论文标题
基于机器学习的喷气机和事件分类在电子离子对撞机上，并应用于强子结构和旋转物理
Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics
论文作者
论文摘要
我们在未来的电子离子对撞机（EIC）上探索基于机器学习的喷气和事件识别。我们研究基于机器学习的分类器在相对较低的EIC能量上的有效性，重点是（i）识别喷气机的风味，以及（ii）确定事件的基本硬过程。 We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in the cold nuclear matter environment in electron-nucleus collisions.我们建立了第一个基准测试，并将EIC的风味标记的估计性能与大型强子对撞机上的估计性能进行了对比。我们进行与检测器设计方面有关的研究，包括粒子识别，电荷信息和最小横向动量功能。此外，我们研究了使用完整事件信息的影响，而不是仅使用与已确定的喷气机相关的信息。这些方法可以在适当准确的蒙特卡洛事件发生器上部署，也可以直接在几种应用程序上进行实验数据。我们为最终将这些基于机器学习的方法与量子染色体动力学中的第一原理计算联系起来提供了前景。
We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the flavor of the jet and (ii) identifying the underlying hard process of the event. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in the cold nuclear matter environment in electron-nucleus collisions. We establish first benchmarks and contrast the estimated performance of flavor tagging at the EIC with that at the Large Hadron Collider. We perform studies relevant to aspects of detector design including particle identification, charge information, and minimum transverse momentum capabilities. Additionally, we study the impact of using full event information instead of using only information associated with the identified jet. These methods can be deployed either on suitably accurate Monte Carlo event generators, or, for several applications, directly on experimental data. We provide an outlook for ultimately connecting these machine learning-based methods with first principles calculations in quantum chromodynamics.
