基于混合表示学习的电磁量能器中的反中子重建
Antineutron reconstruction in electromagnetic calorimeters with mixed-representation learning
AI总结：
针对 GeV 级加速器实验中传统电磁量能器重建长寿命中性强子的瓶颈，提出基于混合表示学习的反中子重建方法，开发混合表示量热网络，提高反中子动量方向重建精度，实现动量大小直接测量，拓宽了 ECAL 系统科学范围。
AI中文摘要：
GeV 级加速器实验中的一个长期瓶颈在于在传统电磁量能器（ECAL）中重建长寿命中性强子，强子与原子核的相互作用超出了探测器的固有响应范围。本文利用大量真实碰撞数据，开发了一种受物理启发的反中子重建表示方法。基于 ECAL 中穿透高能反中子的两种不同能量沉积模式，提出了混合表示量热网络（MrCAL），它在统一的目标检测架构中集成了互补的视觉和序列表示分支，联合预测粒子身份、动量方向和动量大小。该方法将反中子动量方向重建精度提高了 96%，首次仅从 ECAL 读数直接测量动量大小，在 1 GeV/c 时实现了约 17%的动量分辨率。通过涵盖各种物理过程和背景环境的综合泛化测试，该模型保持了稳健性能。这项工作为大型实验设施中的传统 ECAL 系统开启了一种新的测量能力，通过创新的末态中性强子检测拓宽了其科学范围。
英文摘要：
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.
