an anti-lepton and a free neutron. In a water Cherenkov neutrino detector like Super-K or the next generation Hyper-K, the free neutron is captured by a hydrogen or gadolinium nucleus about one hundred microseconds after the collision. The low-energy signal from the neutron capture (ranging from 2-8 MeV of gamma rays) is recorded by only tens of photomultiplier tubes (PMTs), making neutron captures difficult to distinguish from radioactive decay, muon spallation and other background sources. Improved methodologies for neutron tagging can advance understanding and enable new research over a survey of topics in particle physics. In this research, machine learning techniques are employed to optimize the neutron capture detection capability in the new intermediate water Cherenkov detector (IWCD) for Hyper-K. In particular, boosting decision tree (XGBoost) and graph neural network (GCN, DGCNN) models are developed and benchmarked against a statistical likelihood-based approach, achieving up to a 10% increase in classification accuracy. Characteristic features are also engineered from the datasets and analyzed using SHAP (SHapley Additive exPlanations) to provide insight into the pivotal factors influencing event type outcomes. Three main datasets were used for evaluative purposes in this research, each consisting of roughly 1.6 million events in total, divided nearly evenly between neutron capture and a distinct background electron source.

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An Intermediate Water Cherenkov Detector at J-PARC

Cited by 3 publications

References 12 publications

Letter of interest for a neutrino beam from Protvino to KM3NeT/ORCA

Letter of interest for a neutrino beam from Protvino to KM3NeT/ORCA

Long-baseline neutrino oscillation sensitivities with Hyper-Kamiokande and impact of Intermediate Water Cherenkov Detector

Using Machine Learning to Improve Neutron Tagging Efficiency in Water Cherenkov Detectors

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