Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.25753

Search for new physics with charm data at BESIII

Xinyu Chai
View original source ↗Read source document ↗Export metadata ↓

Abstract

Up to now, the Standard Model (SM) remains confronted with several outstanding issues, including the matter-antimatter asymmetry in the universe, as well as the masses, oscillations, and hierarchical structure of neutrinos. These challenges are closely tied to the naturalness and stability of the SM, and have motivated numerous proposals for new physics beyond the SM. The BESIII experiment has accumulated the world's largest data samples at the $J/ψ$ and $ψ(3686)$ resonances, providing an ideal environment for probing signatures of physics beyond the SM. This proceeding presents a summary of recent results published by the BESIII Collaboration on the searches for lepton/baryon number violation (LNV/BNV), charged lepton flavor violation (CLFV), and charmonium weak decays.

Explore related subjects

hep-ex

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xinyu Chai. 2026-10-19. Search for new physics with charm data at BESIII. https://arxiv.org/abs/2610.25753

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Machine Can Automatically Discover Parametric Functions to Model HEP Data

In HEP data analyses, finding an adequate function to model binned data has largely relied on a manual process: guess a functional form by intuition, fit, examine, then repeat until successful. We show that this iterative process can be automated by a machine using symbolic regression, which performs a data-driven search over function space without requiring prior knowledge of what an adequate function should look like. We present the SymbolFit package, which pairs symbolic regression with uncertainty modeling to target HEP analysis use cases, and demonstrate it on the CMS and ATLAS Run 2 dijet spectra: 560 independent seeded runs across seven simple fit configurations generated over 1000 functions fitting the spectra with $χ^2/\text{NDF}\approx 1$, and 111 of the runs rediscovered the very dijet and UA2 functions used in published dijet searches.

hep-ex↗
arXiv2026

Exploring ESS$ν$SB Near Water Cherenkov Detector Designs Through Graph Neural Network Flavour Identification

The ESS$ν$SB experiment aims to measure CP violation in the leptonic sector with high precision, necessitating robust reconstruction of neutrino events in the water Cherenkov (WC) detectors. In this work, we investigate the flavour identification potential of the proposed near WC detector using graph neural network (GNN)-based classification, with a focus on variations of key detector design parameters. In particular, we study whether a smaller and/or less instrumented detector can achieve the required classification performance. Using detailed Monte Carlo simulations of charged-current (CC) neutrino interactions, we train GNN classifiers to distinguish electron and muon neutrino CC events. We find that GNN-based classification remains accurate even for detector configurations with volumes up to a factor of eight smaller than the nominal design, with only moderate degradation in classification efficiency at fixed background rejection. The resulting loss in efficiency can largely be compensated by increased exposure time. Furthermore, we demonstrate that reduced photomultiplier tube (PMT) coverage in the nominal detector has a limited impact on classification performance, provided that coverage is maintained in regions of highest signal yield, in particular near the forward end-cap.

hep-ex↗
arXiv2026

Low-Latency Beam-Background Suppression for the BESIII Trigger System Using Machine Learning Trained on Experimental Data

Beam-induced backgrounds contribute to the trigger and data-acquisition rate at high-luminosity electron-positron colliders. Their dependence on beam and machine conditions motivates data-driven studies alongside simulation. A data-driven machine-learning method for trigger-level beam-background suppression is developed using experimental data recorded by BESIII. Compact neural-network classifiers are trained and evaluated for two trigger configurations: one based only on tracking and time-of-flight information, and one based only on electromagnetic-calorimeter information. Each classifier uses 48 binary Level-1 trigger conditions of the BESIII detector. The models distinguish hadron events from beam-background events and are evaluated against the established trigger selection at matched hadron-event efficiency. Relative to the established trigger selection, the beam-background false-positive rate is reduced by 20-60% across the studied configurations and center-of-mass energies, with stable suppression performance as tested using random-trigger data. Hardware-oriented synthesis indicates that the compact network is suitable for low-latency FPGA implementation. The proposed method can reduce the fraction of beam-background events in the trigger stream in future BESIII trigger upgrades. It also provides a reference for trigger optimization at future high-luminosity electron-positron colliders.

hep-ex↗