Vision Calorimeter for Anti-neutron Reconstruction: A Baseline

TL;DR

This paper introduces Vision Calorimeter (ViC), a deep learning-based method for reconstructing anti-neutron properties from electromagnetic calorimeter data, significantly improving accuracy and enabling momentum measurement in high-energy physics experiments.

Contribution

The study presents the first deep learning approach for anti-neutron reconstruction, outperforming traditional methods and enabling momentum measurement from calorimeter data.

Findings

Reduced incident position prediction error by 42.81%.

Achieved first measurement of anti-neutron momentum.

Demonstrated deep learning's effectiveness in particle reconstruction.

Abstract

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.