arXiv subjects

Yongfeng Zhu

Publications and source records attributed to Yongfeng Zhu.

At least 19 recordsLinked to original sources

arXiv2026

Deep-learning jet flavor tagging for precision hadronic Higgs measurements at future $e^+e^-$ Higgs factories

Precise measurements of Higgs decays into quarks and gluons are essential for probing the Yukawa couplings of the Higgs boson and testing the flavor structure of the Standard Model. We investigate the process $e^+e^- \to ZH$ at $\sqrt{s}=240~\mathrm{GeV}$ at a future $e^+e^-$ Higgs factory, taking the CEPC design as a benchmark. The analysis focuses on events with $Z\toν\barν$ and hadronic Higgs decays $H\to b\bar{b}$, $c\bar{c}$, $s\bar{s}$ and $gg$. Jet flavor is identified using state-of-the-art particle-level deep neural network taggers (ParticleNet, Particle Transformer and More-Interaction Particle Transformer), whose per-jet outputs are combined with global event observables in a two-stage analysis employing XGBoost classifiers to separate the four Higgs decay modes from the dominant two- and four-fermion Standard Model backgrounds. Assuming an integrated luminosity of $20~\mathrm{ab}^{-1}$, we obtain projected relative precision on $σ(ZH)\times\mathrm{Br}(H\to X)$ of 0.17% for $X=b\bar{b}$, 1.06% for $c\bar{c}$, 0.50% for $gg$ and 68% for $s\bar{s}$. Compared with the CEPC published results, the precisions for $H\to c\bar{c}$ and $H\to gg$ are improved by about 43% and 29%, respectively. For $H\to s\bar{s}$ we present a quantitative sensitivity estimation corresponding to a statistical significance of about $1.5σ$. These results highlight the potential of deep-learning-based jet flavor tagging for precision studies of Higgs decays at future $e^+e^-$ Higgs factories.

hep-ph↗

arXiv2026

Rongzai agent: A Large Language Model-Based Autonomous Assistant for Rietveld Refinement of Neutron Diffraction Data

Neutron diffraction (ND) is an indispensable technique for determining atomic positions (especially light elements) and thus serves as a critical probe for revealing microscopic structures in materials science. However, traditional Rietveld refinement of ND data relies heavily on manual operation of specialized software, which is time-consuming, labor-intensive, and highly dependent on user expertise, severely hindering automated analysis. The automation of Rietveld refinement has long been a long-standing and challenging problem in crystallography. To address this challenge, this paper presents the Dr.Sai-Rongzai agent, an autonomous refinement assistant based on a large language model (LLM), a specialist knowledge base, and the GSAS-II refinement engine, achieving for the first time an intelligent refinement that integrates knowledge-driven decision-making. The agent accomplishes a fully automated workflow from natural language task parsing to autonomous decision-making, execution of refinement strategies, and report generation. Evaluation on five representative samples shows that the Rongzai agent achieves lower Rwp values than human specialists on three samples (2.88% vs. 4.42%, 5.06% vs. 5.40%, 7.60% vs. 9.00%), while on the other two samples its results are very close to those of the specialists. The agent is currently deployed at the China Spallation Neutron Source (CSNS) and is open for external user registration, providing an intelligent and user-friendly analytical tool for materials research. This work fully leverages the cutting-edge advantages of LLM, offers a new path to solve the long-standing problem of automated refinement, takes a key step toward intelligent and fully automated crystallographic analysis, and holds great potential to accelerate AI for Science discoveries in neutron-based materials characterization.

cond-mat.mtrl-sci↗

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

Evaluation of PID Performance at CEPC and Optimization with Combined dN/dx and Time-of-Flight Data

Charged-hadron identification (PID) is a critical requirement for the physics program of the Circular Electron-Positron Collider (CEPC). The baseline detector relies on ionization measurements from a time projection chamber (TPC), which provides strong PID capability at low momenta but becomes less effective at higher momenta. In this work, we investigate an extended PID strategy that combines dN/dx information from the TPC with time-of-flight (ToF) measurements from a silicon-based outer tracker (OTK) and a timing-upgraded inner tracker (ITK) equipped with AC-LGAD sensors. A unified discriminant is constructed to exploit the complementary sensitivity of ionization and timing observables. The performance is evaluated using simulated $Z \to q\bar{q}$ events, focusing on kaon identification in the presence of dominant pion backgrounds. The combined configuration significantly improves both efficiency and purity over a broad kinematic range, extending PID capability to both sub-GeV and multi-GeV regions. These results highlight the impact of precision timing on tracking detectors and demonstrate a viable path to enhanced PID performance for future lepton colliders.

hep-ex↗

arXiv2026

Hadronic decay branching ratio measurements of the Higgs boson at future colliders using the Holistic Approach

Accurately measuring the properties of the Higgs boson is one of the primary physics objectives of the high-energy frontier. By incorporating the inclusive information of all reconstructed particles to identify the signal events, referred to as the holistic approach, we estimate the relative statistical uncertainty for the Higgs hadronic decay modes $H \to b\bar{b}$, $c\bar{c}$, $gg$, $WW^{*} \to 4q$, and $ZZ^{*} \to 4q$ at the Circular Electron--Positron Collider (CEPC) operating as a Higgs factory with an integrated luminosity of 21.6~ab$^{-1}$. In the $Z(μ^{+}μ^{-})H$ and $Z(ν\barν)H$ channels, the relative statistical uncertainties for these decay modes are projected to range from 0.36\% to 5.21\% and 0.16\% to 2.52\%, respectively. Compared to the CEPC Snowmass results, the holistic approach boosts the measurement precision by a factor of two to four. The scaling behavior, specifically the dependence of the anticipated accuracy on the training dataset size, is observed and analyzed. The precision of these leading Higgs decay modes, especially the $H \to b\bar{b}$ mode, is asymptotically approaching the statistical limit. The scaling behavior could also be applied to monitor the robustness and to quantify the uncertainties of the holistic approach.

hep-ex↗

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

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

A Novel Quantum Realization of Jet Clustering in High-Energy Physics Experiments

Exploring the application of quantum technologies to fundamental sciences holds the key to fostering innovation for both sides. In high-energy particle collisions, quarks and gluons are produced and immediately form collimated particle sprays known as jets. Accurate jet clustering is crucial as it retains the information of the originating quark or gluon and forms the basis for studying properties of the Higgs boson, which underlies teh mechanism of mass generation for subatomic particles. For the first time, by mapping collision events into graphs--with particles as nodes and their angular separations as edges--we realize jet clustering using the Quantum Approximate Optimization Algorithm (QAOA), a hybrid quantum-classical algorithm for addressing classical combinatorial optimization problems with available quantum resources. Our results, derived from 30 qubits on quantum computer simulator and 6 qubits on quantum computer hardware, demonstrate that jet clustering performance with QAOA is comparable with or even better than classical algorithms for a small-sized problem. This study highlights the feasibility of quantum computing to revolutionize jet clustering, bringing the practical application of quantum computing in high-energy physics experiments one step closer.

quant-ph↗

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

Measurement of CKM element $|V_{cb}|$ from $W$ boson decays at the future Higgs factories

This study investigates the precision measurement of the CKM matrix element $|V_{cb}|$ through semileptonic $WW$ events at future Higgs factories, $i.e.$, FCC-ee, ILC, C$^3$, and CEPC. We use full detector simulation to generate the $WW \to \ell νcb$ signal events and various backgrounds at $\sqrt{s} = 240$ GeV with unpolarized beams. The relative statistical uncertainties are projected to be 0.91\% for the muon channel and 1.2\% for the electron channel, assuming a baseline integrated luminosity of 5 ab$^{-1}$. The sensitivities at other Higgs factory scenarios are also projected. Possible contributors to systematic uncertainties are discussed, with the most prominent one being the systematics of flavor-tagging and mistagging rates. Combining with $WW$ threshold runs, the relative systematic uncertainty can be further reduced.

hep-ph↗

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↗

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

The $Higgs\to b\bar{b}, c\bar{c}, gg$ measurement at CEPC

Accurately measuring the properties of the Higgs boson is one of the core physics objectives of the Circular Electron Positron Collider (CEPC). As a Higgs factory, the CEPC is expected to operate at a centre-of-mass energy of $240\,GeV$, deliver an integrated luminosity of $5.6\,ab^{-1}$, and produce one million Higgs bosons according to the CEPC Conceptual Design Report (CDR). Combining measurements of the $\ell^+\ell^-H$, $ν\barν H$, and $q\bar{q}H$ channels, we conclude that the signal strength of $H\to b\bar{b}/c\bar{c}/gg$ can be measured with a relative accuracy (statistic uncertainty only) of 0.27\%/4.03\%/1.56\%. Extrapolating to the recently released TDR operating parameters corresponding to the integrated luminosity of $20\,ab^{-1}$, the relative accuracy of $H\to b\bar{b}/c\bar{c}/gg$ signal strength is 0.14\%/2.13\%/0.82\%. We analyze the dependence of the expected accuracies on the critical detector performances: Color Singlet Identification (CSI) for the $q\bar{q}H$ channel and flavor tagging for both $ν\barν H$ and $q\bar{q}H$ channels. We observe that compared to the baseline CEPC detector performance, ideal flavor tagging increases the $H\to b\bar{b}/c\bar{c}/gg$ signal strength accuracy by 2\%/63\%/13\% in the $ν\barν H$ channel and 35\%/122\%/181\% in the $q\bar{q}H$ channel. In addition, better performance of CSI can significantly improve the anticipated accuracy of signal strength. The relevant systematics are also discussed in this paper.

hep-ex↗

arXiv2022

Requirement analysis for dE/dx measurement and PID performance at the CEPC baseline detector

The Circular Electron-Positron Collider (CEPC) can be operated not only as a Higgs factory but also as a Z-boson factory, offering great opportunities for flavor physics studies where Particle Identification (PID) is critical. The baseline detector of the CEPC could record TOF and dE/dx information that can be used to distinguish particles of different species. We quantify the physics requirements and detector performance using physics benchmark analyzes with full simulation. We conclude that at the benchmark TOF performance of $50\,$ps, the dE/dx resolution should be better than 3% for incident particles in the barrel region with a relevant energy larger than $2\, $GeV/c. This performance leads to an efficiency/purity of $K^{\pm}$ identification 97%/96%, $D^0\to π^+K^-$ reconstruction 68.19%/89.05%, and $ϕ\to K^+K^-$ reconstruction 82.26%/77.70%, providing solid support for relevant CEPC flavor physics measurements.

physics.ins-det↗

arXiv2022

Optimization of tracker configuration for the CEPC

We investigate the tracker configuration optimization for the Circular Electron Position Collider (CEPC), a proposed Higgs and $Z$ factory. Fixing the construction cost comparable to that of the baseline detector design and considering the benchmark channels ($Z\rightarrow f\bar{f}$, $WW$ fusion with $H\rightarrow f\bar{f}$, $ZH\rightarrowν\barνf\bar{f}$, and $t\bar{t}\rightarrow b\bar{b}μν_μud$) of various operating modes of the CEPC, we obtain the optimal tracker radius that provides the best average resolution of the track momentum or jet energy. The optimal tracker radii for track momentum resolution range from 1.59\,m to 1.73\,m and for jet energy resolution from 1.82\,m to 1.97\,m, depending on the benchmark channels. Compared to the jets, the tracks prefer a smaller radius and a longer length because the track momentum resolution degrades more significantly than jet energy resolution in the forward region. The benchmark channel for $Z$-pole prefers a smaller radius and longer length compared to other benchmark channels because the final state particles at the $Z$-pole have a more forward distribution. We also analyze the scaling behavior of the optimal tracker configuration at floating construction cost and observe a weak dependence.

physics.ins-det↗