arXiv subjects

Yuexin Wang

Publications and source records attributed to Yuexin Wang.

14 recordsLinked to original sources

arXiv2026

Learning from all particles in high-energy collisions

Particle colliders stand as an irreplaceable pillar of inquiry for exploring the fundamental building blocks of matter and forces of the Universe, yet fully decoding complex collision event information remains a significant challenge. Recent advances in artificial intelligence (AI) have revolutionized complex data analysis across scientific disciplines, inspiring novel strategies to extract the rich information embedded in collider events. Here we introduce two complementary concepts -- the holistic approach and Advanced Color Singlet Identification -- to enhance signal-background separation, which is a critical prerequisite for precise physics measurements. By leveraging all reconstructed particles and inferring their parentage via deep learning, these methods improve the precision of key Higgs physics benchmark measurements by up to sixfold and enable realistic prospects for observing rare Higgs decays previously deemed inaccessible. Our results demonstrate how integrating particle-level information with modern AI technologies can substantially boost the discovery potential of high-energy colliders, paving a new path to unravel the fundamental physical laws underlying particle physics experiments.

hep-ex↗

arXiv2026

Conceptual Design of a Novel Highly Granular Crystal Electromagnetic Calorimeter for Future Higgs Factories

Next-generation high-energy electron-positron colliders, operating as Higgs factories, require an unprecedented jet energy resolution for precision measurements of Higgs and Z/W bosons. To address this challenge, a conceptual design is presented for a novel high-granularity crystal electromagnetic calorimeter that combines the superior intrinsic energy resolution of a homogeneous calorimeter with the fine segmentation required for particle-flow reconstruction. The crystal electromagnetic calorimeter design is based on orthogonally arranged long scintillating crystal bars read out by silicon photomultipliers (SiPMs) at both ends. Key design specifications were established through comprehensive simulation studies. Critical technical considerations, including crystal choices, photosensors, electronics, mechanical support, and radiation damage, are discussed. A dedicated digitisation framework was developed to realistically model effects from the crystal, SiPMs, and readout electronics. The performance of a single calorimeter module was evaluated using simulated electron showers. Simulation results for a single module demonstrate an excellent electromagnetic energy resolution of $1.12\%/\sqrt{E(\mathrm{GeV})}\oplus0.22\%$ and an energy linearity within $\pm0.5\%$ for electrons from 3 GeV to 100 GeV. The performance significantly exceeds the design requirement of $\leq 3\%/\sqrt{E(\mathrm{GeV})}\oplus1\%$. The results establish the feasibility of the proposed high-granularity crystal calorimeter concept and point to a promising pathway toward the precision calorimetry required for future high-energy electron-positron collider experiments.

physics.ins-det↗

arXiv2025

Prospect for measurement of CP-violating parameters of $B_s^0 \to ϕγ$ at the Tera Z factory

$b \to sγ$ transition is a critical flavor-changing neutral current (FCNC) process that could be used to probe CP violation (CPV) and new physics (NP). We quantify the anticipated precision for measuring $B_s^0 \to ϕγ$ at the CEPC Z pole operation, showing that the relative statistical uncertainty could be as low as 0.16\%, improved by approximately two orders of magnitude compared to existing measurements. Additionally, we perform a time-dependent analysis of the $B_s^0 \to ϕγ$ decay, accounting for $B_s^0/\bar{B}_s^0$ mixing extract the mixing-induced and CP-violating parameters $\boldsymbol{\mathcal{A}_{ϕγ}^Δ}$, $\boldsymbol{C_{ϕγ}}$ and $\boldsymbol{S_{ϕγ}}$. Using central value from LHCb measurement as input, we evaluate the anticipated accuracy of measurements of these parameters. The projected statistical uncertainties are $σ_{A_{ϕγ}^Δ{}^{\text{stat}}} = 0.021$, $σ_C^{\text{stat}} = 0.0092$ and $σ_S^{\text{stat}} = 0.0096$, and the systematic uncertainties are $σ_{A_{ϕγ}^Δ{}^{\text{syst}}} = 0.035$, $σ_C^{\text{syst}} = 0.0027$ and $σ_S^{\text{syst}} = 0.0064$. Furthermore, the 1$σ$ sensitivity boundaries for NP in this study are found to be $\mathcal{A}_{ϕγ}^Δ< -0.05$ or $\mathcal{A}_{ϕγ}^Δ> 0.15$, $\mathcal{C}_{ϕγ} < -0.02$ or $\mathcal{C}_{ϕγ} > 0.04$, and $\mathcal{S}_{ϕγ} < -0.04$ or $\mathcal{S}_{ϕγ} > 0.04$. We also conduct a relevant detector optimization study by establishing the correlation between the anticipated precision and the intrinsic resolution of the ECAL, as well as the performance of the PID system.

hep-ex↗

arXiv2025

Normal-Abnormal Guided Generalist Anomaly Detection

Generalist Anomaly Detection (GAD) aims to train a unified model on an original domain that can detect anomalies in new target domains. Previous GAD methods primarily use only normal samples as references, overlooking the valuable information contained in anomalous samples that are often available in real-world scenarios. To address this limitation, we propose a more practical approach: normal-abnormal-guided generalist anomaly detection, which leverages both normal and anomalous samples as references to guide anomaly detection across diverse domains. We introduce the Normal-Abnormal Generalist Learning (NAGL) framework, consisting of two key components: Residual Mining (RM) and Anomaly Feature Learning (AFL). RM extracts abnormal patterns from normal-abnormal reference residuals to establish transferable anomaly representations, while AFL adaptively learns anomaly features in query images through residual mapping to identify instance-aware anomalies. Our approach effectively utilizes both normal and anomalous references for more accurate and efficient cross-domain anomaly detection. Extensive experiments across multiple benchmarks demonstrate that our method significantly outperforms existing GAD approaches. This work represents the first to adopt a mixture of normal and abnormal samples as references in generalist anomaly detection. The code and datasets are available at https://github.com/JasonKyng/NAGL.

cs.CV↗

arXiv2025

BigBang-Proton Technical Report: Next-Word-Prediction is Scientific Multitask Learner

We introduce BigBang-Proton, a unified sequence-based architecture for auto-regressive language modeling pretrained on cross-scale, cross-structure, cross-discipline real-world scientific tasks to construct a scientific multi-task learner. BigBang-Proton incorporates three fundamental innovations compared to mainstream general-purpose LLMs: Theory-Experiment Learning paradigm aligns large-scale numerical experimental data with theoretical text corpora; Binary Patch Encoding replaces byte pair encoding(BPE) tokenization; Monte Carlo Attention substitutes traditional transformer architectures. Through next-word-prediction pretraining on cross-discipline scientific datasets of real-world problems mixed with general textual corpus, followed by fine-tuning and inference on downstream tasks, BigBang-Proton demonstrates 100\% accuracy in up to 50-digit arithmetic addition operations, performance on par with leading specialized models in particle physics jet tagging, matching MAE of specialized models in inter-atomic potential simulation, performance comparable to traditional spatiotemporal models in water quality prediction, and benchmark-exceeding performance in genome modeling. These results prove that language-guided scientific computing can match or exceed the performance of task-specific scientific models while maintaining multitask learning capabilities. We further hypothesize to scale the pretraining to the universe scale as a fundamental step toward developing material world foundational model.

cs.LG↗

arXiv2025

One-to-one correspondence reconstruction at the electron-positron Higgs factory

We propose one-to-one correspondence reconstruction for electron-positron Higgs factories. For each visible particle, one-to-one correspondence aims to associate relevant detector hits with only one reconstructed particle and accurately identify its species. To achieve this goal, we develop a novel detector concept featuring 5-dimensional calorimetry that provides spatial, energy, and time measurements for each hit, and a reconstruction framework that combines state-of-the-art particle flow and artificial intelligence algorithms. In the benchmark process of Higgs to di-jets, over 90% of visible energy can be successfully mapped into well-reconstructed particles that not only maintain a one-to-one correspondence relationship but also associate with the correct combination of cluster and track, improving the invariant mass resolution of hadronically decayed Higgs bosons by 25%. Performing simultaneous identification on these well-reconstructed particles, we observe efficiencies of 97% to nearly 100% for charged particles ($e^{\pm}$, $μ^{\pm}$, $π^{\pm}$, $K^{\pm}$, $p/\bar{p}$) and photons ($γ$), and 75% to 80% for neutral hadrons ($K_L^0$, $n$, $\bar{n}$). For physics measurements of Higgs to invisible and exotic decays, golden channels to probe new physics, one-to-one correspondence could enhance discovery power by 10% to up to a factor of two. This study demonstrates the necessity and feasibility of one-to-one correspondence reconstruction at electron-positron Higgs factories.

hep-ex↗

arXiv2025

VTD-CLIP: Video-to-Text Discretization via Prompting CLIP

Vision-language models bridge visual and linguistic understanding and have proven to be powerful for video recognition tasks. Existing approaches primarily rely on parameter-efficient fine-tuning of image-text pre-trained models, yet they often suffer from limited interpretability and poor generalization due to inadequate temporal modeling. To address these, we propose a simple yet effective video-to-text discretization framework. Our method repurposes the frozen text encoder to construct a visual codebook from video class labels due to the many-to-one contrastive alignment between visual and textual embeddings in multimodal pretraining. This codebook effectively transforms temporal visual data into textual tokens via feature lookups and offers interpretable video representations through explicit video modeling. Then, to enhance robustness against irrelevant or noisy frames, we introduce a confidence-aware fusion module that dynamically weights keyframes by assessing their semantic relevance via the codebook. Furthermore, our method incorporates learnable text prompts to conduct adaptive codebook updates. Extensive experiments on HMDB-51, UCF-101, SSv2, and Kinetics-400 have validated the superiority of our approach, achieving more competitive improvements over state-of-the-art methods. The code will be publicly available at https://github.com/isxinxin/VTD-CLIP.

cs.CV↗

arXiv2024

Scaling Particle Collision Data Analysis

For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particularly in domains involving large-scale numerical data analysis, such as experimental high energy physics. This limitation is primarily due to BPE tokenization's inefficacy with numerical data. In this paper, we propose a task-agnostic architecture, BBT-Neutron, which employs a binary tokenization method to facilitate pretraining on a mixture of textual and large-scale numerical experimental data. We demonstrate the application of BBT-Neutron to Jet Origin Identification (JoI), a critical categorization challenge in high-energy physics that distinguishes jets originating from various quarks or gluons. Our results indicate that BBT-Neutron achieves comparable performance to state-of-the-art task-specific JoI models. Furthermore, we examine the scaling behavior of BBT-Neutron's performance with increasing data volume, suggesting the potential for BBT-Neutron to serve as a foundational model for particle physics data analysis, with possible extensions to a broad spectrum of scientific computing applications for Big Science experiments, industrial manufacturing and spacial computing. The project code is available at https://github.com/supersymmetry-technologies/bbt-neutron.

cs.LG↗

arXiv2024

Jet-Origin Identification and Its Application at an Electron-Positron Higgs Factory

To enhance the scientific discovery power of high-energy collider experiments, we propose and realize the concept of jet origin identification that categorizes jets into 5 quark species $(b,c,s,u,d)$, 5 anti-quarks $(\bar{b},\bar{c},\bar{s},\bar{u},\bar{d})$, and the gluon. Using state-of-the-art algorithms and simulated $ν\barνH, H\rightarrow jj$ events at 240 GeV center-of-mass energy at the electron-positron Higgs factory, the jet origin identification simultaneously reaches jet flavor tagging efficiencies ranging from 67% to 92% for bottom, charm, and strange quarks, and jet charge flip rates of 7% to 24% for all quark species. We apply the jet origin identification to Higgs rare and exotic decay measurements at the nominal luminosity of the Circular Electron Positron Collider (CEPC), and conclude that the upper limits on the branching ratios of $H\rightarrow s \bar{s}, u\bar{u}, d\bar{d}$, and $H\rightarrow sb, db, uc, ds$ can be determined to $2\!\!\times\!\!10^{-4}$ to $1\!\!\times\!\!10^{-3}$ at 95% confidence level. The derived upper limit for $H\rightarrow s \bar{s}$ decay is approximately three times the prediction of the Standard Model.

hep-ex↗

arXiv2024

Jet charge identification in ee-Z-qq process at Z pole operation

Accurate jet charge identification is essential for precise electroweak and flavor measurements at the high-energy frontier. We propose a novel method called the Leading Particle Jet Charge method (LPJC) to determine the jet charge based on information about the leading charged particle. Tested on Z - bb and Z - cc samples at a center-of-mass energy of 91.2GeV, the LPJC achieves an effective tagging power of 20%/9% for the c/b jet, respectively. Combined with the Weighted Jet Charge method (WJC), we develop a Heavy Flavor Jet Charge method (HFJC), which achieves an effective tagging power of 39%/20% for c/b jet, respectively. This paper also discusses the dependencies between jet charge identification performance and the fragmentation process of heavy flavor jets, and critical detector performances.

hep-ex↗

arXiv2023

ParticleNet and its application on CEPC Jet Flavor Tagging

Identification of quark flavor is essential for collider experiments in high-energy physics, relying on the flavor tagging algorithm. In this study, using a full simulation of the Circular Electron Positron Collider (CEPC), we investigated the flavor tagging performance of two different algorithms: ParticleNet, originally developed at CMS, and LCFIPlus, the current flavor tagging algorithm employed at CEPC. Compared to LCFIPlus, ParticleNet significantly enhances flavor tagging performance, resulting in a significant improvement in benchmark measurement accuracy, i.e., a 36% improvement for $ν\barνH\to c\bar{c}$ measurement and a 75% improvement for $|V_{cb}|$ measurement via W boson decay when CEPC operates as a Higgs factory at the center-of-mass energy of 240 GeV and integrated luminosity of 5.6 $ab^{-1}$. We compared the performance of ParticleNet and LCFIPlus at different vertex detector configurations, observing that the inner radius is the most sensitive parameter, followed by material budget and spatial resolution.

hep-ex↗

arXiv2023

A conceptual design of TOF based on MRPC technology for the future electron-positron Higgs factory

Future electron-positron Higgs factories could provide excellent opportunities to examine the Standard Model and search for new physics with much higher precision than the LHC. A precise particle identification is crucial for the physics program at these future colliders and can be achieved via precise time-of-flight (TOF) measurements of the final state particles. In this paper, we propose a conceptual design of TOF system based on the multigap resistive plate chamber (MRPC) technology for future electron-positron Higgs factories. This TOF system has a time resolution of < 35 ps, a total active area of 77m2, and a construction budget of the order of 5 million USD. Keywords: MRPC, TOF, PID, CEPC

hep-ex↗

arXiv2022

Prospects for $B^0_{(s)}\toπ^0π^0$ and $B^0_{(s)}\toηη$ modes and corresponding $CP$ asymmetries at Tera-$Z$

The physics potential of measuring $B^0_{(s)}\toπ^0π^0$ and $B^0_{(s)}\toηη$ decays via four-photon final states at Tera-$Z$ phase of CEPC or FCC-ee is investigated in this paper. We propose an electromagnetic calorimeter (ECAL) with both high energy resolution and excellent separation power to efficiently reconstruct $π^0$ and $η$ from hadronic final states with high photon multiplicity. The resulting $B$-meson mass resolution is approximately 30 MeV, allowing 3 $σ$ separation between $B^0$ and $B_s^0$. With the assistance of the $b$-jet tagging, the relative sensitivities to $B^0\toπ^0π^0$, $B^0_s\toπ^0π^0$, $B^0\toηη$, and $B^0_s\toηη$ signal strengths at Tera-$Z$ are projected as 0.45%, 4.5%, 18%, and 0.95%, respectively. Their dependence on various detector performances is also discussed. In addition, $B^0\toπ^0π^0$ and its two isospin-related modes are paid special attention due to their roles in the determination of the CKM angle $α$ ($ϕ_2$). The anticipated precisions of their branching-ratio and $CP$-asymmetry measurements at Tera-$Z$ are evaluated. We show that the measurement of the time-integrated $B^0\toπ^0π^0$ $CP$ asymmetry at Tera-$Z$ is complementary to $B$-factory ones. The precision on $α$ combining $Z$- and $B$-factory results reaches $0.4^\circ$, lower than the systematic uncertainties attached to isospin breaking.

hep-ph↗

arXiv2022

Analysis of $B_s\toϕν\barν$ at CEPC

The rare $b\to sν\barν$ decays are sensitive to contributions of new physics (NP) and helpful to resolve the puzzle of multiple $B$ flavor anomalies. In this work, we propose to study the $b\to sν\barν$ transition at a future lepton collider operating at the $Z$ pole through the $B_s \to ϕν\barν$ decay. Using the $B_s\toϕ$ decay form factors from lattice simulations, we first update the SM prediction of BR($B_s \to ϕν\barν)_{\mathrm{SM}}=(9.93\pm 0.72)\times 10^{-6}$ and the corresponding $ϕ$ longitudinal polarization fraction $F_{L,{\mathrm{SM}}}=0.53\pm 0.04$. Our analysis uses the full CEPC simulation samples with a net statistic of $\mathcal{O}(10^9)$ $Z$ decays. Precise $ϕ$ and $B_s$ reconstructions are used to suppress backgrounds. The results show that BR($B_s \to ϕν\barν)$ can be measured with a statistical uncertainty of $\mathcal{O}(\%)$ and an $S/B$ ratio of $\mathcal{O}(1)$ at the CEPC. The quality measures for the event reconstruction are also derived. By combining the measurement of BR($B_s \to ϕν\barν)$ and $F_L$, the constraints on the effective theory couplings at low energy are given.

hep-ph↗