arXiv subjects

Liangjian Wen

Publications and source records attributed to Liangjian Wen.

52 records · Page 3Linked to original sources

arXiv2021

Progress in Nuclear Astrophysics of East and Southeast Asia

Nuclear astrophysics is an interdisciplinary research field of nuclear physics and astrophysics, seeking for the answer to a question, how to understand the evolution of the Universe with the nuclear processes which we learn. We review the research activities of nuclear astrophysics in east and southeast Asia which includes astronomy, experimental and theoretical nuclear physics and astrophysics. Several hot topics such as the Li problems, critical nuclear reactions and properties in stars, properties of dense matter, r-process nucleosynthesis and $ν$-process nucleosynthesis are chosen and discussed in further details. Some future Asian facilities, together with physics perspectives, are introduced.

nucl-ex↗

arXiv2021

Boosting Few-Shot Classification with View-Learnable Contrastive Learning

The goal of few-shot classification is to classify new categories with few labeled examples within each class. Nowadays, the excellent performance in handling few-shot classification problems is shown by metric-based meta-learning methods. However, it is very hard for previous methods to discriminate the fine-grained sub-categories in the embedding space without fine-grained labels. This may lead to unsatisfactory generalization to fine-grained subcategories, and thus affects model interpretation. To tackle this problem, we introduce the contrastive loss into few-shot classification for learning latent fine-grained structure in the embedding space. Furthermore, to overcome the drawbacks of random image transformation used in current contrastive learning in producing noisy and inaccurate image pairs (i.e., views), we develop a learning-to-learn algorithm to automatically generate different views of the same image. Extensive experiments on standard few-shot learning benchmarks demonstrate the superiority of our method.

cs.CV↗

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↗

arXiv2020

A practical approach of high precision U and Th concentration measurement in acrylic

The Jiangmen Underground Neutrino Observatory will build the world's largest liquid scintillator detector to study neutrinos from various sources. The 20 kt liquid scintillator will be stored in a $\sim$600 t acrylic sphere with 35.4 m diameter due to the good light transparency, chemical compatibility and low radioactivity of acrylic. The concentration of U/Th in acrylic is required to be less than 1 ppt (10$^{-12}$ g/g) to achieve a low radioactive background in the fiducial volume of the JUNO detector. The mass production of acrylic has started, and the quality control requires a fast and reliable radioassay on U/Th in acrylic. We have developed a practical method of measuring U/Th in acrylic to sub-ppt level using the Inductively Coupled Plasma Mass Spectrometer (ICP-MS). The U/Th in acrylic can be concentrated by vaporizing acrylic in a class 100 environment, and the residue will be collected and sent to ICP-MS for measuring U/Th. All the other chemical operation is done in a class 100 clean room, and the ICP-MS measurement is done in a class 1000 clean room. The recovery efficiency is studied by adding the natural nonexistent nuclei $^{229}$Th and $^{233}$U as the tracers. The resulting method detection limit (MDL) with 99% confidence can reach 0.02/0.06 pg $^{238}$U/$^{232}$Th /g acrylic with $\sim$75% recovery efficiency. This equipment and method can not only be used for the quality control of JUNO acrylic, but also be further optimized for the radioassay on other materials with extremely low radioactivity, such as ultra-pure water and liquid scintillator.

physics.ins-det↗

arXiv2020

Evaluation of the KLauS ASIC at low temperature

The Taishan Antineutrino Observatory (TAO) is proposed to first use a cold liquid scintillator detector (-50~$^\circ$C) equipped with large-area silicon photomultipliers (SiPMs) ($\sim$10~m$^2$) to precisely measure the reactor antineutrino spectrum with a record energy resolution of < 2\% at 1 MeV. The KLauS ASIC shows excellent performance at room temperature and is a potential readout solution for TAO. In this work, we report evaluations of the fifth version of the KLauS ASIC (KLauS5) from room temperature to -50~$^\circ$C with inputs of injected charge or SiPMs. Our results show that KLauS5 has good performance at the tested temperatures with no significant degradation of the charge noise, charge linearity, gain uniformity or recovery time. Meanwhile, we also observe that several key parameters degrade when the chip operates in cold conditions, including the dynamic range and power consumption. However, even with this degradation, a good signal-to-noise ratio and good resolution of a single photoelectron can still be achieved for the tested SiPM with a gain of greater than 1.5$\times$10$^6$ and even an area of SiPM up to 1~cm$^2$ in one channel, corresponding to an input capacitance of approximately 5~nF. Thus, we conclude that KLauS5 can fulfill the TAO requirements for precise charge measurement.

physics.ins-det↗

arXiv2020

Mutual Information Gradient Estimation for Representation Learning

Mutual Information (MI) plays an important role in representation learning. However, MI is unfortunately intractable in continuous and high-dimensional settings. Recent advances establish tractable and scalable MI estimators to discover useful representation. However, most of the existing methods are not capable of providing an accurate estimation of MI with low-variance when the MI is large. We argue that directly estimating the gradients of MI is more appealing for representation learning than estimating MI in itself. To this end, we propose the Mutual Information Gradient Estimator (MIGE) for representation learning based on the score estimation of implicit distributions. MIGE exhibits a tight and smooth gradient estimation of MI in the high-dimensional and large-MI settings. We expand the applications of MIGE in both unsupervised learning of deep representations based on InfoMax and the Information Bottleneck method. Experimental results have indicated significant performance improvement in learning useful representation.

stat.ML↗

arXiv2019

Structured Pruning of Recurrent Neural Networks through Neuron Selection

Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications. However, the huge sizes and computational burden of these models make it difficult for their deployment on edge devices. A practically effective approach is to reduce the overall storage and computation costs of RNNs by network pruning techniques. Despite their successful applications, those pruning methods based on Lasso either produce irregular sparse patterns in weight matrices, which is not helpful in practical speedup. To address these issues, we propose structured pruning method through neuron selection which can reduce the sizes of basic structures of RNNs. More specifically, we introduce two sets of binary random variables, which can be interpreted as gates or switches to the input neurons and the hidden neurons, respectively. We demonstrate that the corresponding optimization problem can be addressed by minimizing the L0 norm of the weight matrix. Finally, experimental results on language modeling and machine reading comprehension tasks have indicated the advantages of the proposed method in comparison with state-of-the-art pruning competitors. In particular, nearly 20 x practical speedup during inference was achieved without losing performance for language model on the Penn TreeBank dataset, indicating the promising performance of the proposed method

cs.LG↗

arXiv2019

Low-rank Kernel Learning for Graph-based Clustering

Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning in kernel space has shown impressive performance on a number of benchmark data sets. However, its performance is largely determined by the chosen kernel matrix. To address this issue, the previous multiple kernel learning algorithm has been applied to learn an optimal kernel from a group of predefined kernels. This approach might be sensitive to noise and limits the representation ability of the consensus kernel. In contrast to existing methods, we propose to learn a low-rank kernel matrix which exploits the similarity nature of the kernel matrix and seeks an optimal kernel from the neighborhood of candidate kernels. By formulating graph construction and kernel learning in a unified framework, the graph and consensus kernel can be iteratively enhanced by each other. Extensive experimental results validate the efficacy of the proposed method.

cs.LG↗

arXiv2017

Study on the large area MCP-PMT glass radioactivity reduction

The Jiangmen Underground Neutrino Observatory (JUNO) will install about 18,000 20-inch Photomultiplier Tubes (PMTs) in the center detector to achieve 3%/sqrt(E(MeV)) energy resolution. From the full detector Monte Carlo (MC) simulation, besides the liquid scitillator (LS) and Acrylic nodes, PMT glass has the largest contribution to the natural radioactive background. Various technologies have been developed in the Chinese industry to control the environment and to improve the production process. We have monitored the glass production for more than two months, and the radioactivity in glass was measured using a low background gamma ray spectrometer equipped with a high resolution HPGe detector. The 238U, 232Th and 40K of the glass bulb are reduced by a factor of 2, 9 and 15 respectively, and now they can reach 2.5 Bq/kg for 238U, 0.5 Bq/kg for 232Th and 0.5 Bq/kg for 40K.

physics.ins-det↗

arXiv2015

Simulation of natural radioactivity backgrounds in the central detector

The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment proposed to determine the neutrino mass hierarchy and probe the fundamental properties of neutrino oscillation. The JUNO central detector is a spherical liquid scintillator detector with 20 kton fiducial mass. It is required to achieve a $3\%/\sqrt{E(MeV)}$ energy resolution with very low radioactive background, which is a big challenge to the detector design. In order to ensure the detector performance can meet the physics requirements, reliable detector simulation is necessary to provide useful information for detector design. A simulation study of natural radioactivity backgrounds in the JUNO central detector has been performed to guide the detector design and set requirements to the radiopurity of detector materials.

physics.ins-det↗

arXiv2015

Neutrino Oscillation Studies with Reactors

Nuclear reactors are one of the most intense, pure, controllable, cost-effective, and well-understood sources of neutrinos. Reactors have played a major role in the study of neutrino oscillations, a phenomenon that indicates that neutrinos have mass and that neutrino flavors are quantum mechanical mixtures. Over the past several decades reactors were used in the discovery of neutrinos, were crucial in solving the solar neutrino puzzle, and allowed the determination of the smallest mixing angle $θ_{13}$. In the near future, reactors will help to determine the neutrino mass hierarchy and to solve the puzzling issue of sterile neutrinos.

hep-ex↗

arXiv2014

Production of Gadolinium-loaded Liquid Scintillator for the Daya Bay Reactor Neutrino Experiment

We report on the production and characterization of liquid scintillators for the detection of electron antineutrinos by the Daya Bay Reactor Neutrino Experiment. One hundred eighty-five tons of gadolinium-loaded (0.1% by mass) liquid scintillator (Gd-LS) and two hundred tons of unloaded liquid scintillator (LS) were successfully produced from a linear-alkylbenzene (LAB) solvent in six months. The scintillator properties, the production and purification systems, and the quality assurance and control (QA/QC) procedures are described.

physics.ins-det↗

arXiv2009

Experimental Requirements to Determine the Neutrino Mass Hierarchy Using Reactor Neutrinos

This paper presents experimental requirements to determine the neutrino mass hierarchy using reactor neutrinos. The detector shall be located at a baseline around 58 km from the reactor(s) to measure the energy spectrum of electron antineutrinos ($\barν_e$) precisely. By applying Fourier cosine and sine transform to the L/E spectrum, features of the neutrino mass hierarchy can be extracted from the $|Δ{m}^2_{31}|$ and $|Δ{m}^2_{32}|$ oscillations. To determine the neutrino mass hierarchy above 90% probability, requirements to the baseline, the energy resolution, the energy scale uncertainty, the detector mass and the event statistics are studied at different values of $\sin^2(2θ_{13})$

hep-ex↗

arXiv2008

Determination of the Neutrino Mass Hierarchy at an Intermediate Baseline

It is generally believed that neutrino mass hierarchy can be determined at a long baseline experiment, often using accelerator neutrino beams. Reactor neutrino experiments at an intermediate baseline have the capability to distinguish normal or inverted hierarchy. Recently it has been demonstrated that the mass hierarchy could possibly be identified using Fourier transform to the L/E spectrum if the mixing angle $\sin^2(2θ_{13})>0.02$. In this study a more sensitive Fourier analysis is introduced. We found that an ideal detector at an intermediate baseline ($\sim 60$ km) could identify the mass hierarchy for a mixing angle $\sin^2(2θ_{13}) > 0.005$, without requirements on accurate information of reactor neutrino spectra and the value of $Δm^2_{32}$.

hep-ex↗

arXiv2006

Measuring cosmogenic Li9 background in a reactor neutrino experiment

Cosmogenic isotopes 9Li and 8He produced in the detector are the most problematic background in the reactor neutrino experiments designed to determine precisely the neutrino mixing angle theta13. The average time interval of cosmic-ray muons in the detector is often on the order of the lifetimes of the 9Li and 8He isotopes. We have developed a method for determining this kind of background from the distribution of time since last muon for muon rate up to about 20 Hz when the background-to-signal ratio is small, on the order of a few percents.

hep-ex↗