Large Liquid Scintillator Detector Research Articles

Search for charged excited states of dark matter with KamLAND-Zen

Particle dark matter could belong to a multiplet that includes an electrically charged state. WIMP dark matter (χ0) accompanied by a negatively charged excited state (χ−) with a small mass difference (e.g. < 20 MeV) can form a bound-state with a nucleus such as xenon. This bound-state formation is rare and the released energy is O(1−10) MeV depending on the nucleus, making large liquid scintillator detectors suitable for detection. We searched for bound-state formation events with xenon in two experimental phases of the KamLAND-Zen experiment, a xenon-doped liquid scintillator detector. No statistically significant events were observed. For a benchmark parameter set of WIMP mass mχ0=1 TeV and mass difference Δm=17 MeV, we set the most stringent upper limits on the recombination cross section times velocity 〈σv〉 and the decay-width of χ− to 9.2×10−30cm3/s and 8.7×10−14 GeV, respectively at 90% confidence level.

Optimized α/β pulse shape discrimination in Borexino

Borexino could efficiently distinguish between α and β radiation in its liquid scintillator by the characteristic time profile of its scintillation pulse. This α/β discrimination, first demonstrated on the ton scale in the counting test facility prototype, was used throughout the lifetime of the experiment between 2007 and 2021. With this method, the α events are identified and subtracted from the solar neutrino events similar to β. This is particularly important in liquid scintillators, as the α scintillation is strongly quenched. In Borexino, the prominent Po210 decay peak was a background in the energy range of electrons scattered from Be7 solar neutrinos. Optimal α/β discrimination was achieved with a , with a higher ability to leverage the timing information of the scintillation photons detected by the photomultiplier tubes. An event-by-event, high efficiency, stable, and uniform pulse shape discrimination was essential in characterizing the spatial distribution of background in the detector. This benefited most Borexino measurements, including solar neutrinos in the pp chain and the first direct observation of the CNO cycle in the Sun. This paper presents key milestones in α/β discrimination in Borexino as a term of comparison for current and future large liquid scintillator detectors. Published by the American Physical Society 2024

First attempt of directionality reconstruction for atmospheric neutrinos in a large homogeneous liquid scintillator detector

The directionality information of incoming neutrinos is essential to atmospheric neutrino oscillation analysis since it is directly related to the oscillation baseline length. Large homogeneous liquid scintillator detectors, while offering excellent energy resolution, are traditionally very limited in their capabilities of measuring event directionality. In this paper, we present a novel directionality reconstruction method for atmospheric neutrino events in large homogeneous liquid scintillator detectors based on waveform analysis and machine learning techniques. We demonstrate for the first time that such detectors can achieve good direction resolution and potentially play an important role in future atmospheric neutrino oscillation measurements. Published by the American Physical Society 2024

Flavor identification of atmospheric neutrinos in JUNO with machine learning

The Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine neutrino mass ordering (NMO) using a large liquid scintillator detector located in southern China. While JUNO's NMO sensitivity mostly comes from reactor neutrinos, atmospheric neutrino oscillations in JUNO can provide complimentary sensitivity via matter effects, and enhance its overall sensitivity in a joint analysis. Neutrino flavor identification is crucial to atmospheric neutrino oscillation measurements, but is traditionally a very difficult task in liquid scintillator detectors such as JUNO. In this paper, we present a novel method for the flavor identification of atmospheric neutrinos in JUNO with machine learning techniques. This method takes features from photomultiplier tube waveforms as inputs, and shows promising results with JUNO simulation. This method could also be applied to other liquid scintillator detectors, potentially benefiting other future atmospheric neutrino oscillation experiments.

Environmental radon control in the 700 m underground laboratory at JUNO

The Jiangmen Underground Neutrino Observatory is constructing the world’s largest liquid scintillator detector, with a 20 kt target mass and approximately 700 m of overburden. The total underground space of civil construction is around 300,000 m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}, with the main hall comprising about 120,000 m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}, making it the largest experimental hall in the world. Maintaining a low radon concentration in the underground air is crucial for both human health and the accuracy of experiments involving rare decay detection, such as neutrino and dark matter experiments. To ensure human health and the integrity of neutrino physics experiments, the nominal radon concentration in the main hall must be kept below 200 Bq/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document} with a maximum value below 400 Bq/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}. Introduction of fresh air from above ground can significantly lower radon concentration. A benchmark experiment conducted in the refuge room near the main hall revealed that the radon emanating from underground water is a significant source of radon in the underground air. The total underground ventilation rate is approximately 160,000 m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}/h of fresh air with about 30 Bq/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}222\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{222}$$\\end{document}Rn from the bottom of the vertical tunnel after the installation of powerful fans. Of this, 55,000 m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}/h is used for ventilation in the main hall. As a result of these measures, the radon concentration inside the main hall has decreased from 1600 Bq/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document} to below 200 Bq/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document} under stable working conditions, with exceptions during rare adverse weather events or fan failures. The employed strategies to control radon concentration in the underground air are described in this paper.

JUNO sensitivity on proton decay p → ν K + searches* *We are grateful for the ongoing cooperation from the China General Nuclear Power Group. This study was supported by the Chinese Academy of Sciences, the National Key R&D Program of China, the CAS Center for Excellence in Particle Physics, Wuyi University, and the Tsung-Dao Lee Institute of Shanghai Jiao

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via is 36.9% ± 4.9% with a background level of events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies.

Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors

Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors

Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach

Large-scale detectors consisting of a liquid scintillator target surrounded by an array of photo-multiplier tubes (PMTs) are widely used in the modern neutrino experiments: Borexino, KamLAND, Daya Bay, Double Chooz, RENO, and the upcoming JUNO with its satellite detector TAO. Such apparatuses are able to measure neutrino energy which can be derived from the amount of light and its spatial and temporal distribution over PMT channels. However, achieving a fine energy resolution in large-scale detectors is challenging. In this work, we present machine learning methods for energy reconstruction in the JUNO detector, the most advanced of its type. We focus on positron events in the energy range of 0–10 MeV which corresponds to the main signal in JUNO – neutrinos originated from nuclear reactor cores and detected via the inverse beta decay channel. We consider the following models: Boosted Decision Trees and Fully Connected Deep Neural Network, trained on aggregated features, calculated using the information collected by PMTs. We describe the details of our feature engineering procedure and show that machine learning models can provide the energy resolution sigma = 3% at 1 MeV using subsets of engineered features. The dataset for model training and testing is generated by the Monte Carlo method with the official JUNO software.

Verification of the calibration method for the boundary effect in JUNO

Jiangmen Underground Neutrino Observatory (JUNO) is constructed to probe multiple physics goals, including determining the neutrino mass ordering (MO), measuring solar neutrinos, detecting supernova neutrinos, etc. A large Liquid Scintillator (LS) detector with a high energy resolution is being built to determine the neutrino MO within 6-year of data taking. To accomplish this goal, a calibration complex has been designed. Concerning the energy leakage near the LS boundary, a Guide Tube Calibration System (GTCS) will be deployed on the outer surface of the JUNO central detector to help better understand the boundary effect. A fitting algorithm has been presented during the design of GTCS considering the overlap of full-energy peak and Compton tail. In this paper, we verify the fitting algorithm experimentally, using a radioactive source in the Daya Bay experiment. The maximum bias of full-energy peak for the experiment at Daya Bay is less than 1.0% even if a change of ±20.0% in acrylic density was assumed. Besides, the tuned acrylic density is applied in JUNO GTCS simulation. The maximum uncertainty estimation of GTCS energy response in total is 0.52%, satisfying the JUNO GTCS requirement which is 1.0%.

Status of the Jiangmen Underground Neutrino Observatory

The Jiangmen Underground Neutrino observatory (JUNO) experiment uses a large liquid scintillator detector to detect electron antineutrinos issued from nuclear reactors at a distance of 53 km. Its main goal is to determine the neutrino mass hierarchy and precisely measure some of the oscillation parameters. The detector will be located 700 m underground and will consist of 20 ktons of liquid scintillator contained in a 35 m diameter acrylic sphere, instrumented with 17612 (20-inch) and 25600 (3-inch) photomultiplier tubes (PMT), respectively. It will achieve an unprecedented 3% energy resolution (at 1 MeV). The objective is to detect 100000 events after 6 years of data taking. Two independent veto systems are foreseen to reduce the different backgrounds. A 40 kton ultrapure water Cherenkov pool instrumented with 2400 20-inch PMTs surrounds the central detector. It will tag events coming from outside the neutrino target. It will also act as a passive shielding for neutrons and gammas. In addition, a muon tracker will be installed on top of the detector (top muon veto) in order to tag cosmic muons and validate the muon track reconstruction.

Improvement of machine learning-based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs

Improvement of machine learning-based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs

Large-energy single hits at JUNO from atmospheric neutrinos and dark matter

Large liquid scintillator detectors, such as JUNO, present a new opportunity to study neutral current events from the low-energy end of the atmospheric neutrinos, and possible new physics signals due to light dark matter. We carefully study the possibility of detecting ``large-energy singles'' (LES), i.e., events with visible scintillation energy $&gt;15\text{ }\text{ }\mathrm{MeV}$, but no other associated tags. For an effective exposure of $20\text{ }\text{ }\mathrm{kton}\ensuremath{-}\mathrm{yr}$ and considering only Standard Model physics, we expect the LES sample to contain $\ensuremath{\sim}40$ events from scattering on free protons and $\ensuremath{\sim}108$ events from interaction with carbon, from neutral-current interactions of atmospheric neutrinos. Backgrounds, largely due to $\ensuremath{\beta}$ decays of cosmogenic isotopes, are shown to be significant only below 15 MeV visible energy. The LES sample at JUNO can competitively probe a variety of new physics scenarios, such as boosted dark matter and annihilation of galactic dark matter to sterile neutrinos.

Alternative design for large scale liquid scintillator detectors

The construction of large-scale liquid scintillator detectors is complicated by the need to separate the scintillation region from photomultiplier tubes (PMTs) due to their intrinsic radioactivity. This is generally done using acrylic or nylon barriers, whose own activity can also lead to substantial reduction of the fiducial detection volume for a number of low energy (MeV) studies, and whose construction becomes increasingly difficult and expensive for larger detector volumes. Here we present an initial simulation study of an alternative geometric construction known as SLIPS (Stratified LIquid Plane Scintillator), which aims to avoid such physical barriers entirely by instead using layers of lipophobic liquids to separate PMTs from the scintillation region.

Prospects for searching new Double Beta Decay physics with KamLAND-Zen 800

Two-neutrino double beta decay (2υββ) is a rare second-order weak radioactive decay process. It has been observed in neutrinoless double beta decay (0υββ) search experiments. Precise observation of 2υββ is essential to reduce the theoretical uncertainty in the calculation of nuclear matrix elements required to obtain the effective Majorana mass from a lifetime of 0υββ. Also, 2υββ itself is interesting because new physics could be hiding in the energy spectrum, such as for example, Majoron emission mode 0υββ and 2υββ with neutrino self-interaction. KamLAND-Zen 800 is an experiment to search for 0υββ of 136Xe with a large ultra-pure liquid scintillator detector, KamLAND. KamLAND-Zen 800 has been observing 745 kg of xenon gas at 91% enriched in 136Xe since 2019, providing a high statistics 2υββ decay sample. We describe the potential for new physics searches in 2υββ with KamLAND-Zen 800.

Unambiguously Resolving the Potential Neutrino Magnetic Moment Signal at Large Liquid Scintillator DetectorsSupported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA10010800). DLX and ZPY are grateful to the Double First Class Start-up Fund (WF220442603) provided by Shanghai Jiao Tong University. DLX also thanks support from the CAS Center for Excellence in Particle Physics

Non-vanishing electromagnetic properties of neutrinos have been predicted by many theories beyond the Standard Model, and an enhanced neutrino magnetic moment can have profound implications for fundamental physics. The XENON1T experiment recently detected an excess of electron recoil events in the 1–7 keV energy range, which can be compatible with solar neutrino magnetic moment interaction at a most probable value of μν = 2.1 × 10−11 μ B. However, tritium backgrounds or solar axion interaction in this energy window are equally plausible causes. Upcoming multi-tonne noble liquid detectors will test these scenarios more in depth, but will continue to face similar ambiguity. We report a unique capability of future large liquid scintillator detectors to help resolve the potential neutrino magnetic moment scenario. With O(100) kton⋅year exposure of liquid scintillator to solar neutrinos, a sensitivity of μν < 10−11 μ B can be reached at an energy threshold greater than 40 keV, where no tritium or solar axion events but only neutrino magnetic moment signal is still present.

The study of active geomagnetic shielding coils system for JUNO

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator (LS) detector for neutrino mass ordering and other neutrino physics research. The detector uses large-size 20 inches photomultiplier tubes to detect photons from a liquid scintillator. The large PMTs are sensitive and easily affected such that the detection efficiency loses about 60% under the geomagnetic field intensity (∼500 mG). It has a significantly negative effect on the detector performance, and a compensation system is necessary for geomagnetic field shielding. As permalloys are easily rusted in water, a better way for the geomagnetic shielding is to apply an active compensation coils system. The simulations show that a set of 32 circular coils can meet the experiment requirement. The residual magnetic field is less than 0.05 G in the Central Detector Photomultiplier Tube (CD-PMT) region (38.5–39.5 m in diameter). A prototype coil system with a 1.2 m was built to validate the simulation and the design. The measured data of prototype and simulation results are consistent with each other, and geomagnetic field intensity is effectively reduced by coils, verifying the shielding coils system design for JUNO. This study is expected to provide practical guidance for the PMT magnetic field shielding for future large-scale detector designs.

Study of Using Machine Learning for Level 1 Trigger Decision in JUNO Experiment

A study on the use of a machine learning algorithm for the level 1 trigger decision in the JUNO experiment ispresented. JUNO is a medium baseline neutrino experiment in construction in China, with the main goal of determining the neutrino mass hierarchy. A large liquid scintillator (LS)volume will detect the electron antineutrinos issued from nuclear reactors. The LS detector is instrumented by around 20000 large photomultiplier tubes. The hit information from each PMT will be collected into a center trigger unit for the level 1 trigger decision. The current trigger algorithm used to select a neutrino signal event is based on a fast vertex reconstruction. We propose to study an alternative level 1 (L1) trigger in order to achieve a similar performance as the vertex fitting trigger but with less logic resources by using firmware implemented machine learning model at the L1 trigger level. We treat the trigger decision as a classification problem and train a Multi-Layer Perceptron (MLP)model to distinguish the signal events with an energy higher than a certain threshold from noise events. We use JUNO software to generate datasets which include 100K physics events with noise and 100K pure noise events coming from PMT dark noise.For events with energy higher than 100 keV, the L1 trigger based on the converged MLP model can achieve an efficiency higher than 99%. After the training performed on simulations,we successfully implemented the trained model into a Kintex 7FPGA. We present the technical details of the neural network development and training, as well as its implementation in the hardware with the FPGA programming. Finally the performance of the L1 trigger MLP implementation is discussed.

Improving the energy uniformity for large liquid scintillator detectors

It is challenging to achieve high precision energy resolution for large liquid scintillator detectors. Energy non-uniformity is one of the main obstacles. To surmount it, a calibration-data driven method was developed previously to reconstruct event energy in the JUNO experiment. In this paper, we investigated the choice of calibration sources thoroughly, optimized the calibration positions and corrected the residual detector azimuthal asymmetry. All these efforts lead to a reduction of the energy non-uniformity near the detector boundary, from about 0.64% to 0.38%. And within the fiducial volume of the detector it is improved from 0.3% to 0.17%. As a result the energy resolution could be further improved.

Neutral-current background induced by atmospheric neutrinos at large liquid-scintillator detectors. II. Methodology for in situ measurements

Future large liquid-scintillator (LS) detectors are competitive with and complementary to the water-Cherenkov detectors on the searches for diffuse supernova neutrino background and nucleon decay. In a companion paper, we have performed a systematic calculation of the neutral-current (NC) background induced by atmospheric neutrino interactions on $^{12}\mathrm{C}$ nuclei in LS detectors, which are expected to be crucially important for the experimental searches for the diffuse supernova neutrino background and nucleon decay. In this paper, we perform a systematic study on the measurement of the NC background and evaluate the associated uncertainties. We first exploit the characteristics of the NC background, in particular, the multiplicities of neutrons and pions, and the possible association with unstable residual nuclei. It turns out that the neutron multiplicity distribution is very powerful to discriminate among different models. Then, we develop a maximum-likelihood method to allow an in situ measurement of the NC interactions with a triple-coincidence signature. Finally, a data-driven approach is proposed to evaluate the uncertainty of the NC background in the search for the diffuse supernova neutrino background. We conclude that future large LS experiments like the Jiangmen Underground Neutrino Observatory will be able to make a unique contribution to the worldwide dataset to improve the prediction of atmospheric neutrino NC interactions on $^{12}\mathrm{C}$.

Neutral-current background induced by atmospheric neutrinos at large liquid-scintillator detectors. I. Model predictions

The experimental searches for diffuse supernova neutrino background and proton decay in next-generation large liquid-scintillator (LS) detectors are competitive with and complementary to those in the water-Cherenkov detectors. In this paper, we carry out a systematic study of the dominant background induced by atmospheric neutrinos via their neutral-current (NC) interactions with the $^{12}{\rm C}$ nuclei in the LS detectors. The atmospheric neutrino fluxes at the location of Jiangmen Underground Neutrino Observatory (JUNO) are used, as the JUNO detector is obviously a suitable representative for future LS detectors. Then, we implement the sophisticated generators \texttt{GENIE} and \texttt{NuWro} to simulate the neutrino interactions with the carbon nuclei, and the package \texttt{TALYS} to deal with the deexcitations of final-state nuclei. Finally, the event rates for the production of additional nucleons, $\gamma$'s, $\alpha$'s, pions and kaons are obtained and categorized, and the systematic uncertainty of the NC background represented by a variety of data-driven nuclear models is estimated. The implications of the NC background from atmospheric neutrinos for the detection of diffuse supernova neutrino background and proton decay are also discussed.