arXiv subjects

Beijiang Liu

Publications and source records attributed to Beijiang Liu.

11 recordsLinked to original sources

arXiv2026

A High Performance Partial Wave Analysis Framework for Hadron Spectroscopy

Partial wave analysis (PWA) is a key method in hadron physics for extracting the properties of hadronic resonances. As experimental statistics grow, conventional implementations face severe difficulties in both memory usage and speed. We present \texttt{CTPWA}, a high performance PWA framework based on covariant tensor formalism. This framework adopts extensive precomputation and caching mechanisms, as well as fully GPU-based likelihood computation and minimization. With these optimization, the fitting speed is accelerated by two orders of magnitude relative to autograd-based GPU PWA programs, rendering high-statistics partial-wave analysis feasible at high-precision experiments like BESIII.

hep-ex↗

arXiv2026

HepScript: A Dual-Use DSL for Human-AI Collaborative Data Analysis Workflows in High-Energy Physics

The escalating data scale in High-Energy Physics (HEP) fuels a growing aspiration for higher analytical efficiency. While Large Language Models (LLMs) offer a path toward automation via agentic AI, they struggle with complex scientific workflows that require deep domain knowledge and are tightly coupled to experiment-specific codebases. To address this, we introduce a methodology centered on HepScript, a dual-use Domain-Specific Language (DSL) for HEP data analysis workflows. HepScript serves as a shared formal interface, abstracting HEP analysis logic into a constrained syntax that is both intuitive for human experts and reliably generable by AI agents. First developed for the Beijing Spectrometer III (BESIII) experiment, HepScript hides the complexity of the underlying software stack, translating high-level analysis intent into low-level, production-ready code. In our case studies, this abstraction reduces the required human-written code by 93\%. Crucially, HepScript's constrained grammar defines a tractable action space, enabling AI agents to autonomously generate executable specifications for core analysis stages directly from published literature with a 95\% success rate. Our work demonstrates a scalable pathway toward human-AI collaborative systems, where a formally specified DSL acts as an unambiguous translation layer between human expertise, AI automation, and production environment, rendering previously intractable automation problems solvable.

hep-ex↗

arXiv2026

Dr.Sai: An agentic AI for real-world physics analysis at BESIII

High Energy Physics (HEP) experiments like BESIII produce petabyte-scale data. Extracting physics results requires complex workflows (simulation, reconstruction, statistical analysis, etc.) that traditionally take experts months or years. Current manual methods are labor-intensive, prone to bias, and limit large-scale systematic scans. As data grows, this paradigm slows discovery. Large Language Models (LLMs) offer a solution. Their natural language understanding and code generation capabilities allow them to interpret scientific tasks and integrate with HEP tools (e.g., ROOT, BOSS) to act as an "AI partner" for autonomous analysis. We present Dr.Sai, an LLM-powered multi-agent system that translates natural language into rigorous physics workflows. As validation, Dr.Sai performed large-scale re-measurements of ten J/psi decay branching fractions - without manual coding. It successfully navigated the real BESIII computing environment and produced results matching established benchmarks. The article details Dr.Sai's architecture, the validation results, and performance evaluation. This work provides a blueprint for autonomous discovery, with relevance to other data-intensive fields like astronomy and genomics.

hep-ex↗

arXiv2026

AI Agents, Language, Deep Learning and the Next Revolution in Science

Modern science is reaching a critical inflection point. Instruments across disciplines, from particle physics and astronomy to genomics and climate modeling, now produce data of such scale, diversity, and interdependence that traditional analytical methods can no longer keep pace. This growing imbalance between data generation and data understanding signals the need for a new scientific paradigm. We propose that intelligent, human-supervised AI agents operating over deep-learning algorithms, represent the next evolution of the scientific method. Built upon large language models and multimodal learning, these agents can interpret scientific intent, design and execute analytical workflows, and ensure traceability through domain-specific languages that preserve human oversight and accountability. Particle physics, a historic incubator of computational innovation, offers the ideal testbed for this transition. At the Institute of High Energy Physics of the Chinese Academy of Sciences, the Dr. Sai system embodies this vision, a multi-agent reasoning framework deployed within collider research at the CEPC. This emerging approach does not replace human scientists but extends their cognitive reach, enabling discovery to scale with complexity and redefining how knowledge itself is produced in the age of intelligent machines. The significance of this paradigm transcends particle physics, offering a blueprint for all data-driven sciences facing the same complexity ceiling.

hep-ex↗

arXiv2025

Machine-Learning-Based Method for Goodness-of-Fit Test in Amplitude Analysis

\textbf{Purpose:} Amplitude analysis is a pivotal tool in hadron spectroscopy, fundamentally involving a series of likelihood fits to multi-dimensional experimental distributions. While robust goodness-of-fit tests exist for low-dimensional scenarios, evaluating goodness-of-fit in amplitude analysis remains challenging. \textbf{Methods:} We propose a machine-learning approach using anomaly detection for goodness-of-fit assessment in amplitude analysis. Our method employs a classifier to identify discrepancies between data and fit results in multi-dimensional phase space. \textbf{Results and Conclusion:} Using Monte Carlo simulations of $J/\psi\to\gamma \pi^+\pi^-\pi^0\pi^0$ decays, we demonstrate that this method detects contributions from an additional resonance with a signal strength of 1\%. The detection power is sufficient for practical amplitude analyses, where contributions with fit fractions larger than 1\% are typically included in the nominal fit. This approach shows promise for amplitude analyses of multi-body processes.

physics.data-an↗

arXiv2019

Light meson spectroscopy at $e^+e^-$ machines

The study of light hadrons is central to the understanding of confinement--a unique property of QCD. The quark model describs mesons as bound states of quarks and antiquarks. LQCD and QCD-motivated models for hadrons, however, predict a richer spectrum of mesons that takes into account not only the quark degrees of freedom but also the gluonic degrees of freedom. A selection of recent progress in the light-quark sector with unprecedented high-statistics data sets from $e^+e^-$ experiments are reviewed.

hep-ex↗

arXiv2018

Application of machine learning techniques at BESIII experiment

BESIII is a currently running tau-charm factory with the largest samples of on threshold charm meson pairs, directly produced charmonia and some other unique datasets at BEPCII collider. Machine learning techniques have been employed to improve the performance of BESIII software. The studies for reweighing MC, particle identification and cluster reconstruction for the CGEM (Cylindrical Gas Electron Multiplier) inner tracker are presented.

physics.ins-det↗

arXiv2015

Recent Results on Hadron Spectroscopy from BESIII

Hadron spectroscopy is one of the most important physics goals of BESIII. BESIII brings great opportunities to study the XYZ states of charmonium by directly producing the Y states up to 4.6 GeV. High statistics of charmonium decays collected at BESIII provide an excellent place for hunting gluonic excitations and studying the excited baryons. Recent results of light hadron spectroscopy and charmonium spectroscopy from BESIII will be reported.

hep-ex↗

arXiv2014

Partial Wave Analysis at BESIII

The BESIII experiment in Beijing takes data in $τ$-charm domain since 2009. For the moment the world largest samples of $J/ψ$, $ψ(3686)$, $ψ(3770)$ and $ψ(4040)$ data have been collected. Hadron spectroscopy is a unique way to access QCD, which is one of the most important physics goals of BESIII. Experimental search of new forms of hadrons and subsequent investigation of their properties would provide validation of and valuable input to the quantitative understanding of QCD. The key to success lies in high levels of precision during the measurement and high statistics in the recorded data set complemented with sophisticated analysis methods. Partial wave analysis (PWA) is a powerful tool to study the hadron spectroscopy, that allows one to extract the resonance's spin-parity, mass, width and decay properties with high sensitivity and accuracy. In this poster, we present the working PWA framework of BESIII -- GPUPWA and the recent results of PWA of $J/ψ\toγηη$. GPUPWA is a PWA framework for high statistics partial wave analyses harnessing the GPU parallel computing.

hep-ex↗

arXiv2014

Background subtraction using probabilistic event weights

Background treatment is crucial to extract physics from precision experiments. In this paper, we introduce a novel method to assign each event a signal probability. This could then be used to weight the event's contribution to the likelihood during fitting. To illustrate the effect of this method, we test it with MC samples. The consistence between the constructed background and the background from MC truth shows that the background subtraction method with probabilistic event weights is feasible in partial wave analysis at BES III.

hep-ex↗

arXiv2010

Study of a0(980)-f0(980) mixing at BES III and study of charged kappa at BES II

Recent BES results on light scalars are reported in this talk, including the observation of a charged kappa decaying to K pi0 with 5.8 * 10^7 J/psi data at BES II and the direct measurements of a0(980)-f0(980) mixing in the processes J/psi --> phi f0(980) --> phi a0(980) --> phi eta pi0 and chi_c1 --> pi0 a0(980) --> pi0 f0(980) --> pi0 pi+ pi- with 2.26 * 10^8 J/psi data and 1.06 * 10^8 psi' data at BES III.

hep-ex↗