arXiv subjects

Lifeng Zhang

Publications and source records attributed to Lifeng Zhang.

12 recordsLinked to original sources

arXiv2026

Simulation of the electron escape ratio for keV alpha-particle ionization tracks in liquid helium

The electron escape ratio for 5.3 MeV alpha-particle ionization tracks in liquid helium under varying external electric fields has been measured in several experiments. However, to the best of our knowledge, the corresponding ratio for keV-scale alpha tracks in the same medium has not yet been reported. In this article, we demonstrate for the first time that this ratio can be accurately characterized using COMSOL-based simulations. Our simulation framework was developed in two stages. In Stage I, we aimed to verify consistency between our simulated results and published experimental data for 5.3 MeV alpha particles. Following successful verification in Stage I, we proceeded to Stage II, in which the 5.3 MeV track was replaced by 2, 5, and 10 keV tracks. Our simulated results reveal that (a) keV-scale tracks exhibit electron escape ratios approximately 1.5-2.5 times higher than that of the 5.3 MeV track, and (b) the escape ratios for all track energies (2, 5, 10 keV, and 5.3 MeV) exhibit a linear dependence on the ion number density at the simulation's T0, but not on the electron number density.

astro-ph.IM↗

arXiv2025

Supporting renewable energy planning and operation with data-driven high-resolution ensemble weather forecast

The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high-resolution weather information. Coarse-grid global numerical weather forecasts are typically downscaled to meet these requirements, introducing challenges of scale inconsistency, process representation error, computation cost, and entanglement of distinct uncertainty sources from chaoticity, model bias, and large-scale forcing. We address these challenges by learning the climatological distribution of a target wind farm using its high-resolution numerical weather simulations. An optimal combination of this learned high-resolution climatological prior with coarse-grid large scale forecasts yields highly accurate, fine-grained, full-variable, large ensemble of weather pattern forecasts. Using observed meteorological records and wind turbine power outputs as references, the proposed methodology verifies advantageously compared to existing numerical/statistical forecasting-downscaling pipelines, regarding either deterministic/probabilistic skills or economic gains. Moreover, a 100-member, 10-day forecast with spatial resolution of 1 km and output frequency of 15 min takes < 1 hour on a moderate-end GPU, as contrast to $\mathcal{O}(10^3)$ CPU hours for conventional numerical simulation. By drastically reducing computational costs while maintaining accuracy, our method paves the way for more efficient and reliable renewable energy planning and operation.

cs.LG↗

arXiv2025

RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud Detection

Fraud detection remains a challenging task due to the complex and deceptive nature of fraudulent activities. Current approaches primarily concentrate on learning only one perspective of the graph: either the topological structure of the graph or the attributes of individual nodes. However, we conduct empirical studies to reveal that these two types of features, while nearly orthogonal, are each independently effective. As a result, previous methods can not fully capture the comprehensive characteristics of the fraud graph. To address this dilemma, we present a novel framework called Relation-Aware GNN with transFormer~(RAGFormer) which simultaneously embeds both semantic and topological features into a target node. The simple yet effective network consists of a semantic encoder, a topology encoder, and an attention fusion module. The semantic encoder utilizes Transformer to learn semantic features and node interactions across different relations. We introduce Relation-Aware GNN as the topology encoder to learn topological features and node interactions within each relation. These two complementary features are interleaved through an attention fusion module to support prediction by both orthogonal features. Extensive experiments on two popular public datasets demonstrate that RAGFormer achieves state-of-the-art performance. The significant improvement of RAGFormer in an industrial credit card fraud detection dataset further validates the applicability of our method in real-world business scenarios.

cs.LG↗

arXiv2024

A novel nuclear recoil calibration for liquid noble gas detectors

According to many dark matter models, a potential signal registered in a detector would feature a single-scattering nuclear recoil (NR). So, it is crucial to calibrate the detector's response to NR events. The conventional calibrations implement $\sim$ keV to MeV neutrons, which can be produced by an accelerator, a neutron generator, or a radioactive source. Although the calibrating methods have been widely employed, they could be improved in several ways: (a) the incident neutron energy should be more monoenergetic, (b) the calibrating NR energy should line up with the region of interest (ROI) of the experiment, and (c) the intensity of the beam should be appropriate. In the paper, we introduce a novel NR calibration method for liquid helium detectors, in which a helium beam ($α$ particles) will be implemented to calibrate the detectors. The helium beam can (i) be tuned precisely to have a jitter of $\lesssim $ 4\% (the $α$ beam's kinetic energy is equivalent to the recoil energy in the conventional calibrations with fast neutrons); (ii) have an energy between $\sim$ 100 eV and tens of keV; and (iii) provide a tunable flux from nA to 100 $μ$A, which presents convenience in beam pipe configuration to obtain a $\sim$ 100 Hz events rate so that the events pileup would be ignorable.

physics.ins-det↗

arXiv2023

Characterization of FBK NUV-HD-Cryo SiPMs near LHe temperature

Five FBK ``NUV-HD-Cryo'' SiPMs have been characterized at 7 K and 10 K, with 405 nm and 530 nm LED light, respectively. The dark count rate (DCR) was measured to be $\sim$ 1 Hz for the $\sim$ 100 mm$^2$-size SiPMs, or 0.01 Hz/mm$^2$, which is $\sim$ 7 orders lower than the DCR at room temperature (RT). Given the very low DCR at these cryogenic temperatures, we measured the SiPMs' I-V curves with such a method: illuminated the SiPMs with weak light, which differs from the conventional measurements at RT. Then, we measured the photo-detection efficiency (PDE), after-pulse (AP), and cross-talk (CT) with a bias voltage ranging from overvoltage (OV) 5 to 11 V. At the OV interval (5 to 11 V), the PDE was between 20\% - 45\%, and the AP and CT were both between $\sim$ 5\% and $\sim$ 20\%. With an OV higher than 10 V, the PDE would be $\ge$ 40\%, and the AP and CT are $\sim$ 20\%. Combining all of the measurements, we are confident that the SiPMs can be equipped as the photosensors on liquid helium detectors, including but not limited to the time projection chambers, which we have proposed in hunting for low-mass dark matter directly and beyond.

physics.ins-det↗

arXiv2023

Search for ER and/or NR-like dark matter signals with the especially low background liquid helium TPCs

In the Dark Matter (DM) direct detection community, the absence of convincing signals has become a "new normal" for decades. Among other possibilities, the "new normal" might indicate that DM-matter interactions could generate not only the hypothetical NR (Nuclear Recoil) events but also the ER (Electron Recoil) ones, which have often been tagged as backgrounds historically. Further, we argue that ER and NR-like DM signals could co-exist in a DM detector's same dataset. So in total, there would be three scenarios we can search for DM signals: (i) ER excess only, (ii) NR excess only, and (iii) ER and NR excesses combined. To effectively identify any possible DM signal under the three scenarios, a DM detector should (a) have the minimum ER and NR backgrounds and (b) be capable of discriminating ER events from NR ones. Accordingly, we introduce the newly established project, ALETHEIA, which implements liquid helium-filled TPCs (Time Projection Chambers) in hunting for DM. Thanks to the nearly single-digit number of ER and NR backgrounds on 1 ton*yr exposure, presumably, the ALETHEIA detectors could identify any form of DM-induced excess in its ROI (Research Of Interest). As far as we know, ALETHEIA is the first DM direct detection experiment claiming such an inclusive search; conventional detectors search DM mainly on the "ER excess only" and/or the "NR excess only" channel, not the "ER and NR excesses combined" channel.

astro-ph.IM↗

arXiv2023

Conceptual design and progress of transmitting $\sim$ MV DC HV into 4 K LHe detectors

A dual-phase TPC (Time Projection Chamber) is more advanced in characterizing an event than a single-phase one because it can, in principle, reconstruct the 3D (X-Y-Z) image of the event, while a single-phase detector can only show a 2D (X-Y) picture. As a result, more enriched physics is expected for a dual-phase detector than a single-phase one. However, to build such a detector, DC HV (High Voltage) must be delivered into the chamber (to have a static electric field), which is a challenging task, especially for an LHe detector due to the extremely low temperature, $\sim$ 4 K, and the very high voltage, $\sim$ MV (Million Volts). This article introduces a convincing design for transmitting $\sim$ MV DC into a 4 K LHe detector. We also report the progress of manufacturing a 100 kV DC feedthrough capable of working at 4 K. Surprisingly, we realized that the technology we developed here might be a valuable reference to the scientists and engineers aiming to build residential bases on the Moon or Mars.

physics.ins-det↗

arXiv2023

Entanglement Verification with Deep Semi-supervised Machine Learning

Quantum entanglement lies at the heart in quantum information processing tasks. Although many criteria have been proposed, efficient and scalable methods to detect the entanglement of generally given quantum states are still not available yet, particularly for high-dimensional and multipartite quantum systems. Based on FixMatch and Pseudo-Label method, we propose a deep semi-supervised learning model with a small portion of labeled data and a large portion of unlabeled data. The data augmentation strategies are applied in this model by using the convexity of separable states and performing local unitary operations on the training data. We verify that our model has good generalization ability and gives rise to better accuracies compared to traditional supervised learning models by detailed examples.

quant-ph↗

arXiv2022

Coating $μ$m TPB on a cylindrical detector and studying the sample films being cooled to LN and LHe temperatures

ALETHEIA is a newly established dark matter direct detection project that aims at hunting for low-mass WIMPs. TPB is widely implemented in liquid helium and argon experiments to shift VUV photons to visible light. We first report that we have successfully coated $\sim 3 ~μ$m TPB on the inner walls of a 10-cm cylindrical PTFE detector; we split the coating process into two steps to have all of the surfaces being coated with the same thickness; three independent methods were applied to figure out the thickness of the TPB coating layers, and consistent results were obtained. Second, with an SEM machine, we scanned the surface of TPB coating sample films exposed to different cryogenic temperatures. The first group of sample layers were immersed into a liquid nitrogen dewar for forty hours, the second group samples were cooled to 4.5 K for three hours, and the third group stayed at room temperature after coating. The SEM-scanned images of the sample films barely show any noticeable difference.

physics.ins-det↗

arXiv2022

Introduction to a low-mass dark matter project, ALETHEIA: A Liquid hElium Time projection cHambEr In dArk matter

Dark Matter (DM) is one of the most critical questions to be understood and answered in fundamental physics today. Plenty of astronomical and cosmological observations have already pinned down that DM exists in the Universe, the Milky Way, and the Solar System. However, understanding DM with the language of elementary physics is still in progress. DM direct detection tests the interactive cross-section between galactic DM particles and an underground detector's nucleons. WIMPs is the most discussed DM candidate. After decades of hunting, a convincing WIMPs signal is still at large. Relatively, the low-mass WIMPs region ($\sim$ 10 MeV/c$^2$ - 10 GeV/c$^2$) has not been fully exploited compared to high-mass WIMPs ($\sim$ 10 GeV/c$^2$ - 10 TeV/c$^2$). By filling the arguably cleanest bulk material, LHe, into the arguably most competitive detector in the field, TPCs, ALETHEIA is supposed to achieve an extremely low-level background; therefore, to help answer one of the most pressing physical questions today: the nature of DM. In this paper, we briefly go through the physics motivation of low-mass DM, the ALETHEIA detector's design, possible analysis channels available for DM searches, and the progress we have made since the project launched in the summer of 2020.

astro-ph.IM↗

arXiv2021

Einstein-Podolsky-Rosen steering based on semi-supervised machine learning

Einstein-Podolsky-Rosen(EPR)steering is a kind of powerful nonlocal quantum resource in quantum information processing such as quantum cryptography and quantum communication. Many criteria have been proposed in the past few years to detect the steerability both analytically and numerically. Supervised machine learning such as support vector machines and neural networks have also been trained to detect the EPR steerability. To implement supervised machine learning, one needs a lot of labeled quantum states by using the semidefinite programming, which is very time consuming. We present a semi-supervised support vector machine method which only uses a small portion of labeled quantum states in detecting quantum steering. We show that our approach can significantly improve the accuracies by detailed examples.

quant-ph↗

arXiv2021

A low-mass dark matter project, ALETHEIA: A Liquid hElium Time projection cHambEr In dArk matter

Dark Matter (DM) is one of the most critical questions to be understood and answered in fundamental physics today. Observations with varied astronomical and cosmological technologies already pinned down that DM exists in the Universe, the Milky Way, and the Solar System. However, the understanding of DM under the language of elementary physics is still in progress. DM direct detection aims to test the interactive cross-section between galactic DM particles and an underground detector's nucleons. Although Weakly Interactive Massive Particles (WIMPs) is the most discussed DM candidate, the null-WIMPs conclusion has been consistently addressed by most convincing experiments in the field. The low-mass WIMPs region (100s MeV/c$^2$ - 10 GeV/c$^2$) has not been fully exploited comparing to high-mass WIMPs (10 GeV/c$^2$ - 1 TeV/c$^2$) experiments which implement liquid xenon or argon TPCs (Time Projection Chambers). The ALETHEIA experiment aims to hunt for low-mass WIMPs with liquid helium-filled TPCs. In this paper, we go through the physics motivation of low-mass DM, the ALETHEIA detector's design, a series of R&D programs that should be launched to address a liquid helium TPC's functionality, and possible analysis channels available for DM searches. We designed and homemade a 30 g liquid helium prototype detector at CIAE. In Summer 2021, the detector was cooled down to 4.5 K successfully. The detector's dark current is less than ten pA for an external voltage up to 17 kV/cm when filled with vacuum, 1 atm nitrogen gas, and liquid nitrogen.

astro-ph.IM↗