arXiv subjects

Ye Liang

Publications and source records attributed to Ye Liang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Molecular structure, electric property, and scintillation and quenching of liquid scintillators

Liquid scintillators are widely used in particle and nuclear physics. Understanding the scintillation and quenching mechanisms is a fundamental issue in designing a high-light-yield liquid scintillator. In this work, the basic scintillation process for two-component liquid scintillators is discussed, highlighting the processes of excitation, ionization, and anion-cation recombination. A molecule's polar group, polarization characteristics, and the corresponding material's dielectric constant are found to be correlated with a liquid scintillator's scintillation efficiency. Polar groups and high relative dielectric constant (permittivity) can cause quenching and should be avoided. The tellurium loading scheme in the liquid scintillator of the SNO+ experiment, TeBD, is discussed. The hydroxyl groups introduce polar structures in the TeBD, and for the first time, the relative dielectric constant of TeBD is measured to be $16\pm1$. These discussions explain part of the quenching of the TeBD liquid scintillator.

physics.ins-det↗

arXiv2026

Investigating Production of TeV-scale Muons in Extensive Air Shower at 2400 Meters Underground

Deep underground experiments present a new avenue to probe the first interactions in extensive air showers or hadronic interactions in the extreme forward phase space. The China Jinping Underground Laboratory, characterized by a vertical rock overburden of 2,400~m, provides an exceptionally effective shield against cosmic muons with energies below 3~TeV. The surviving high-energy muons, produced in the first interactions of extensive air showers, open a unique observational window into primary cosmic rays from tens of TeV up to the PeV scale and beyond. This distinctive feature also enables detailed studies of charged hadron production in the earliest stages of shower development. Using 1,338.6 live days of data collected with a one-ton prototype detector for the Jinping Neutrino Experiment, we measured the underground muon flux originating from air showers. The results show discrepancies of about 40\% corresponding to significances of more than 2$σ$, relative to predictions from several leading hadronic interaction models. We interpret these findings from two complementary perspectives: (i) by adopting the expected cosmic-ray spectra, we constrain the modeling of the first hadronic interactions in air showers and provide novel insights into resolving the long-standing \textit{muon puzzle}; and (ii) by assuming specific hadronic interaction models, we infer the mass composition of cosmic rays, and our data favor a lighter component in the corresponding energy range. Our study demonstrates the potential of deep underground laboratories to provide new experimental insights into air shower physics and cosmic rays.

hep-ex↗

arXiv2026

The Geometry of Quasi-Cycles: How Stoichiometric Covariance Alters Pre-Bifurcation Signatures

Environmental enrichment can destabilize predator--prey coexistence through a Hopf bifurcation, yet real ecosystems are finite and intrinsically stochastic. We investigate how mechanistically derived demographic noise shapes near-Hopf dynamics in the Rosenzweig--MacArthur model by systematically comparing two diffusion closures that share identical deterministic drift but differ solely in predation-induced covariance structure. Starting from a continuous-time Markov chain description, we derive a full-covariance stochastic differential equation whose diffusion tensor inherits stoichiometric coupling, generating a negative prey--predator cross-covariance. This model is contrasted with a drift-matched diagonal-noise comparator. Using linear noise approximation, Lyapunov analysis, and matrix-valued power spectral density formulations, we propagate local covariance structure through the entire diagnostic chain, including stochastic sensitivity ellipses and a dimensionless noisy-precursor indicator. The results highlight that drift equivalence does not imply covariance equivalence and show how event-level noise geometry influences macroscopic behavior in nonlinear ecological systems. This work integrates bifurcation theory and stochastic analysis to advance multi-scale modeling of complex interacting systems.

math.DS↗

arXiv2026

Full-Covariance Chemical Langevin Predator--Prey Diffusion with Absorbing Boundaries

Many stochastic Rosenzweig--MacArthur predator--prey models inject ad hoc independent (diagonal) noise and therefore cannot encode the event-level coupling created by predation and biomass conversion. We derive an absorbed, fully mechanistic diffusion approximation and its extinction structure from a continuous-time Markov chain on $\mathbb N_0^2$ with four reaction channels: prey birth, prey competition death, predator death, and a coupled predation--conversion event. Absorbing coordinate axes are imposed to represent the irreversibility of demographic extinction. Under Kurtz density-dependent scaling, the law-of-large-numbers limit recovers the classical RM ODE, while central-limit scaling yields a chemical-Langevin diffusion with explicit drift and full state-dependent covariance. A distinctive signature is the strictly negative cross-covariance $Σ_{12}(N,P)=-mNP/(1+N)$ induced solely by the predation--conversion increment $(-1,1)$. We define the absorbed Itô SDE by freezing trajectories at the first boundary hit and prove strong well-posedness, non-explosion, and moment bounds up to absorption. Extinction has positive probability from every interior state, and predator extinction is almost sure when $m\le c$.

math.PR↗

arXiv2026

Dedifferentiation stabilizes stem cell lineages: From CTMC to diffusion theory and thresholds

We study stem-terminally differentiated (TD) lineages in small niches where demographic noise from discrete division and death events is non-negligible. Starting from a mechanistic five-channel, density-dependent CTMC (symmetric self-renewal, symmetric differentiation, asymmetric division, dedifferentiation, TD death), we derive its mean-field limit and a functional CLT, obtaining a chemical Langevin diffusion whose explicit state-dependent covariance exactly matches the CTMC's aggregated channel-wise infinitesimal covariances. Within this diffusion approximation we remove the dedifferentiation flux and obtain a sharp dichotomy: in subcritical regimes the stem coordinate becomes extinct asymptotically almost surely, whereas in supercritical regimes polynomial moments diverge exponentially. This identifies, at the diffusion level, a structural failure mode of strictly hierarchical lineages under demographic fluctuations and clarifies how a cyclic return flux can rescue homeostasis. For interpretation we also derive an exact totals ODE backbone from a damage-structured transport model and obtain two steady-state constraints (ratio and equalization laws) linking compartment ratios to turnover and balancing dedifferentiation against fate bias. Numerical experiments corroborate the $Ω^{-1/2}$ fluctuation scaling, illustrate the pathology, and contrast theorem-regime global convergence with threshold (Allee-type) behaviour outside the theorem hypotheses.

physics.bio-ph↗

arXiv2025

An exploratory study of a tellurium-loaded liquid scintillator based on water and p-dioxane

Tellurium-loaded liquid scintillators are critical for neutrinoless double-beta decay experiments. However, conventional organic scintillators are constrained by the limited solubility of organic tellurium compounds compared with that of inorganic ones in water, whereas water-based scintillators are likely constrained by the destabilization of surfactants caused by inorganic tellurium compounds. In this work, a surfactant-free water-containing route is explored, in which an aqueous telluric acid solution is introduced into a water-miscible organic scintillator comprising p-dioxane, naphthalene, and PPO. The phase behavior of this system is mapped to delineate homogeneous-mixture domains and to estimate practical upper bounds on tellurium loading. Optical properties are characterized by UV-visible absorption spectroscopy and fluorescence spectroscopy. The scintillation light yield is obtained with a relative method that compares to a reference LAB-PPO scintillator. The measurements demonstrate scintillation quenching induced by water and by tellurium acid. These results provide benchmarks for water-containing and surfactant-free formulations and support the development of high-loading liquid scintillators for future detector design.

physics.ins-det↗

arXiv2025

High-dimensional Statistical Inference and Variable Selection Using Sufficient Dimension Association

Simultaneous variable selection and statistical inference is challenging in high-dimensional data analysis. Most existing post-selection inference methods require explicitly specified regression models, which are often linear, as well as sparsity in the regression model. The performance of such procedures can be poor under either misspecified nonlinear models or a violation of the sparsity assumption. In this paper, we propose a sufficient dimension association (SDA) technique that measures the association between each predictor and the response variable conditioning on other predictors in the high-dimensional setting. Our proposed SDA method requires neither a specific form of regression model nor sparsity in the regression. Alternatively, our method assumes normalized or Gaussian-distributed predictors with a Markov blanket property. We propose an estimator for the SDA and prove asymptotic properties for the estimator. We construct three types of test statistics for the SDA and propose a multiple testing procedure to control the false discovery rate. Extensive simulation studies have been conducted to show the validity and superiority of our SDA method. Gene expression data from the Alzheimer Disease Neuroimaging Initiative are used to demonstrate a real application.

stat.ME↗

arXiv2024

Study of neutron production for 360 GeV cosmic muons

The China Jinping underground Laboratory (CJPL) is an excellent location for studying solar, terrestrial, and supernova neutrinos due to its 2400-meter vertical rock overburden. Its unparalleled depth gives an opportunity to investigate the cosmic-ray muons with exceptionally high average energy at $\sim360$ GeV. This paper details a study of muon-related backgrounds based on 1178 days of data collected by the 1-ton prototype neutrino detector used for the Jinping Neutrino Experiment (JNE) since 2017. The apparent effects for the leakage of muons' secondary particles due to detector's finite size on the measured neutron yield are first discussed in detail. The analysis of 493 cosmic-ray muon candidates and $13.6\pm5.7$ cosmogenic neutron candidates, along with a thorough evaluation of detection efficiency and uncertainties, gives a muon flux of $(3.56\pm0.16_{\mathrm{stat.}}\pm0.10_{\mathrm{syst.}})\times10^{-10}~\mathrm{cm}^{-2}\mathrm{s^{-1}}$ and a cosmogenic neutron yield of $(3.37\pm 1.41_{\mathrm{stat.}}\pm 0.31_{\mathrm{syst.}}) \times 10^{-4}~\mathrmμ^{-1} \mathrm{g}^{-1} \mathrm{cm}^{2}$ in LAB-based liquid scintillator.

hep-ex↗

arXiv2023

Optical property measurements of lithium chloride aqueous solution for a novel solar neutrino experiment

The lithium chloride aqueous solution has great potential to be the detection medium of a novel solar neutrino detector. The nuclide \ce{^7 Li} provides a charged-current interaction channel with a high cross-section for the MeV-scale solar electron-neutrinos, enabling measurement of the solar neutrino spectrum. This work measures the optical properties and the light yields of a saturated lithium chloride solution. After adsorption with activated carbon and recrystallization, the solution shows little absorption in the sensitive wavelength range of the bialkali photomultipliers. The attenuation length is evaluated to reach 50 meters at 430 nm. In addition to being a pure Cherenkov detector medium, a wavelength shifter, carbostyril 124, is added to the LiCl aqueous solution. The compatibility and the enhancement of the light yield are confirmed, enabling the development of a water-based Cherenkov-enhanced lithium-rich detector.

physics.ins-det↗

arXiv2023

Performance of the 1-ton Prototype Neutrino Detector at CJPL-I

China Jinping Underground Laboratory provides an ideal site for solar, geo-, and supernova neutrino studies. With a prototype neutrino detector running since 2017, containing 1-ton liquid scintillator, we tested its experimental hardware, performed the detector calibration and simulation, and measured its radioactive backgrounds, as an early stage of the Jinping Neutrino Experiment (JNE). We investigated the radon background and implemented the nitrogen sealing technology to control it. This paper presents the details of these studies and will serve as a key reference for the construction and optimization of the future large detector of JNE.

hep-ex↗

arXiv2022

Measurement of Muon-induced Neutron Production at the China Jinping Underground Laboratory

Solar, terrestrial, and supernova neutrino experiments are subject to muon-induced radioactive backgrounds. The China Jinping Underground Laboratory (CJPL), with its unique advantage of a 2400 m rock coverage and long distance from nuclear power plants, is ideal for MeV-scale neutrino experiments. Using a 1-ton prototype detector of the Jinping Neutrino Experiment (JNE), we detected 343 high-energy cosmic-ray muons and (7.86$ \pm $3.97) muon-induced neutrons from an 820.28-day dataset at the first phase of CJPL (CJPL-I). Based on the muon-induced neutrons, we measured the corresponding muon-induced neutron yield in a liquid scintillator to be $ (3.44 \pm 1.86_{\rm stat.}\pm 0.76_{\rm syst.})\times 10^{-4}μ^{-1}\rm g^{-1}cm^{2} $ at an average muon energy of \SI{340}{GeV}. We provided the first study for such neutron background at CJPL. A global fit including this measurement shows a power-law coefficient of (0.75$ \pm $0.02) for the dependence of the neutron yield at the liquid scintillator on muon energy.

hep-ex↗

arXiv2022

The Potential to Probe Solar Neutrino Physics with LiCl Water Solution

Lithium chloride water solution is a good option for solar neutrino detection. The $ν_e$ charged-current (CC) interaction cross-section on $\rm{{}^{7}Li}$ is evaluated with new B(GT) experimental measurements. The total CC interaction cross-section weighted by the solar $^8$B electron neutrino spectrum is $3.759\times10^{-42} \rm{cm}^2$, which is about 60 times that of the neutrino-electron elastic scattering process. The final state effective kinetic energy after the CC interaction on $\rm{{}^{7}Li}$ directly reflects the neutrino energy, which stands in sharp contrast to the plateau structure of recoil electrons of the elastic scattering. With the high solubility of LiCl of 74.5 g/100 g water at 10$^\circ$C and the high natural abundance of 92.41%, the molarity of $\rm{{}^{7}Li}$ in water can reach 11 mol/L for safe operation at room temperature. The CC event rate of $ν_e$ on $\rm{{}^{7}Li}$ in the LiCl water solution is comparable to that of neutrino-electron elastic scattering. In addition, the $ν_e$ CC interaction with the contained $\rm{{}^{37}Cl}$ also contributes a few percent of the total CC event rate. The contained $\rm{{}^{35}Cl}$ and $\rm{{}^{6}Li}$ also make a delay-coincidence detection for electron antineutrinos possible. The recrystallization method is found to be applicable for LiCl sample purification. The measured attenuation length of $11\pm1$ m at 430 nm shows that the LiCl solution is practicable for a 10-m diameter detector for solar neutrino detection. Clear advantages are found in studying the upturn effect of solar neutrino oscillation, light sterile neutrinos, and Earth matter effect. The sensitivities in discovering solar neutrino upturn and light sterile neutrinos are shown.

hep-ex↗

arXiv2020

A Simple Prediction Model for the Development Trend of 2019-nCov Epidemics Based on Medical Observations

In order to predict the development trend of the 2019 coronavirus (2019-nCov), we established an prediction model to predict the number of diagnoses case in China except Hubei Province. From January 25 to January 29, 2020, we optimized 6 prediction models, 5 of them based on the number of medical observations to predicts the peak time of confirmed diagnosis will appear on the period of morning of January 29 from 24:00 to February 2 before 5 o'clock 24:00. Then we tracked the data from 24 o'clock on January 29 to 24 o'clock on January 31, and found that the predicted value of the data on the 3rd has a small deviation from the actual value, and the actual value has always remained within the range predicted by the comprehensive prediction model 6. Therefore we discloses this finding and will continue to track whether this pattern can be maintained for longer. We believe that the changes medical observation case number may help to judge the trend of the epidemic situation in advance.

q-bio.PE↗

arXiv2019

Graph-based Multivariate Conditional Autoregressive Models

The conditional autoregressive model is a routinely used statistical model for areal data that arise from, for instances, epidemiological, socio-economic or ecological studies. Various multivariate conditional autoregressive models have also been extensively studied in the literature and it has been shown that extending from the univariate case to the multivariate case is not trivial. The difficulties lie in many aspects, including validity, interpretability, flexibility and computational feasibility of the model. In this paper, we approach the multivariate modeling from an element-based perspective instead of the traditional vector-based perspective. We focus on the joint adjacency structure of elements and discuss graphical structures for both the spatial and non-spatial domains. We assume that the graph for the spatial domain is generally known and fixed while the graph for the non-spatial domain can be unknown and random. We propose a very general specification for the multivariate conditional modeling and then focus on three special cases, which are linked to well known models in the literature. Bayesian inference for parameter learning and graph learning is provided for the focused cases, and finally, an example with public health data is illustrated.

stat.ME↗

arXiv2019

Orthogonal Series Density Estimation for Complex Surveys

We propose an orthogonal series density estimator for complex surveys, where samples are neither independent nor identically distributed. The proposed estimator is proved to be design-unbiased and asymptotically design-consistent. The asymptotic normality is proved under both design and combined spaces. Two data driven estimators are proposed based on the proposed oracle estimator. We show the efficiency of the proposed estimators in simulation studies. A real survey data example is provided for an illustration.

stat.ME↗

arXiv2017

Wide field-of-view and high-efficiency light concentrator

To improve light yield and energy resolution in large-volume neutrino detectors, light concentrators are often mounted on photomultiplier tubes to increase the detection efficiency of optical photons from scintillation or Cherenkov light induced by charged particles. We propose a method to optimize previous light concentrators design in order to attain a field of view of 90 degrees and a geometrical collection efficiency above 98%. This improvement could be crucial to Jinping and other future neutrino experiments whichever it is applicable.

physics.ins-det↗

arXiv2017

Bayesian Nonparametric Inference for Panel Count Data with an Informative Observation Process

In this paper, the panel count data analysis for recurrent events is considered. Such analysis is useful for studying tumor or infection recurrences in both clinical trial and observational studies. A bivariate Gaussian Cox process model is proposed to jointly model the observation process and the recurrent event process. Bayesian nonparametric inference is proposed for simultaneously estimating regression parameters, bivariate frailty effects and baseline intensity functions. Inference is done through Markov chain Monte Carlo, with fully developed computational techniques. Predictive inference is also discussed under the Bayesian setting. The proposed method is shown to be efficient via simulation studies. A clinical trial dataset on skin cancer patients is analyzed to illustrate the proposed approach.

stat.AP↗

arXiv2017

The Inuence of Misspecified Covariance on False Discovery Control when Using Posterior Probabilities

This paper focuses on the influence of a misspecified covariance structure on false discovery rate for the large scale multiple testing problem. Specifically, we evaluate the influence on the marginal distribution of local fdr statistics, which are used in many multiple testing procedures and related to Bayesian posterior probabilities. Explicit forms of the marginal distributions under both correctly specified and incorrectly specified models are derived. The Kullback-Leibler divergence is used to quantify the influence caused by a misspecification. Several numerical examples are provided to illustrate the influence. A real spatio-temporal data on soil humidity is discussed.

math.ST↗