标题： 基于多重性的喷注背景减除方法在重离子碰撞中的应用 显示英文标题

标题： Multiplicity Based Background Subtraction for Jets in Heavy Ion Collisions

摘要： 在低喷注动量下，重离子碰撞中的喷注测量可以为夸克胶子等离子体的性质提供约束，但会受到显著且波动的背景影响。 我们基于之前的工作，该工作展示了喷注多重性方法能够将喷注测量扩展到低喷注动量领域 [1, Mengel:2023]。 我们将此方法扩展到广泛的喷注分辨率参数范围。 我们研究了用于解决喷注背景减法问题的不可解释机器学习方法通过网络优化带来的过度复杂性。 最后，我们表明所得到的浅层神经网络能够学习喷注多重性与背景波动之间的基本关系，且复杂度较低，从而强化了可解释方法的实用性。 显示英文摘要

摘要： Jet measurements in heavy ion collisions at low jet momentum can provide constraints on the properties of the quark gluon plasma but are overwhelmed by a significant, fluctuating background. We build upon our previous work which demonstrated the ability of the jet multiplicity method to extend jet measurements into the domain of low jet momentum [1, Mengel:2023]. We extend this method to a wide range of jet resolution parameters. We investigate the over-complexity of non-interpretable machine learning used to tackle the problem of jet background subtraction through network optimization. Finally, we show that the resulting shallow neural network is able to learn the underlying relationship between jet multiplicity and background fluctuations, with a lesser complexity, reinforcing the utility of interpretable methods.