arXiv subjects

Yongpeng Zhang

Publications and source records attributed to Yongpeng Zhang.

15 recordsLinked to original sources

arXiv2026

Reconstruction of the Effective Energy-deposition Vertex of Muon Showers using PMT Waveform in a Large-scale Liquid Scintillator Detector

Cosmogenic muon-induced radioactive isotopes pose a significant background source for deep-underground low-background experiments. Although rock overburdens at underground sites substantially attenuate the cosmogenic muon flux, residual muon-induced backgrounds still require active suppression. For future multi-kiloton liquid scintillator (LS) detectors, such as the Jiangmen Underground Neutrino Observatory (JUNO), shower muons contribute to more than 88\% of all muon-induced isotopes. Consequently, precise reconstruction of shower vertices is essential for implementing localized spatial vetoes. We propose a novel waveform-based method to reconstruct the shower vertex, defined as the energy-deposition centroid. By subtracting the track contributions from non-shower muons in the recorded waveforms, the isolated shower component is extracted. Subsequently, combined with a photon propagation model and an iterative optimization algorithm, the shower vertex positions are reconstructed. Simulations show that for 68\% of events, the single shower vertex resolution is better than 0.16~m, 0.15~m, and 0.26~m along X, Y, and Z respectively. Furthermore, the reconstruction efficiency exceeds 96\% when requiring the distance between the reconstructed and true vertices to be less than 3.0 m. This method provides a critical technical foundation for muon-induced background suppression in JUNO and other large-scale LS detectors.

hep-ex↗

arXiv2026

Quantitative U/Th deposition and cleanliness control strategies in the JUNO site air

The Jiangmen Underground Neutrino Observatory (JUNO) employs a 20 kt liquid scintillator (LS) detector located 700 m underground. To meet its physics objectives, the LS must achieve an ultra-low $^{238}$U/$^{232}$Th content of 10$^{-17}$ g/g. Given that airborne dust exhibits radioactivity about 12 orders of magnitude higher, exceptional cleanliness is essential during on-site installation. The total permissible dust mass in the 20 kt LS is only about 8 mg. To attain this, the acrylic vessel interior must comply with class 1,000 cleanliness. Pre-filling water spray cleaning improves cleanliness by roughly two orders of magnitude, requiring the overall environment to be maintained between class 10,000 and 100,000. At JUNO, a cleanroom management system has been implemented across the 120,000 m$^3$ underground experimental hall. Since May 2022, continuous laser particle monitoring has consistently achieved an average cleanliness class of 74,000. Furthermore, we developed a method to directly measure $^{238}$U/$^{232}$Th deposition rates on detector surfaces. Using ICP-MS, sensitivity reaches sub-ppt levels ($<$10$^{-12}$ g/g), enabling effective cleanliness control and assessment of external contamination during detector construction.

physics.ins-det↗

arXiv2025

Study of a Compact Device for Water Attenuation Length Measurements

This study presents the development and validation of a compact device for measuring the water attenuation length (WAL), utilizing photomultiplier tubes (PMTs), optical fibers, and light-emitting diodes (LEDs). An 8 m water tank and the device was constructed and validated in the laboratory. The device is capable of measuring WAL values up to 50 m. The stray light was blocked mainly by a custom-designed shutter. Toy Monte Carlo simulations were employed to evaluate the measurement uncertainty, which was found to be within reasonable limits. These simulations further indicate that the uncertainty can be reduced and more accurately predicted for a larger-scale device with a length of 30 m. Real-time monitoring was achieved by integrating the device into a water purification circulation system, providing a practical, scalable solution for WAL measurement in future large-scale water Cherenkov detectors.

physics.ins-det↗

arXiv2025

Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector

Atmospheric neutrino oscillations are important to the study of neutrino properties, including the neutrino mass ordering problem. A good capability to identify neutrinos' flavor and neutrinos against antineutrinos is crucial in such measurements. In this paper, we present a machine-learning-based approach for identifying atmospheric neutrino events in a large homogeneous liquid scintillator detector. This method identifies features of PMT waveforms that reflect event topologies and uses them as input to machine learning models. In addition, neutron-capture information is utilized to achieve neutrino versus antineutrino discrimination. Preliminary performances based on Monte Carlo simulations are presented, which demonstrate such a detector's potential in future measurements of atmospheric neutrinos such as the one planned for the JUNO experiment.

hep-ex↗

arXiv2025

Improve Language Model and Brain Alignment via Associative Memory

Associative memory engages in the integration of relevant information for comprehension in the human cognition system. In this work, we seek to improve alignment between language models and human brain while processing speech information by integrating associative memory. After verifying the alignment between language model and brain by mapping language model activations to brain activity, the original text stimuli expanded with simulated associative memory are regarded as input to computational language models. We find the alignment between language model and brain is improved in brain regions closely related to associative memory processing. We also demonstrate large language models after specific supervised fine-tuning better align with brain response, by building the \textit{Association} dataset containing 1000 samples of stories, with instructions encouraging associative memory as input and associated content as output.

cs.CL↗

arXiv2024

Dark Count of 20-inch PMTs Generated by Natural Radioactivity

The primary objective of the JUNO experiment is to determine the ordering of neutrino masses using a 20-kton liquid-scintillator detector. The 20-inch photomultiplier tube (PMT) plays a crucial role in achieving excellent energy resolution of at least 3% at 1 MeV. Understanding the characteristics and features of the PMT is vital for comprehending the detector's performance, particularly regarding the occurrence of large pulses in PMT dark counts. This research paper aims to further investigate the origin of these large pulses in the 20-inch PMT dark count rate through measurements and simulations. The findings confirm that the main sources of the large pulses are natural radioactivity and muons striking the PMT glass. By analyzing the PMT dark count rate spectrum, it becomes possible to roughly estimate the radioactivity levels in the surrounding environment.

physics.ins-det↗

arXiv2023

Study on the radon adsorption capability of low-background activated carbon

Radon is a significant background source in rare event detection experiments. Activated Carbon (AC) adsorption is widely used for effective radon removal. The selection of AC considers its adsorption capacity and radioactive background. In this study, using self-developed devices, we screened and identified a new kind of low-background AC from Qingdao Inaf Technology Company that has very high Radon adsorption capacity. By adjusting the average pore size to 2.3 nm, this AC demonstrates a radon adsorption capacity of 2.6 or 4.7 times higher than Saratech or Carboact activated carbon under the same conditions.

hep-ex↗

arXiv2023

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.

hep-ex↗

arXiv2023

Environmental radon control in the 700-m underground laboratory at JUNO

The Jiangmen Underground Neutrino Observatory is building the world's largest liquid scintillator detector with a 20 kt target mass and about 700 m overburden. The total underground space of civil construction is about 300,000 m$^3$ with the main hall volume of about 120,000 m$^3$, which is the biggest laboratory in the world. Radon concentration in the underground air is quite important for not only human beings' health but also the background of experiments with rare decay detection, such as neutrino and dark matter experiments. The radon concentration is the main hall is required to be around 100 Bq/m$^3$. Optimization of the ventilation with fresh air is effective to control the radon underground. To find the radon sources in the underground laboratory, we made a benchmark experiment in the refuge room near the main hall. The result shows that the radon emanating from underground water is one of the main radon sources in the underground air. The total underground ventilation rate is about 160,000 m$^3$/h fresh air with about 30 Bq/m$^3$ $^{222}$Rn from the bottom of the vertical tunnel after optimization, and 55,000 m$^3$/h is used for the ventilation in the main hall. Finally, the radon concentration inside the main hall decreased from 1600 Bq/m$^3$ to around 100 Bq/m$^3$. The suggested strategies for controlling radon concentration in the underground air are described in this paper.

physics.ins-det↗

arXiv2023

Imaging of muon track in CsI(Tl) crystal with single photon sensitive camera

As a novel approach on visual photon imaging by a single photon sensitive camera and PMTs, this work is trying to measure and identify muon tracks from the 2-D images of CsI(Tl) crystal (scintillator detectors). It is possible that muon tracks can be seen directly with a good signal-to-noise ratio neither with further amplification nor external light, which provides an evolution method for particle measurement in the photon-starved regime of scintillation detectors. The setup of the crystal and camera testing system and the identification algorithm of muon track will be discussed in detail including the system calibration, identification model, signal-to-noise ratio, muon track confirmation, and an expectation on further improvements and applications.

physics.ins-det↗

arXiv2022

Study of 20-inch PMTs dark count generated large pulses

The main goal of the JUNO experiment is to determine the neutrino mass ordering with a 20\,kt liquid-scintillator detector. The 20-inch PMT is crucial as one of JUNO key instruments to realize an excellent energy resolution of at least 3\,\% at 1\,MeV. The knowledge on PMT's characterisation and feature is critical for detector performance understanding. Large pulses from PMT dark count such as from flasher or others are one of the serious concerns for detector noise control. Focusing on the large pulses from 20-inch PMT dark count, this paper is trying to investigate the causes by measurements with a muon tagging system. It is found that the large pules of 20-inch PMT dark count is contributed mainly from muons hitting the PMT glass. A simulation is also realized and achieved a consistent understanding.

physics.ins-det↗

arXiv2022

Muon shower vertex reconstruction with waveform information in JUNO

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton liquid scintillator detector currently being built in a dedicated underground laboratory in China. It is a multi-purpose underground experiment with a physics program including neutrino mass hierarchy determination, precision measurement of neutrino oscillation parameters, measurement of solar, atmospheric, geo-neutrinos and other important neutrino physics searches. Electron anti-neutrinos are detected via the inverse beta decay by measuring the correlated positron and neutron signals. In this detection channel cosmic ray muon induced radioactive isotopes are the main background, especially those connected to cosmogenic backgrounds ($^{9}$Li/$^{8}$He and fast neutrons). They are predominantly produced by showing muons which account for about 10\% of all muons. Considering that the $^{9}$Li/$^{8}$He background is correlated with the parent muon in time and space, the vertex reconstruction of showers along the muon track is helpful to reject the backgrounds of $^{9}$Li/$^{8}$He and other isotopes. Based on the waveform simulation analysis, we know that the multi-peaks in waveform output by PMTs are mainly caused by these showers. Waveform analysis of muon events and preliminary results of shower vertex reconstruction based on detector simulation have been studied.

physics.ins-det↗

arXiv2020

Learn to Segment Retinal Lesions and Beyond

Towards automated retinal screening, this paper makes an endeavor to simultaneously achieve pixel-level retinal lesion segmentation and image-level disease classification. Such a multi-task approach is crucial for accurate and clinically interpretable disease diagnosis. Prior art is insufficient due to three challenges, i.e., lesions lacking objective boundaries, clinical importance of lesions irrelevant to their size, and the lack of one-to-one correspondence between lesion and disease classes. This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading. We propose Lesion-Net, a new variant of fully convolutional networks, with its expansive path re-designed to tackle the first challenge. A dual Dice loss that leverages both semantic segmentation and image classification losses is introduced to resolve the second challenge. Lastly, we build a multi-task network that employs Lesion-Net as a side-attention branch for both DR grading and result interpretation. A set of 12K fundus images is manually segmented by 45 ophthalmologists for 8 DR-related lesions, resulting in 290K manual segments in total. Extensive experiments on this large-scale dataset show that our proposed approach surpasses the prior art for multiple tasks including lesion segmentation, lesion classification and DR grading

cs.CV↗

arXiv2020

Developing the radium measurement system for the water Cherenkov detector of the Jiangmen Underground Neutrino Observatory

The Jiangmen Underground Neutrino Observatory is proposed to determine neutrino mass hierarchy using a 20~ktonne liquid scintillator detector. Strict radio-purity requirements have been put forward for all the components of the detector. According to the MC simulation results, the radon dissolved in the water Cherenkov detector should be below 200~mBq/m$^3$. Radium, the progenitor of radon, should also be taken seriously into account. In order to measure the radium concentration in water, a radium measurement system, which consists of a radium extraction system, a radon emanation chamber and a radon concentration measurement system, has been developed. In this paper, the updated radon concentration in gas measurement system with a one-day-measurement sensitivity of $\sim$5~mBq/m$^3$, the detail of the development of the radium concentration in water measurement system with a sensitivity of $\sim$23~mBq/m$^3$ as well as the measurement results of Daya Bay water samples will be presented.

astro-ph.IM↗

arXiv2014

A GPU Accelerated Aggregation Algebraic Multigrid Method

We present an efficient, robust and fully GPU-accelerated aggregation-based algebraic multigrid preconditioning technique for the solution of large sparse linear systems. These linear systems arise from the discretization of elliptic PDEs. The method involves two stages, setup and solve. In the setup stage, hierarchical coarse grids are constructed through aggregation of the fine grid nodes. These aggregations are obtained using a set of maximal independent nodes from the fine grid nodes. We use a ``fine-grain'' parallel algorithm for finding a maximal independent set from a graph of strong negative connections. The aggregations are combined with a piece-wise constant (unsmooth) interpolation from the coarse grid solution to the fine grid solution, ensuring low setup and interpolation cost. The grid independent convergence is achieved by using recursive Krylov iterations (K-cycles) in the solve stage. An efficient combination of K-cycles and standard multigrid V-cycles is used as the preconditioner for Krylov iterative solvers such as generalized minimal residual and conjugate gradient. We compare the solver performance with other solvers based on smooth aggregation and classical algebraic multigrid methods.

math.NA↗