论文标题
使用端到端的深度学习与CMS检测器中的域延续重建腐烂的光子合并光子
Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector
论文作者
论文摘要
引入了一种基于机器学习的新技术，以重建高度洛伦兹增强颗粒的衰变。该技术使用端到端的深度学习策略绕过了通常用于高能物理分析中的基于规则的粒子重建方法。它使用最小处理的检测器数据作为输入，并直接输出感兴趣的粒子特性。为重建CMS检测器中不变质量的颗粒质量的重建证明了新技术。假设标量粒子$ \ MATHCAL {a} $的衰减成两个光子，$ \ mathcal {a} $ $ \ to $ $γγ$，被选为基准衰减。洛伦兹（Lorentz）提升$γ_\ mathrm {l} $ = 60-600，范围从两个光子解决的机制到光子紧密合并为一个对象的机制。引入了使用域延续的训练方法，从而使未解决的光子对不变的质量重建以新颖的方式进行。新技术使用$π^0 $ $ \至$ $γγ$衰减在LHC碰撞数据中。
A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used in high energy physics analyses. It uses minimally processed detector data as input and directly outputs particle properties of interest. The new technique is demonstrated for the reconstruction of the invariant mass of particles decaying in the CMS detector. The decay of a hypothetical scalar particle $\mathcal{A}$ into two photons, $\mathcal{A}$ $\to$ $γγ$, is chosen as a benchmark decay. Lorentz boosts $γ_\mathrm{L}$ = 60-600 are considered, ranging from regimes where both photons are resolved to those where the photons are closely merged as one object. A training method using domain continuation is introduced, enabling the invariant mass reconstruction of unresolved photon pairs in a novel way. The new technique is validated using $π^0$ $\to$ $γγ$ decays in LHC collision data.
