Multiplicity Based Background Subtraction for Jets in Heavy Ion Collisions
arxiv(2024)
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.
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