Multiplicity Based Background Subtraction for Jets in Heavy Ion Collisions
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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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Cited by 2 Pith papers
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Jet Quenching Identification via Supervised Learning in Simulated Heavy-Ion Collisions
Sequential machine learning on jet declustering history trees outperforms static models at identifying jet quenching in heavy-ion collision simulations.
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Jet Quenching Identification via Supervised Learning in Simulated Heavy-Ion Collisions
Sequential ML models classify quenched jets with >93% accuracy and show sensitivity to medium implementation details that traditional observables miss.
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