论文标题
使用机器学习技术，在5.02 TEV的PB-PB碰撞中的包容性喷射测量
Inclusive Jet Measurements in Pb-Pb Collisions at 5.02 TeV with ALICE using Machine Learning Techniques
论文作者
论文摘要
这些程序报告在$ \ sqrt {s _ {\ rm nn}} = 5.02 $ tev记录了Alice tecter记录的PB-PB和PP碰撞中的含有全套射流和核修饰因子的测量报告（包含带电和中性成分）和PP碰撞。这些测量结果采用基于机器学习的背景校正，从而减少了残余波动。此方法允许测量比以前在爱丽丝中降低横向动量和更大的喷射分辨率参数（R）。在这种方法中，使用机器学习技术用于使用喷气参数（例如有关喷气机构组成部分的信息）逐射流式横向动量。还将讨论研究通过从成分中学习引入的潜在碎片偏差的影响的研究。考虑到这些研究，将结果与理论预测进行了比较。
These proceedings report on measurements of the jet spectrum and nuclear modification factor for inclusive full jets (containing both charged and neutral constituents) in Pb-Pb and pp collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV recorded with the ALICE detector. These measurements use a machine learning based background correction, which reduces residual fluctuations. This method allows for measurements to lower transverse momenta and larger jet resolution parameter (R) than previously possible in ALICE. In this method, machine learning techniques are used to correct the jet transverse momentum on a jet-by-jet basis using jet parameters such as information about the constituents of the jet. Studies that investigate the effect of the potential fragmentation bias introduced by learning from constituents will also be discussed. With these studies in mind, the results are compared to theoretical predictions.
