arXiv subjects

Zhiyu Sun

Publications and source records attributed to Zhiyu Sun.

At least 19 recordsLinked to original sources

arXiv2026

Projected Sensitivity to Slow Muonphilic Dark Matter with Accelerator Muon Beams

The nature of dark matter (DM) remains one of the most enduring open questions in modern physics, and muonphilic DM has emerged as a promising scenario that complements traditional DM candidates. Following the recently established cosmic-ray muon scattering approach, we investigate the sensitivity for probing slow muonphilic DM with accelerator muon beams. A Geant4-based simulation framework is developed, incorporating the detector geometry from the PKMu muon tomography system and a dedicated elastic $\mu$-DM scattering process. The projected sensitivity is found to be largely insensitive to both the beam energy and the transverse beam size when the beam is fully contained within the detector acceptance. For a benchmark beam intensity of $10^5/\rm{s}$, the simulated pure-muon beam surpasses the existing cosmic-ray limit of $1.61\times10^{-17}$ cm$^2$ at $m_{\rm DM}=1$ GeV within approximately 11 seconds. A realistic muon beam phase-space distribution based on simulations for the High Intensity heavy-ion Accelerator Facility (HIAF) is also implemented, yielding projected limits that improve upon the cosmic-ray results by nearly two orders of magnitude in a one-day exposure. These results demonstrate that a beam-muon scattering experiment offers a robust and promising route toward significantly improved sensitivity to slow muonphilic DM.

hep-ex↗

arXiv2026

Low-energy Muon-Nucleon scattering experiment: LUNE (White Paper)

The HIAF will provide high-intensity, high-quality muon beams with momenta from 0.5 to 7.5 GeV/c. This energy range is uniquely suited for precision muon scattering, bridging the gap between low-energy electron facilities and future high-energy lepton-ion colliders. In particular, HIAF will enable precision measurements with both positive and negative muon beams over a broad kinematic range, complementing existing electron-scattering facilities such as JLab, EicC and EIC. Based on HIAF muon source, the LUNE Collaboration has been established to address several fundamental questions in nuclear and particle physics, including the proton charge radius puzzle, nucleon electromagnetic structure, and the dynamics of quantum electrodynamics and hadronic interactions. The program proceeds in two phases, from elastic scattering to nucleon structure and beyond-Standard-Model searches. The experiment is expected to determine the proton charge radius with a precision of approximately 1.0\% using elastic muon-proton scattering. It will also perform systematic measurements of the proton electromagnetic form factors with both $\mu^+$ and $\mu^-$ beams, enabling precise studies of two-photon exchange effects and stringent tests of quantum electrodynamics. Beyond elastic scattering, LUNE will investigate TMD, gravitational form factors, and nuclear charge radii, providing new insights into the 3D structure of nucleons and nuclei. The experiment will further address important topics including Coulomb-distortion corrections, nuclear medium effects, and possible signatures of physics beyond the Standard Model. This white paper presents the scientific motivation, detector concept, expected performance, and long-term strategy of LUNE.

hep-ex↗

arXiv2026

Production of lepton-flavor-violating scalars through resonant positive-muon annihilation on atomic electrons

We investigate an invisible lepton-flavor-violating scalar $\phi$ with exclusive $e-\mu$ couplings and study its resonant production via $\mu^+e^-\to\phi$ in fixed-target experiments. Since the effective center-of-mass energy is determined by the momentum of the initial-state bound electrons, atomic effects can significantly affect the resonance behavior. We therefore employ relativistic bound-state electron wave functions to calculate the production cross section and reveal a material-dependent broadening of the resonance lineshape. For the proposed HIAF experiment, fewer than one day of data taking ($6\times10^{10}$ MOT) can probe couplings at the $10^{-5}$ level at 90\% confidence level near resonance, demonstrating that high-intensity muon fixed-target experiments provide a powerful complementary probe of lepton-flavor violation.

hep-ph↗

arXiv2026

The muon Moonshot: Moon subsurface tomography with upward-going muons

We propose a novel muon Moonshot concept for lunar subsurface tomography based on upward-going muons originated from the lunar regolith. Unlike the Earth, the Moon lacks an atmosphere, leaving a dense regolith below and a near-vacuum environment above. Consequently, while most downward-going hadrons are absorbed before decaying, upward-going hadrons escaping the regolith can decay in flight, producing a significant source of lunar muons. These muons are detectable by instruments on the lunar surface or in near-lunar orbit. We perform Monte Carlo simulations to investigate their energy spectra, angular distributions, and integrated fluxes under various theoretical and detector configurations. The results indicate that the lunar muon flux is sensitive to detector altitude under a flat-terrain assumption, demonstrating its potential as a novel non-invasive probe of shallow subsurface voids. We also present case studies on the detection of underground cavities and water resources, with cavity-induced flux variations observable in less than two minutes and weaker water signals distinguishable after about 36 minutes of data collection with a $1~\mathrm{m^2}$ detector, and discuss potential implementations in future lunar missions.

astro-ph.EP↗

arXiv2026

Improving Muon-Scattering Material Identification via Coarse Momentum Encoding and Unsupervised Domain Adaptation

Cosmic-ray muon scattering has shown considerable potential for detecting nuclear materials and other dense contraband, but practical deployment remains challenging. A major difficulty arises from the coupling between material properties and muon momentum, since the broad natural momentum distribution influences the scattering angle and prevents unambiguous material identification. In this work, we propose a Coarse Momentum-Aware Domain Adaptation (CMADA) method to enable precise identification of materials. Instead of relying on high-precision momentum measurements, the proposed framework adopts coarse momentum binning combined with unsupervised domain adaptation to learn transferable scattering representations. In addition, a precision review mode based on averaging repeated samplings was proposed to further enhances identification performance. The coarse momentum binning strategy improves same-domain identification accuracy from 62.15% without momentum information to 89.52% with 5-bin momentum information, and further to 93.37% (precision review mode). Furthermore, the proposed unsupervised domain adaptation framework improves the cross-domain identification accuracy from 71.71% for the source-only baseline to 89.00% without requiring target domain labels.

physics.ins-det↗

arXiv2026

Protecting K-Nearest Neighbor Queries from Location Inference Attacks

The k-nearest neighbor query (kNNQ) is a core component of modern location-based services (LBS) and has been widely adopted in popular features such as ``people nearby''. However, its potential privacy risks have long been overlooked. In this work, we present the first two attacks against kNNQ, namely the geometric intersection location inference attack (GI-LIA) and the zero-order optimization location inference attack (ZO-LIA), revealing the inherent location privacy risks posed by kNNQ. To mitigate these privacy risks, we further propose DPRS, a differential privacy framework for kNNQ protection. The core idea of DPRS is to incorporate a rejection sampling mechanism within a constrained perturbation interval, thereby mitigating the distance distortion caused by excessive noise injection. In addition, we design a private interval construction algorithm to construct the perturbation interval, enabling the rejection sampling mechanism to achieve a more favorable trade-off between privacy protection and query utility in kNNQ. Extensive experiments on real-world spatial datasets demonstrate that DPRS outperforms existing methods in both privacy protection and query utility. Our code is available at https://github.com/reanatom/DPRS.

cs.CR↗

arXiv2026

Wave-number-dependent closure condition for fluid moment equations

Fluid models offer crucial computational efficiency for plasma simulations, yet accurately capturing kinetic effects like Landau damping remains a fundamental challenge. While conventional closures (e.g., Hammett-Perkins and Hunana) are widely used, their fidelity relative to exact kinetic response degrades significantly depending on the perturbation wave number. Here, we propose a novel wave-number-dependent closure condition for the three-moment fluid equations that explicitly preserves the primary dispersion relation. By mapping Pad\'e approximant coefficients directly to the kinetic roots of the collisionless Vlasov-Poisson system, we derive an analytical closure that rigorously embeds exact kinetic scaling across all spatial scales. We further demonstrate that this framework readily extends to collisional plasmas via the BGK model. This deterministic approach precisely captures the long-term macroscopic evolution of fluid moments and field energy, offering a rigorous foundation for high-fidelity fluid modeling.

physics.plasm-ph↗

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 $\phi$) 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 $\mu^-$ run, we highlight the potential of a complementary $\mu^+$ beam option, further improving sensitivity to dark matter below 200 $\mathrm{MeV}$ by an order of magnitude.

hep-ph↗

arXiv2026

Permutation invariant multi-scale full quantum neural network wavefunction

Solving the intricate quantum behavior of interacting particles is key to unlocking the mysteries of condensed matter, but capturing their complex correlations across different scales remains a monumental challenge. We introduce a neural network framework that overcomes this barrier by modeling the full quantum wavefunction of a system, including electrons, nuclei and muons, directly capturing the full quantum effects beyond the Born-Oppenheimer approximation. The neural network approximates joint wavefunction of different interacting particles with a rigorous handling of permutation invariance, enabling simultaneous treatment of nuclear quantum effects and electron-nucleus-muon couplings without explicit excited states. Validated on molecular systems, this approach offers a computationally feasible way to model full quantum phenomena in complex many-body systems, establishing a direct connection between fundamental particle properties and emergent material behavior.

physics.chem-ph↗

arXiv2026

Reverse-Engineering Model Editing on Language Models

Large language models (LLMs) are pretrained on corpora containing trillions of tokens and, therefore, inevitably memorize sensitive information. Locate-then-edit methods, as a mainstream paradigm of model editing, offer a promising solution by modifying model parameters without retraining. However, in this work, we reveal a critical vulnerability of this paradigm: the parameter updates inadvertently serve as a side channel, enabling attackers to recover the edited data. We propose a two-stage reverse-engineering attack named \textit{KSTER} (\textbf{K}ey\textbf{S}paceRecons\textbf{T}ruction-then-\textbf{E}ntropy\textbf{R}eduction) that leverages the low-rank structure of these updates. First, we theoretically show that the row space of the update matrix encodes a ``fingerprint" of the edited subjects, enabling accurate subject recovery via spectral analysis. Second, we introduce an entropy-based prompt recovery attack that reconstructs the semantic context of the edit. Extensive experiments on multiple LLMs demonstrate that our attacks can recover edited data with high success rates. Furthermore, we propose \textit{subspace camouflage}, a defense strategy that obfuscates the update fingerprint with semantic decoys. This approach effectively mitigates reconstruction risks without compromising editing utility. Our code is available at https://github.com/reanatom/EditingAttack.

cs.CR↗

arXiv2026

U-Net Based Image Enhancement for Short-time Muon Scattering Tomography

Muon Scattering Tomography (MST) is a promising non-invasive inspection technique, yet the practical application of short-time MST is hindered by poor image quality due to limited muon flux. To address this limitation, we propose a U-Net-based framework trained on Point of Closest Approach (PoCA) images reconstructed with simulation MST data to enhance image quality. When applied to experimental MST data, the framework significantly improves image quality, increasing the Structural Similarity Index Measure (SSIM) from 0.7232 to 0.9699 and decreasing the Learned Perceptual Image Patch Similarity (LPIPS) from 0.3604 to 0.0270. These results demonstrate that our method can effectively enhance low-statistics MST images, thereby paving the way for the practical deployment of short-time MST.

eess.IV↗

arXiv2025

CANTON-$\mu$ 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-$\mu$ (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↗

arXiv2025

Search for light Dark Sectors with GeV Muon Beams

Sub-GeV light dark matter often requires new light mediators, such as a dark $Z$ boson in the $L_\mu - L_\tau$ gauge theory. We study the search potential for such a $Z^\prime$ boson via the process $\mu e^- \to \mu e^- X$, with $X$ decaying invisibly, in a muon on-target experiment using a high-intensity 1-10 GeV muon beam from facilities such as HIAF-HIRIBL. Events are identified by the scattered muon and electron from the target using silicon strip detectors in a single-station telescope system. Backgrounds are suppressed through a trained boosted decision tree (BDT) classifier, and activity in downstream subdetectors remains low. This approach can probe a $Z^\prime$ boson in the 10 MeV mass range with improved sensitivity. Nearly three orders of magnitude improvement is achievable with a full multi-telescope station system employing a 160 GeV muon beam at CERN, such as in the MUonE experiment.

hep-ph↗

arXiv2025

Muonium Spectroscopy as a Quantum Sensor for Ultralight Axion Dark Matter

High-intensity muon beams could enable a Muonium-based Axion Search through resonant quantum transitions between Hyperfine states (MASH). Combining theoretical calculations with simulation results, we demonstrate that such a muonium-based experimental approach could complement and tighten constraints on the axion-muon coupling beyond existing limits from the muon $g\!-\!2$ measurement, over the axion mass range of 18--130 $\mu$eV. These results establish a new spectroscopic channel in muonium that enables searches for axion and axion-like particle dark matter.

hep-ph↗

arXiv2025

Improving Muon Scattering Tomography Performance With A Muon Momentum Measurement Scheme

Muon imaging, especially muon scattering tomography (MST), has recently garnered significant attention. MST measures the magnitude of muon scattering angles inside an object, which depends not only on the material properties but also on the muon momentum. Due to the difficulty of simultaneous measurement of momentum, it was neglected and taken as a constant in multiple MST reconstruction algorithms. Recently, an experimental measurement scheme has emerged that is feasible in engineering, but it requires many layers of detectors to approach the true momentum. From this, we proposed both an algorithm to incorporating momentum into MST, and a scheme to determine the thresholds of Cherenkov detectors. This novel scheme, termed the "equi-percentage scheme", sets momentum thresholds for Cherenkov detector layers based on cosmic muon momentum distribution. Results showed our approach delivers noticeable enhancement in reconstructed image quality even with only two detector layers, reaching near-saturation performance with four layers. This study proves that momentum measurement significantly enhances short-duration MST, and that substantial improvement can be achieved with relatively coarse momentum measurement using 2-4 layers of Cherenkov detectors.

physics.ins-det↗

arXiv2025

BitParticle: Partializing Sparse Dual-Factors to Build Quasi-Synchronizing MAC Arrays for Energy-efficient DNNs

Bit-level sparsity in quantized deep neural networks (DNNs) offers significant potential for optimizing Multiply-Accumulate (MAC) operations. However, two key challenges still limit its practical exploitation. First, conventional bit-serial approaches cannot simultaneously leverage the sparsity of both factors, leading to a complete waste of one factor' s sparsity. Methods designed to exploit dual-factor sparsity are still in the early stages of exploration, facing the challenge of partial product explosion. Second, the fluctuation of bit-level sparsity leads to variable cycle counts for MAC operations. Existing synchronous scheduling schemes that are suitable for dual-factor sparsity exhibit poor flexibility and still result in significant underutilization of MAC units. To address the first challenge, this study proposes a MAC unit that leverages dual-factor sparsity through the emerging particlization-based approach. The proposed design addresses the issue of partial product explosion through simple control logic, resulting in a more area- and energy-efficient MAC unit. In addition, by discarding less significant intermediate results, the design allows for further hardware simplification at the cost of minor accuracy loss. To address the second challenge, a quasi-synchronous scheme is introduced that adds cycle-level elasticity to the MAC array, reducing pipeline stalls and thereby improving MAC unit utilization. Evaluation results show that the exact version of the proposed MAC array architecture achieves a 29.2% improvement in area efficiency compared to the state-of-the-art bit-sparsity-driven architecture, while maintaining comparable energy efficiency. The approximate variant further improves energy efficiency by 7.5%, compared to the exact version. Index-Terms: DNN acceleration, Bit-level sparsity, MAC unit

cs.AR↗

arXiv2025

Transfer learning empowers material Z classification with muon tomography

Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers (Z values), facilitating the identification of various Z-class materials, particularly those radioactive high-Z nuclear elements. Most of the traditional identification methods are based on complex muon event reconstruction and trajectory fitting processes. Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of target materials, significantly limiting their practical applicability in detecting concealed materials. For the first time, transfer learning is introduced into the field of muon tomography in this work. We propose two lightweight neural network models for fine-tuning and adversarial transfer learning, utilizing muon tomography data of bare materials to predict the Z-class of coated materials. By employing the inverse cumulative distribution function method, more accurate scattering angle distributions could be obtained from limited data, leading to an improvement by nearly 4\% in prediction accuracy compared with the traditional random sampling based training. When applied to coated materials with limited labeled or even unlabeled muon tomography data, the proposed method achieves an overall prediction accuracy exceeding 96\%, with high-Z materials reaching nearly 99\%. Simulation results indicate that transfer learning improves prediction accuracy by approximately 10\% compared to direct prediction without transfer. This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data, highlights the promising potential of transfer learning in the field of muon tomography.

physics.ins-det↗

arXiv2025

Probing and Knocking with Muons

We propose here a set of new methods involving probing and knocking with muons (PKMu). There is a wealth of rich physics to explore with GeV muon beams. Examples include but not limited to: muon scattering can occur at large angles, providing evidence of potential muon-philic dark matter or dark mediator candidates; muon-electron scattering can be used to detect new types of bosons associated with charged lepton flavor violation; precise measurements of GeV-scale muon-electron scattering can be employed to probe quantum correlations.

hep-ph↗