arXiv subjects

Guihong Huang

Publications and source records attributed to Guihong Huang.

7 recordsLinked to original sources

arXiv2026

Refined Global Fit of KamLAND Data and the Daya Bay Antineutrino Energy Spectrum

Recently, the JUNO experiment published its measurement of the solar neutrino oscillation parameters $Δm^2_{21}$ and $\sin^2θ_{12}$ based on 59 days of data, with central values differing by $0.2σ$ from those released by the KamLAND experiment in 2013. Meanwhile, short-baseline reactor neutrino oscillation experiments such as Daya Bay, RENO, and Double Chooz have observed significant deviations between the measured antineutrino spectrum and the Huber-Müller model around 5~MeV. To further investigate the impact of these deviations on the measurement of reactor neutrino oscillation parameters, a global analysis framework is constructed that is weakly dependent on the reactor antineutrino flux model. This framework is based on the independently measured $^{235}\mathrm{U}$ and $^{239}\mathrm{Pu}$ fission antineutrino spectra from the Daya Bay experiment, combined with the public data from KamLAND. First, using the Huber-Müller model, the KamLAND 2013 results are successfully reproduced to within $0.1σ$. Then, replacing the Huber-Müller model with the Daya Bay measured antineutrino spectra in a combined analysis, it is found that the best-fit value of the mass-squared difference $Δm^2_{21}$ decreases from $7.53^{+0.17}_{-0.16}\times10^{-5}\,\mathrm{eV^2}$ to $7.50^{+0.19}_{-0.18}\times10^{-5}\,\mathrm{eV^2}$, while the best-fit value of the mixing angle $\tan^2θ_{12}$ also shows a decreasing trend. This result is in better agreement with the latest JUNO measurement, suggesting that differences in the predicted reactor antineutrino spectra may be an important cause of the tension between the two experiments.

hep-ex↗

arXiv2026

Implementation of a Low-Temperature Monitoring and Alarm System for the Taishan Neutrino Experiment

The Taishan Antineutrino Observatory (TAO) is a near-site experiment for the Jiangmen Underground Neutrino Observatory (JUNO). Its primary goal is to provide a precise reference reactor antineutrino energy spectrum, thereby eliminating the model dependence in reactor neutrino spectrum predictions and enhancing the sensitivity of the neutrino mass ordering measurement. To ensure accurate data acquisition and safe operation of the TAO experiment, a low-temperature monitoring and alarm system has been developed. Built on the Experimental Physics and Industrial Control System (EPICS) framework, the system employs PT100 platinum resistance thermometers embedded in the detector to monitor the temperature of the liquid scintillator. Real-time temperature data are acquired, enabling comprehensive thermal monitoring. The alarm program adopts a trigger-based mechanism with multi-level thresholds, providing instant alerts to operators when the temperature deviates from the safe range. The system has been operating stably for six months, accumulating over one thousand alarm records, and has proven effective in ensuring the safe and stable operation of the experiment.

physics.ins-det↗

arXiv2026

PMT Waveform Simulation and Reconstruction with Conditional Diffusion Network

Photomultiplier tubes (PMTs) are widely employed in particle and nuclear physics experiments. The accuracy of PMT waveform reconstruction directly impacts the detector's spatial and energy resolution. A key challenge arises when multiple photons arrive within a few nanoseconds, making it difficult to resolve individual photoelectrons (PEs). Although supervised deep learning methods have surpassed traditional methods in performance, their practical applicability is limited by the lack of ground-truth PE labels in real data. To address this issue, we propose an innovative weakly supervised waveform simulation and reconstruction approach based on a bidirectional conditional diffusion network framework. The method is fully data-driven and requires only raw waveforms and coarse estimates of PE information as input. It first employs a PE-conditioned diffusion model to simulate realistic waveforms from PE sequences, thereby learning the features of overlapping waveforms. Subsequently, these simulated waveforms are used to train a waveform-conditioned diffusion model to reconstruct the PE sequences from waveforms, reinforcing the learning of features of overlapping waveforms. Through iterative refinement between the two conditional diffusion processes, the model progressively improves reconstruction accuracy. Experimental results demonstrate that the proposed method achieves 99% of the normalized PE-number resolution averaged over 1-5 p.e. and 80% of the timing resolution attained by fully supervised learning.

hep-ex↗

arXiv2025

The High Voltage Splitter board for the JUNO SPMT system

The Jiangmen Underground Neutrino Observatory (JUNO) in southern China is designed to study neutrinos from nuclear reactors and natural sources to address fundamental questions in neutrino physics. Achieving its goals requires continuous operation over a 20-year period. The small photomultiplier tube (small PMT or SPMT) system is a subsystem within the experiment composed of 25600 3-inch PMTs and their associated readout electronics. The High Voltage Splitter (HVS) is the first board on the readout chain of the SPMT system and services the PMTs by providing high voltage for biasing and by decoupling the generated physics signal from the high-voltage bias for readout, which is then fed to the front-end board. The necessity to handle high voltage, manage a large channel count, and operate stably for 20 years imposes significant constraints on the physical design of the HVS. This paper serves as a comprehensive documentation of the HVS board: its role in the SPMT readout system, the challenges in its design, performance and reliability metrics, and the methods employed for production and quality control.

physics.ins-det↗

arXiv2024

Machine-Learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors

Photomultiplier tubes (PMTs) are widely used in particle experiments for photon detection. PMT waveform analysis is crucial for high-precision measurements of the position and energy of incident particles in liquid scintillator (LS) detectors. A key factor contributing to the energy resolution in large liquid scintillator detectors with PMTs is the charge smearing of PMTs. This paper presents a machine-learning-based photon counting method for PMT waveforms and its application to the energy reconstruction, using the JUNO experiment as an example. The results indicate that leveraging the photon counting information from the machine learning model can partially mitigate the impact of PMT charge smearing and lead to a relative 2.0% to 2.8% improvement on the energy resolution in the energy range of [1, 9] MeV.

physics.ins-det↗

arXiv2021

Event vertex and time reconstruction in large volume liquid scintillator detector

Large-volume liquid scintillator detectors with ultra-low background levels have been widely used to study neutrino physics and search for dark matter. Event vertex and event time are not only useful for event selection but also essential for the reconstruction of event energy. In this study, four event vertex and event time reconstruction algorithms using charge and time information collected by photomultiplier tubes were analyzed comprehensively. The effects of photomultiplier tube properties were also investigated. The results indicate that the transit time spread is the main effect degrading the vertex reconstruction, while the effect of dark noise is limited. In addition, when the event is close to the detector boundary, the charge information provides better performance for vertex reconstruction than the time information.

physics.ins-det↗

arXiv2021

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.

physics.ins-det↗