arXiv subjects

Q. Tang

Publications and source records attributed to Q. Tang.

11 recordsLinked to original sources

arXiv2026

A three-step proposal for searching for light shining through walls in the X-ray band at the High Energy Photon Source

Despite compelling observational evidence for dark matter (DM), its fundamental physical properties remain poorly understood. In this report, we propose a three-step light-shining-through-walls (LSW) experimental scheme utilizing the high-brilliance, high-energy X-rays from the ID21 Hard X-ray Imaging Beamline at the High Energy Photon Source (HEPS) to search for signatures of dark photons (DPs) and other weakly interacting slim particles (WISPs). The scheme includes three steps of LSW experiments: a short-term (several days) dedicated exposure experiment, a long-term (several years) synchronous accompanying experiment, and a WISP detection with strong magnetic fields. Projection results show that this HEPS-based LSW experiment can effectively constrain DP parameters in the 1 eV--400 keV mass range, covering unexploited parameter space of the existing X-ray LSW experiments. It provides a least model-dependent and most purely-laboratory approach for probing dark sector particles and advancing new physics research beyond the Standard Model gradually.

hep-ex↗

arXiv2025

Measurement of radon concentration in the output water of the 100~t/h ultrapure water system at the Jiangmen Underground Neutrino Observatory

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose low background liquid scintillator detector, was proposed primarily to determine the neutrino mass ordering. To mitigate radioactivity from surrounding rock and enable cosmic muon tagging, its central detector is immersed in a Water Cherenkov Detector (WCD) containing 40~ktons of ultrapure water instrumented with 2400 20-inch micro-channel plate photomultiplier tubes. Stringent radiopurity requirements mandate a radon concentration below 10 ~mBq/m$^3$ in the WCD. To achieve this, we developed a two-stage (ground and underground) ultrapure water system with 100~t/h production capacity, integrating a five-stage degassing membrane for radon removal. A novel microbubble technique was implemented to optimize the degassing membranes' radon removal efficiency. The synergistic combination of the microbubble technology and the multistage degassing membranes achieved a radon removal efficiency exceeding 99.9\%, reducing the system's output to 0.61 $\pm$ 0.50~mBq/m$^3$ in recirculation mode, surpassing design specifications and establishing world-leading performance standards. This paper details the ultrapure system architecture, quantifies the radon contributions of each device, and presents a comprehensive study on microbubble-augmented membrane degassing for low radon ultra-pure water production in a 100~t/h water system.

physics.ins-det↗

arXiv2024

Development of low-radon ultra-pure water for the Jiangmen Underground Neutrino Observatory

The Jiangmen Underground Neutrino Observatory(JUNO) is a state-of-the-art liquid scintillator-based neutrino physics experiment under construction in South China. To reduce the background from external radioactivities, a water Cherenkov detector composed of 35~kton ultra-pure water and 2,400 20-inch photomultiplier tubes is developed. Even after specialized treatment, ultra-pure water still contains trace levels of radioactive elements that can contribute to the detector background. Among which $^{222}$Rn is particularly significant. To address this, an online radon removal system based on the JUNO prototype has been developed. By integrating micro-bubble generators to enhance degasser's radon removal efficiency, the radon concentration in water can be reduced to 1~mBq/m$^{3}$ level, meeting the stringent requirements of JUNO. Additionally, a highly sensitive online radon concentration measurement system capable of detecting concentrations $\sim$1~mBq/m$^3$ has been developed to monitor the radon concentration in water. In this paper, the details regarding both systems will be presented.

physics.ins-det↗

arXiv2024

Developing a $\mu$Bq/m$^{3}$ level $^{226}$Ra concentration in water measurement system for the Jiangmen Underground Neutrino Observatory

The Jiangmen Underground Neutrino Observatory (JUNO), a 20~kton multi-purpose low background Liquid Scintillator (LS) detector, was proposed primarily to determine the neutrino mass ordering. To suppress the radioactivity from the surrounding rocks and tag cosmic muons, the JUNO central detector is submerged in a Water Cherenkov Detector (WCD). In addition to being used in the WCD, ultrapure water is used in LS filling, for which the $^{226}$Ra concentration in water needs to be less than 50~$\mu$Bq/m$^3$. To precisely measure the $^{226}$Ra concentration in water, a 6.0~$\mu$Bq/m$^3$ $^{226}$Ra concentration in water measurement system has been developed. In this paper, the detail of the measurement system as well as the $^{226}$Ra concentration measurement result in regular EWII ultrapure water will be presented.

physics.ins-det↗

arXiv2023

Research of radon diffusion behavior in liquid scintillator

The background caused by radon and its daughters is an important background in the low background liquid scintillator (LS) detectors. The study of the diffusion behaviour of radon in the LS contributes to the analysis of the related background caused by radon. Methodologies and devices for measuring the diffusion coefficient and solubility of radon in materials are developed and described. The radon diffusion coefficient of the LS was measured for the first time and in addition the solubility coefficient was also obtained. In addition, the radon diffusion coefficient of the polyolefine film which is consistent with data in the literature was measured to verify the reliability of the diffusion device.

physics.ins-det↗

arXiv2023

System upgrade for $\mu$Bq/m$^3$ level $^{222}$Rn concentration measurement

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multipurpose underground liquid scintillator detector, was proposed for the determination of the neutrino mass hierarchy as primary physics goal. The central detector will be submerged in a water Cherenkov detector to lower the background from the environment and cosmic muons. Radon is one of the primary background sources. Nitrogen will be used in several sub-systems, and a highly sensitive radon detector has to be developed to measure its radon concentration. A system has been developed based on $^{222}$Rn enrichment of activated carbon and $^{222}$Rn detection based on the electrostatic collection. This paper presents the detail of a $\mu$Bq/m$^3$ level $^{222}$Rn concentration measurement system and gives detailed information about how the adsorption coefficient was measured and how the temperature, flow rate, and $^{222}$Rn concentration affect the adsorption coefficient.

physics.ins-det↗

arXiv2021

A Study on the radon removal performance of low background activated carbon

Radon and radon daughters pose significant backgrounds to rare-event searching experiments. Activated carbon, which has very strong adsorption capacity for radon, can be used for radon removal and radon enrichment. The internal $^{226}$Ra concentration ultimately limits its radon enrichment ability. In order to measure the intrinsic background and study the radon adsorption capability of Saratech activated carbon at various temperatures, a radon-emanation measurement system with a high-sensitivity radon detector and an adsorption-performance research-system have been developed. In this paper, a 0.71~mBq/m$^3$ high-sensitivity radon detector and measurement details of the radon-adsorption capability of Saratech activated carbon at low temperature will be presented.

physics.ins-det↗

arXiv2020

Fast neural Poincar\'e maps for toroidal magnetic fields

Poincar\'e maps for toroidal magnetic fields are routinely employed to study gross confinement properties in devices built to contain hot plasmas. In most practical applications, evaluating a Poincar\'e map requires numerical integration of a magnetic field line, a process that can be slow and that cannot be easily accelerated using parallel computations. We show that a novel neural network architecture, the H\'enonNet, is capable of accurately learning realistic Poincar\'e maps from observations of a conventional field-line-following algorithm. After training, such learned Poincar\'e maps evaluate much faster than the field-line integration method. Moreover, the H\'enonNet architecture exactly reproduces the primary physics constraint imposed on field-line Poincar\'e maps: flux preservation. This structure-preserving property is the consequence of each layer in a H\'enonNet being a symplectic map. We demonstrate empirically that a H\'enonNet can learn to mock the confinement properties of a large magnetic island by using coiled hyperbolic invariant manifolds to produce a sticky chaotic region at the desired island location. This suggests a novel approach to designing magnetic fields with good confinement properties that may be more flexible than ensuring confinement using KAM tori.

physics.plasm-ph↗

arXiv2019

AC Oscillation of a Spin Soliton Driven by a Constant Force

The phenomena of AC oscillation generated by a DC drive, such as the famous Josephson AC effect in superconductors and Bloch oscillation in solid physics, are of great interest in physics. Here we report another example of such counter-intuitive phenomenon that a spin soliton in a two-component Bose-Einstein condensate is driven by a constant force: The initially static spin soliton first moves in a direction opposite to the force and then changes direction, showing an extraordinary AC oscillation in a long term. In sharp contrast to the Josephson AC effect and Bloch oscillation, we find that the nonlinear interactions play important roles and the spin soliton can exhibit a periodic transition between negative and positive inertial mass even in the absence of periodic potentials. We then develop an explicit quasiparticle model that can account for this extraordinary oscillation satisfactorily. Important implications and possible applications of our finding are discussed.

cond-mat.quant-gas↗

arXiv2018

Entropy-based closure for probabilistic learning on manifolds

In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffusion manifolds and the statistical sample generation is accomplished using a projected Ito stochastic differential equation. This probabilistic learning approach has been extended to polynomial chaos representation of databases on manifolds and to probabilistic nonconvex constrained optimization with a fixed budget of function evaluations. The methodology introduces an isotropic-diffusion kernel with hyperparameter {\epsilon}. Currently, {\epsilon} is more or less arbitrarily chosen. In this paper, we propose a selection criterion for identifying an optimal value of {\epsilon}, based on a maximum entropy argument. The result is a comprehensive, closed, probabilistic model for characterizing data sets with hidden constraints. This entropy argument ensures that out of all possible models, this is the one that is the most uncertain beyond any specified constraints, which is selected. Applications are presented for several databases.

math.PR↗

arXiv2017

A Method to Improve the Resolution of the Acoustic Microscopy

In this report, we demonstrate a new principle to improve the resolution of the acoustic microscopy, which is based on the sub-wavelength focusing of acoustic wave passing through an acoustically transparent mesoscale particle. In the principle, the width of the acoustic focal area can be less than one wavelength. The sub-wavelength focusing effect is verified by the FEM simulation.

physics.gen-ph↗