论文标题
一种深入学习方法，用于使用Dampe任务进行宇宙射线的轨迹重建
A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission
论文作者
论文摘要
提出了通过DAMPE实验进行粒子轨迹重建的深度学习方法。开发的算法构成了第一个用于空间颗粒任务的完全机器学习的轨道重建管道。证明了对标准手工工程算法的显着性能改进。由于具有更好的精度，开发的算法有助于在整个能量范围内使用跟踪器的粒子绝对电荷识别，从而在极端能量的PEV量表上打开了宇宙射线质子和氦气光谱的测量，几乎无法通过标准轨道重建方法来实现。此外，开发的方法表明，在粒子方向重建的前所未有的准确性，在高沉积能量下量热计，用于电磁淋浴的辐射式淋浴，高于几百GEV，高于几十GEV。
A deep learning method for the particle trajectory reconstruction with the DAMPE experiment is presented. The developed algorithms constitute the first fully machine-learned track reconstruction pipeline for space astroparticle missions. Significant performance improvements over the standard hand-engineered algorithms are demonstrated. Thanks to the better accuracy, the developed algorithms facilitate the identification of the particle absolute charge with the tracker in the entire energy range, opening a door to the measurements of cosmic-ray proton and helium spectra at extreme energies, towards the PeV scale, hardly achievable with the standard track reconstruction methods. In addition, the developed approach demonstrates an unprecedented accuracy in the particle direction reconstruction with the calorimeter at high deposited energies, above several hundred GeV for hadronic showers and above a few tens of GeV for electromagnetic showers.
