arXiv subjects

Yunxuan Song

Publications and source records attributed to Yunxuan Song.

5 recordsLinked to original sources

arXiv2026

Learning transferable event representations for charmed baryon physics at BESIII

Deep learning has become an essential tool in high-energy physics, where the ability to learn transferable event representations can significantly improve model generalization across related physics processes. In this work, we present a Particle Transformer-based framework for learning such representations for charmed baryon physics in the BESIII experiment. The framework is implemented through large-scale pre-training on Monte Carlo simulation samples and subsequent fine-tuning for downstream analyses. Using the production and decays of the charmed baryon $Λ_c^+$ as a benchmark, we develop pre-trained models for both event classification and momentum-direction regression. The classification model learns discriminative event representations for the dominant physics categories, rejecting 97.0\% of background events at a signal efficiency of 90.0\%. Across 12 benchmark $Λ_c^+$ decay channels, fine-tuning from the pre-trained model achieves performance comparable or better than training from scratch, with particularly clear improvements in low-statistics regimes. For the regression task, the pre-trained model improves the momentum-direction prediction across the same benchmark channels. Further improvement is obtained after fine-tuning in the representative semileptonic decay $Λ_c^+ \to p K^- e^+ ν_e$. This strategy provides a scalable solution for a wide range of physics cases at BESIII and can be extended to other high energy experiments.

physics.data-an↗

arXiv2026

Antineutron reconstruction in electromagnetic calorimeters with mixed-representation learning

A long-standing bottleneck in GeV-scale accelerator experiments lies in reconstructing long-lived neutral hadrons in conventional electromagnetic calorimeters (ECALs), where hadron--nucleus interactions fall outside the detector's native response regime. In this paper, we develop a physics-inspired representation approach for antineutron reconstruction using a large corpus of real collision data. Motivated by two distinct energy deposition patterns from the penetrating high energy antineutrons in ECALs, we propose a Mixed-representation Calorimetric Network (MrCAL) that integrates complementary visual and sequential representation branches within a unified object-detection architecture. This architecture jointly predicts particle identity, momentum direction, and momentum magnitude. Our approach improves the precision of antineutron momentum-direction reconstruction by up to 96% and, for the first time, enables direct measurement of momentum magnitude solely from ECAL readouts, achieving a momentum resolution of approximately 17% at 1 GeV/c. The model maintains robust performance through comprehensive generalization tests spanning a wide variety of physics processes and background environments. This work unlocks a novel measurement capability for legacy ECAL systems at large experimental facilities, broadening their scientific scope via innovative final-state neutral-hadron detection.

hep-ex↗

arXiv2026

Vision Calorimeter for Anti-neutron Reconstruction: A Baseline

In high-energy physics, anti-neutrons ($\bar{n}$) are fundamental particles that frequently appear as final-state particles, and the reconstruction of their kinematic properties provides an important probe for understanding the governing principles. However, this confronts significant challenges instrumentally with the electromagnetic calorimeter (EMC), a typical experimental sensor but recovering the information of incident $\bar{n}$ insufficiently. In this study, we introduce Vision Calorimeter (ViC), a baseline method for anti-neutron reconstruction that leverages deep learning detectors to analyze the implicit relationships between EMC responses and incident $\bar{n}$ characteristics. Our motivation lies in that energy distributions of $\bar{n}$ samples deposited in the EMC cell arrays embody rich contextual information. Converted to 2-D images, such contextual energy distributions can be used to predict the status of $\bar{n}$ ($i.e.$, incident position and momentum) through a deep learning detector along with pseudo bounding boxes and a specified training objective. Experimental results demonstrate that ViC substantially outperforms the conventional reconstruction approach, reducing the prediction error of incident position by 42.81% (from 17.31$^{\circ}$ to 9.90$^{\circ}$). More importantly, this study for the first time realizes the measurement of incident $\bar{n}$ momentum, underscoring the potential of deep learning detectors for particle reconstruction. Code is available at https://github.com/yuhongtian17/ViC.

hep-ex↗

arXiv2026

Vision Calorimeter for High-Energy Particle Detection

In high-energy physics, estimating anti-neutron parameters (position and momentum) using the electromagnetic calorimeter (EMC) is crucial but challenging. To conquer this challenge, we propose Vision Calorimeter (ViC), a framework that migrates visual object detectors to analyze particle images. The motivation lies in introducing a physics-inspired heat-conduction operator (HCO) into the detector's backbone and head to handle the discrete and sparse patterns of these images. Implemented via the Discrete Cosine Transform, HCO extracts frequency-domain features, bridging the distribution gap between natural and particle images. Experiments demonstrate that ViC significantly outperforms conventional methods, reducing the incident position prediction error by 46.16% (from 17.31° to 9.32°) and providing the first baseline result with an incident momentum regression error of 21.48%. This study underscores ViC's great potential as a reliable particle detector for high-energy physics. Code is available at https://github.com/yuhongtian17/ViC.

hep-ex↗

arXiv2022

Boosted tau lepton as a microscope and macroscope

Anomalies from the LHCb lepton flavour universality and Fermilab muon anomalous magnetic momentum, show tantalizing hints of possible new physics from the lepton sectors. Due to its large mass and shorter lifetime than muon, the tau lepton is believed to couple more to possible new physics beyond the standard model. Traditionally, tau leptons are probed through the decay products due to tau's short life time. On the other hand, at a high energy scale, a large fraction of tau leptons could be boosted to a much longer life time and fly a visible distance from several centimetres up to kilometer length scale, yet very informative to new physics beyond the standard model or high energy cosmic rays. In this article, we show unique yet promising tau physics by exploiting long-lived taus as a microscope or macroscope, to measure tau's anomalous magnetic momentum to an unprecedented level of accuracy and detect high energy cosmic neutrinos at the 1 TeV to 1 PeV scale, respectively.

hep-ph↗