arXiv subjects

Bingzhi Li

Publications and source records attributed to Bingzhi Li.

9 recordsLinked to original sources

arXiv2026

CANTON-$μ$ Proposal: A Next-Generation Muon $g-2$ Measurement at Sub-0.1 ppm Precision

We propose a next-generation precision measurement of the muon anomalous magnetic moment (muon g-2) at the High Intensity Heavy-Ion Accelerator Facility (HIAF) in Huizhou, China. We refer to this proposed experimental programme as CANTON-$μ$ (Coherent Anomalous magNetic momenT ObservatioN with muon). HIAF's intense, pulsed GeV-scale muon beams, particularly for negative muons, provide a promising basis for this programme. Building on two previously proposed storage-ring concepts, this work develops HIAF-specific experimental schemes that relax the conventional magic-momentum constraint and allow greater flexibility in the choice of beam momentum. We assess the expected muon intensity at HIAF and the corresponding statistical sensitivity. For each scheme, we identify its distinctive systematic effects, examine feasible control strategies, and propose quantitative systematic-uncertainty targets. Together, the statistical projections and systematic-uncertainty targets indicate prospective total precisions of 0.1 ppm in Phase-I, comparable to the current Fermilab precision, with the potential to reach the 0.05 ppm level in in Phase-II following the planned HIAF upgrade. At the ultimate projected precision, the measurement would provide a stringent test of the Standard Model and probe new physics at multi-TeV scales. A focus on negative-muon measurements would enable direct comparison with existing high-precision positive-muon results and strengthen tests of CPT symmetry in the muon sector within the Standard-Model Extension, with a projected sensitivity at the $10^{-24}$ GeV level, an order of magnitude beyond current limits.

hep-ex↗

arXiv2026

3D Magnetic Field Reconstruction and Mapping with Physics-Informed Neural Networks

Accurate reconstruction of magnetic fields in inaccessible regions is vital for many high-precision experiments in physics. Traditional methods, such as spherical harmonic expansion, often suffer from truncation errors that limit their precision. This study proposes an advanced Physics-Informed Neural Network (PINN) framework for high-precision 3D magnetic field mapping. Unlike conventional data-driven models, the proposed PINN integrates Maxwell's equations directly into the loss function, enforcing divergence-free and curl-free conditions across the entire domain. A key innovation is the inclusion of explicit physics-residual losses at measurement locations, ensuring rigorous physical consistency beyond random collocation sampling. Validation using simulated data achieves a reconstruction accuracy of $10^{-4}$, a tenfold improvement over existing PINN benchmarks. Furthermore, experimental validation using a custom coil assembly demonstrates robust reconstruction with sub-percent relative accuracy, reaching the $10^{-3}$ level under ambient conditions. This AI-driven methodology provides a robust, high-precision solution for field monitoring and measurement in complex experimental environments where direct sensor placement is restricted.

physics.ins-det↗

arXiv2026

DREAMuS: Dark matter REsearch with Advanced Muon Source

We propose DREAMuS, a fixed-target experiment at the High Intensity Heavy-Ion Accelerator Facility (HIAF), to search for muon-philic dark matter mediated by light flavor-violating bosons. DREAMuS is designed to probe the parameter space of a muon-philic dark matter (DM) mediated by a light flavor-violating boson, specifically a vector $Z'$ (or a scalar $ϕ$) which is produced in muon-nucleus interactions and decays into dark matter particles with a distinctive detector signature. Precision tracking and time-of-flight measurements are used to suppress the Standard Model backgrounds. We find that DREAMuS can achieve competitive sensitivity in the GeV-scale muon-philic dark matter parameter space, reaching sensitivity to couplings at the $10^{-4}$, especially in the few-hundred-MeV region.In addition to a $μ^-$ run, we highlight the potential of a complementary $μ^+$ beam option, further improving sensitivity to dark matter below 200 $\mathrm{MeV}$ by an order of magnitude.

hep-ph↗

arXiv2025

Search for Long-lived Particles at Future Lepton Colliders Using Deep Learning Techniques

Long-lived particles (LLPs) provide an unambiguous signal for physics beyond the Standard Model (BSM). They have a distinct detector signature, with decay lengths corresponding to lifetimes of around nanoseconds or longer. Lepton colliders allow LLP searches to be conducted in a clean environment, and such searches can reach their full physics potential when combined with machine learning (ML) techniques.This experimental study, utilizing comprehensive full simulation data samples, focuses on LLP searches resulting from Higgs decay in $e^+e^-\to ZH$. We demonstrate that, by employing deep neural network approaches the LLP signal efficiency can be improved up to 95\% for an LLP mass around 50 GeV and a lifetime of approximately 1 nanosecond, while rejecting all SM backgrounds. Furthermore, the signal sensitivity for the branching ratio of Higgs decaying into LLPs reaches a state-of-art limit of $1.0 \times 10^{-6}$ with a statistics of $4 \times 10^{6}$ Higgs.

hep-ex↗

arXiv2025

Probing Temperature at Nanoscale through Thermal Vibration Characterization using Scanning Precession Electron Diffraction

Accurate, non-contact temperature measurement with high spatial resolution is essential for understanding thermal behavior in integrated nanoscale devices and heterogeneous interfaces. However, existing techniques are often limited by the need for physical contact or insufficient spatial resolution for the measurement of local temperature and mapping its distribution. Here, we showcase the direct temperature measurement of graphene with nanometer spatial resolution in transmission electron microscopy. In experiments, combining a scanning nanobeam with precession electron diffraction offers the collection of kinemetic diffraction from a local area at the nanometer scale. In analysis, we use a pre-calculated, sample-specific structure-factor-based correction method to enable the linear fitting of the diffraction intensities, allowing the determination of the Debye-Waller factor as a function of temperature at the precision of 10-4Å2/°C. With the high spatial resolution and measurement precision, the temperature and thermal vibration mapping further reveal the influence of graphene lattice parameters and thickness on the Debye-Waller factor, providing valuable insights into the vibrational properties impacted by temperature, lattice structure, and graphene layer thickness.

cond-mat.mtrl-sci↗

arXiv2023

SLOG: A Structural Generalization Benchmark for Semantic Parsing

The goal of compositional generalization benchmarks is to evaluate how well models generalize to new complex linguistic expressions. Existing benchmarks often focus on lexical generalization, the interpretation of novel lexical items in syntactic structures familiar from training; structural generalization tasks, where a model needs to interpret syntactic structures that are themselves unfamiliar from training, are often underrepresented, resulting in overly optimistic perceptions of how well models can generalize. We introduce SLOG, a semantic parsing dataset that extends COGS (Kim and Linzen, 2020) with 17 structural generalization cases. In our experiments, the generalization accuracy of Transformer models, including pretrained ones, only reaches 40.6%, while a structure-aware parser only achieves 70.8%. These results are far from the near-perfect accuracy existing models achieve on COGS, demonstrating the role of SLOG in foregrounding the large discrepancy between models' lexical and structural generalization capacities.

cs.CL↗

arXiv2023

Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement

The long-distance agreement, evidence for syntactic structure, is increasingly used to assess the syntactic generalization of Neural Language Models. Much work has shown that transformers are capable of high accuracy in varied agreement tasks, but the mechanisms by which the models accomplish this behavior are still not well understood. To better understand transformers' internal working, this work contrasts how they handle two superficially similar but theoretically distinct agreement phenomena: subject-verb and object-past participle agreement in French. Using probing and counterfactual analysis methods, our experiments show that i) the agreement task suffers from several confounders which partially question the conclusions drawn so far and ii) transformers handle subject-verb and object-past participle agreements in a way that is consistent with their modeling in theoretical linguistics.

cs.CL↗

arXiv2021

Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement

Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject. We take a critical look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks' syntactic ability. Our fine-grained analyses of results on the long-range French object-verb agreement show that contrary to LSTMs, Transformers are able to capture a non-trivial amount of grammatical structure.

cs.CL↗

arXiv2021

Systematic and Statistical Uncertainties of the Hilbert-Transform Based High-precision FID Frequency Extraction Method

Pulsed nuclear magnetic resonance (NMR) is widely used in high-precision magnetic field measurements. The absolute value of the magnetic field is determined from the precession frequency of nuclear magnetic moments. The Hilbert transform is widely used to extract the phase function from the observed free induction decay (FID) signal and then its frequency. In this paper, a detailed implementation of a Hilbert-transform based FID frequency extraction method is described. How artifacts and noise level in the FID signal affect the extracted phase function are derived analytically. A method of mitigating the artifacts in the extracted phase function of an FID is discussed. Correlations between noises of the phase function samples are studied for different noise spectra. We discovered that the error covariance matrix for the extracted phase function is nearly singular and improper for constructing the $χ^2$ used in the fitting routine. A down-sampling method for fixing the singular covariance matrix has been developed, so that the minimum $χ^2$-fit yields properly the statistical uncertainty of the extracted frequency. Other practical methods of obtaining the statistical uncertainty are also discussed.

physics.ins-det↗