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arXiv 2206.04532astro-ph.IMastro-ph.HEphysics.ins-det
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一种用于DAMPE任务宇宙线轨迹重建的深度学习方法

A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission

Andrii Tykhonov, Andrii Kotenko, Paul Coppin, Maksym Deliyergiyev, David Droz, Jennifer Maria Frieden, Chiara Perrina, Enzo Putti-Garcia, Arshia Ruina, Mikhail Stolpovskiy, Xin Wu

AI总结:

本研究提出首个用于空间天体粒子任务的全机器学习径迹重建深度学习方法,在DAMPE实验中相比传统算法性能显著提升,可实现宽能区粒子电荷识别与极端能区宇宙线能谱测量,还大幅提高了量能器的粒子方向重建精度。

摘要:

本文提出了一种用于DAMPE(暗物质粒子探测卫星)实验粒子轨迹重建的深度学习方法。所开发的算法构成了空间天体粒子任务中首个完全基于机器学习的径迹重建流程。实验证明,该方法相比标准的人工设计算法实现了显著的性能提升。得益于更高的精度,所开发的算法可借助径迹探测器在整个能区内实现粒子绝对电荷的识别,为测量极端能量(接近PeV量级)的宇宙线质子和氦核能谱打开了大门,而这一目标采用标准径迹重建方法几乎无法实现。此外,该方法在利用量能器进行粒子方向重建方面展现出前所未有的精度:对于强子簇射,在沉积能量高于数百GeV时精度优异;对于电磁簇射,在沉积能量高于几十GeV时精度优异。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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