arXiv subjects

Yue Meng

Publications and source records attributed to Yue Meng.

At least 109 records · Page 6Linked to original sources

arXiv2021

A Precise Method to Determine the Energy Scale and Resolution using Gamma Calibration Sources in a Liquid Scintillator Detector

Gamma sources are routinely used to calibrate the energy scale and resolution of liquid scintillator detectors. However, non-scintillating material surrounding the source introduces energy losses, which may bias the determination of the centroid and width of the full absorption peak. In this paper, we present a general method to determine the true gamma centroid and width to a relative precision of 0.03\% and 0.50\%, respectively, using energy losses predicted by the Monte Carlo simulation. In particular, the accuracy of the assumed source geometry is readily obtained from the fit. The method performs well with experimental data in the Daya Bay detector.

physics.ins-det↗

arXiv2021

X-ray Diffraction and Equation of State of the C-S-H Room-Temperature Superconductor

X-ray diffraction indicates that the structure of the recently discovered room temperature carbonaceous sulfur hydride (C-S-H) superconductor is derived from previously established van der Waals compounds found in the H$_2$S-H$_2$ and CH$_4$-H$_2$ systems. Crystals of the superconducting phase were produced by a photochemical synthesis technique leading to the superconducting critical temperature $T_c$ of 288 K at 267 GPa. Single-crystal x-ray diffraction patterns measured from 124 to 178 GPa, within the pressure range of the superconducting phase, give an orthorhombic structure derived from the Al$_2$Cu-type determined for (H$_2$S)$_2$H$_2$ and (CH$_4$)$_2$H$_2$ that differs from those predicted and observed for the S-H system to these pressures. The formation and stability of the C-S-H compound can be understood in terms of the close similarity in effective volumes of the H$_2$S and CH$_4$ components over a broad range of pressures. The relative amounts of carbon and sulfur in the structure is not determined, and denser carbon-bearing S-H structures may form at higher pressures. The results are consistent with hole-doping enhancement of $T_c$ by carbon proposed for the room-temperature superconductivity in this system.

cond-mat.supr-con↗

arXiv2021

Search for Light Dark Matter-Electron Scatterings in the PandaX-II Experiment

We report constraints on light dark matter through its interactions with shell electrons in the PandaX-II liquid xenon detector with a total 46.9 tonne$\cdot$day exposure. To effectively search for these very low energy electron recoils, ionization-only signals are selected from the data. 1821 candidates are identified within ionization signal range between 50 to 75 photoelectrons, corresponding to a mean electronic recoil energy from 0.08 to 0.15 keV. The 90% C.L. exclusion limit on the scattering cross section between the dark matter and electron is calculated based on Poisson statistics. Under the assumption of point interaction, we provide the world's most stringent limit within the dark matter mass range from 15 to 30 $\rm MeV/c^2$, with the corresponding cross section from $2.5\times10^{-37}$ to $3.1\times10^{-38}$ cm$^2$.

hep-ex↗

arXiv2021

Localization and Mapping using Instance-specific Mesh Models

This paper focuses on building semantic maps, containing object poses and shapes, using a monocular camera. This is an important problem because robots need rich understanding of geometry and context if they are to shape the future of transportation, construction, and agriculture. Our contribution is an instance-specific mesh model of object shape that can be optimized online based on semantic information extracted from camera images. Multi-view constraints on the object shape are obtained by detecting objects and extracting category-specific keypoints and segmentation masks. We show that the errors between projections of the mesh model and the observed keypoints and masks can be differentiated in order to obtain accurate instance-specific object shapes. We evaluate the performance of the proposed approach in simulation and on the KITTI dataset by building maps of car poses and shapes.

cs.CV↗

arXiv2021

Determination of responses of liquid xenon to low energy electron and nuclear recoils using the PandaX-II detector

We report a systematic determination of the responses of PandaX-II, a dual phase xenon time projection chamber detector, to low energy recoils. The electron recoil (ER) and nuclear recoil (NR) responses are calibrated, respectively, with injected tritiated methane or $^{220}$Rn source, and with $^{241}$Am-Be neutron source, within an energy range from $1-25$ keV (ER) and $4-80$ keV (NR), under the two drift fields of 400 and 317 V/cm. An empirical model is used to fit the light yield and charge yield for both types of recoils. The best fit models can well describe the calibration data. The systematic uncertainties of the fitted models are obtained via statistical comparison against the data.

physics.ins-det↗

arXiv2021

AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition

Temporal modelling is the key for efficient video action recognition. While understanding temporal information can improve recognition accuracy for dynamic actions, removing temporal redundancy and reusing past features can significantly save computation leading to efficient action recognition. In this paper, we introduce an adaptive temporal fusion network, called AdaFuse, that dynamically fuses channels from current and past feature maps for strong temporal modelling. Specifically, the necessary information from the historical convolution feature maps is fused with current pruned feature maps with the goal of improving both recognition accuracy and efficiency. In addition, we use a skipping operation to further reduce the computation cost of action recognition. Extensive experiments on Something V1 & V2, Jester and Mini-Kinetics show that our approach can achieve about 40% computation savings with comparable accuracy to state-of-the-art methods. The project page can be found at https://mengyuest.github.io/AdaFuse/

cs.CV↗

arXiv2021

Internal Calibration of the PandaX-II Detector with Radon Gaseous Sources

We have developed a low-energy electron recoil (ER) calibration method with $^{220}$Rn for the PandaX-II detector. $^{220}$Rn, emanated from natural thorium compounds, was fed into the detector through the xenon purification system. From 2017 to 2019, we performed three dedicated calibration campaigns with different radon sources. We studied the detector response to $α$, $β$, and $γ$ particles with focus on low energy ER events. During the runs in 2017 and 2018, the amount of radioactivity of $^{222}$Rn were on the order of 1\% of that of $^{220}$Rn and thorium particulate contamination was negligible, especially in 2018. We also measured the background contribution from $^{214}$Pb for the first time in PandaX-II with the help from a $^{222}$Rn injection. Calibration strategy with $^{220}$Rn and $^{222}$Rn will be implemented in the upcoming PandaX-4T experiment and can be useful for other xenon-based detectors as well.

physics.ins-det↗

arXiv2021

Results of Dark Matter Search using the Full PandaX-II Exposure

We report the dark matter search results obtained using the full 132 ton$\cdot$day exposure of the PandaX-II experiment, including all data from March 2016 to August 2018. No significant excess of events is identified above the expected background. Upper limits are set on the spin-independent dark matter-nucleon interactions. The lowest 90% confidence level exclusion on the spin-independent cross section is $2.2\times 10^{-46}$ cm$^2$ at a WIMP mass of 30 GeV/$c^2$.

astro-ph.CO↗

arXiv2020

Cable Loop Calibration System for Jiangmen Underground Neutrino Observatory

A cable loop source calibration system is developed for the Jiangmen Underground Neutrino Observatory, a 20 kton spherical liquid scintillator neutrino experiment. This system is capable of deploying radioactive sources into different positions of the detector in a vertical plane with a few-cm position accuracy. The design and the performance of the prototype are reported in this paper.

physics.ins-det↗

arXiv2020

Effects of impulsive harvesting and an evolving domain in a diffusive logistic model

In order to understand how the combination of domain evolution and impulsive harvesting affect the dynamics of a population, we propose a diffusive logistic population model with impulsive harvesting on a periodically evolving domain. Initially the ecological reproduction index of the impulsive problem is introduced and given by an explicit formula, which depends on the domain evolution rate and the impulsive function. Then the threshold dynamics of the population under monotone or nonmonotone impulsive harvesting is established based on this index. Finally numerical simulations are carried out to illustrate our theoretical results, and reveal that a large domain evolution rate can improve the population survival, no matter which impulsive harvesting takes place. Contrary, impulsive harvesting has a negative effect on the population survival, and can even lead to the extinction of the population.

math.AP↗

arXiv2020

AR-Net: Adaptive Frame Resolution for Efficient Action Recognition

Action recognition is an open and challenging problem in computer vision. While current state-of-the-art models offer excellent recognition results, their computational expense limits their impact for many real-world applications. In this paper, we propose a novel approach, called AR-Net (Adaptive Resolution Network), that selects on-the-fly the optimal resolution for each frame conditioned on the input for efficient action recognition in long untrimmed videos. Specifically, given a video frame, a policy network is used to decide what input resolution should be used for processing by the action recognition model, with the goal of improving both accuracy and efficiency. We efficiently train the policy network jointly with the recognition model using standard back-propagation. Extensive experiments on several challenging action recognition benchmark datasets well demonstrate the efficacy of our proposed approach over state-of-the-art methods. The project page can be found at https://mengyuest.github.io/AR-Net

cs.CV↗

arXiv2020

Pressure induced collapse of magnetic order in jarosite

We report a pressure-induced phase transition in the frustrated kagomé material jarosite at ~45 GPa, which leads to the disappearance of magnetic order. Using a suite of experimental techniques, we characterize the structural, electronic, and magnetic changes in jarosite through this phase transition. Synchrotron powder X-ray diffraction and Fourier transform infrared spectroscopy experiments, analyzed in aggregate with the results from density functional theory calculations, indicate that the material changes from a R-3m structure to a structure with a R-3c space group. The resulting phase features a rare twisted kagomé lattice in which the integrity of the equilateral Fe3+ triangles persists. Based on symmetry arguments we hypothesize that the resulting structural changes alter the magnetic interactions to favor a possible quantum paramagnetic phase at high pressure.

cond-mat.mtrl-sci↗

arXiv2020

Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks

To enable intelligent automated driving systems, a promising strategy is to understand how human drives and interacts with road users in complicated driving situations. In this paper, we propose a 3D-aware egocentric spatial-temporal interaction framework for automated driving applications. Graph convolution networks (GCN) is devised for interaction modeling. We introduce three novel concepts into GCN. First, we decompose egocentric interactions into ego-thing and ego-stuff interaction, modeled by two GCNs. In both GCNs, ego nodes are introduced to encode the interaction between thing objects (e.g., car and pedestrian), and interaction between stuff objects (e.g., lane marking and traffic light). Second, objects' 3D locations are explicitly incorporated into GCN to better model egocentric interactions. Third, to implement ego-stuff interaction in GCN, we propose a MaskAlign operation to extract features for irregular objects. We validate the proposed framework on tactical driver behavior recognition. Extensive experiments are conducted using Honda Research Institute Driving Dataset, the largest dataset with diverse tactical driver behavior annotations. Our framework demonstrates substantial performance boost over baselines on the two experimental settings by 3.9% and 6.0%, respectively. Furthermore, we visualize the learned affinity matrices, which encode ego-thing and ego-stuff interactions, to showcase the proposed framework can capture interactions effectively.

cs.CV↗

arXiv2019

An Improved Evaluation of the Neutron Background in the PandaX-II Experiment

In dark matter direct detection experiments, neutron is a serious source of background, which can mimic the dark matter-nucleus scattering signals. In this paper, we present an improved evaluation of the neutron background in the PandaX-II dark matter experiment by a novel approach. Instead of fully relying on the Monte Carlo simulation, the overall neutron background is determined from the neutron-induced high energy signals in the data. In addition, the probability of producing a dark-matter-like background per neutron is evaluated with a complete Monte Carlo generator, where the correlated emission of neutron(s) and $γ$(s) in the ($α$, n) reactions and spontaneous fissions is taken into consideration. With this method, the neutron backgrounds in the Run 9 (26-ton-day) and Run 10 (28-ton-day) data sets of PandaX-II are estimated to be 0.66$\pm$0.24 and 0.47$\pm$0.25 events, respectively.

hep-ex↗

arXiv2019

Searching for Neutrino-less Double Beta Decay of $^{136}$Xe with PandaX-II Liquid Xenon Detector

We report the Neutrino-less Double Beta Decay (NLDBD) search results from PandaX-II dual-phase liquid xenon time projection chamber. The total live time used in this analysis is 403.1 days from June 2016 to August 2018. With NLDBD-optimized event selection criteria, we obtain a fiducial mass of 219 kg of natural xenon. The accumulated xenon exposure is 242 kg$\cdot$yr, or equivalently 22.2 kg$\cdot$yr of $^{136}$Xe exposure. At the region around $^{136}$Xe decay Q-value of 2458 keV, the energy resolution of PandaX-II is 4.2%. We find no evidence of NLDBD in PandaX-II and establish a lower limit for decay half-life of 2.4 $ \times 10^{23} $ yr at the 90% confidence level, which corresponds to an effective Majorana neutrino mass $m_{ββ} < (1.3 - 3.5)$ eV. This is the first NLDBD result reported from a dual-phase xenon experiment.

hep-ex↗

arXiv2019

A New Method for Evaluating the Effectiveness of Plastic Packaging Against Radon Penetration

Deposition of $^{222}Rn$ daughters onto detector materials pose a risk to ultra-low background experiments. To mitigate this risk, a common approach is to enclose detector components in sealed plastic bags made of films known to be effective barriers against radon. We describe a new method to evaluate radon barriers which is unique in that (a) it gauges not only the intrinsic resistance to radon penetration of a plastic film but also the integrity of bags fabricated from the film and sealed following some protocol, and (b) it employs gamma spectroscopy rather than alpha spectroscopy. We report the results of applying this method to sealed bags fabricated from polypropylene, Nylon, Mylar, metallized Mylar, FEP, and PFA. Evaluation of the fluoropolymers FEP and PFA as radon barriers are the first such measurements.

physics.ins-det↗

arXiv2019

SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception

Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on perceiving geometry; however, they usually ignore the coherence of objects and perform poorly under scenarios with dark and noisy environments. In contrast, supervised learning algorithms, which are robust, require large labeled geometric dataset. This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make depth and flow predictions consistent with objects and robust to low lighting conditions. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for depth prediction by 30% (in squared relative error). In particular, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction. Our code will be made available at https://github.com/mengyuest/SIGNet

cs.CV↗

arXiv2018

Evidence for superconductivity above 260 K in lanthanum superhydride at megabar pressures

Recent predictions and experimental observations of high Tc superconductivity in hydrogen-rich materials at very high pressures are driving the search for superconductivity in the vicinity of room temperature. We have developed a novel preparation technique that is optimally suited for megabar pressure syntheses of superhydrides using pulsed laser heating while maintaining the integrity of sample-probe contacts for electrical transport measurements to 200 GPa. We detail the synthesis and characterization, including four-probe electrical transport measurements, of lanthanum superhydride samples that display a significant drop in resistivity on cooling beginning around 260 K and pressures of 190 GPa. Additional measurements on two additional samples synthesized the same way show resistance drops beginning as high as 280 K at these pressures. The loss of resistance at these high temperatures is not observed in control experiments on pure La as well as in partially transformed samples at these pressures, and x-ray diffraction as a function of temperature on the superhydride reveal no structural changes on cooling. We infer that the resistance drop is a signature of the predicted room-temperature superconductivity in LaH10, in good agreement with density functional structure search and BCS theory calculations.

cond-mat.mtrl-sci↗