Identifying groomed jet splittings in heavy-ion collisions
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Measurements of jet substructure in heavy-ion collisions may provide key insight to the nature of jet quenching in the quark-gluon plasma. Jet grooming techniques from high-energy physics have been applied to heavy-ion collisions in order to isolate theoretically controlled jet observables and explore possible modification to the hard substructure of jets. However, the grooming algorithms used have not been tailored to the unique considerations of heavy-ion collisions, in particular to the experimental challenge of reconstructing jets in the presence of a large underlying event. We report a set of simple studies illustrating the impact of the underlying event on identifying groomed jet splittings in heavy-ion collisions, and on associated groomed jet observables. We illustrate the importance of the selection of grooming algorithm, as certain groomers are more robust to these effects, while others, including those commonly used in heavy-ion collisions, are susceptible to large background effects -- which, when uncontrolled, can mimic a jet quenching signal. These experimental considerations, along with appropriate theoretical motivation, provide input to the choice of grooming algorithms employed in heavy-ion collisions.
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Cited by 1 Pith paper
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Validating a Machine Learning Approach to Identify Quenched Jets in Heavy-Ion Collisions
An LSTM model trained on simulated jet substructure learns to predict true jet energy loss and distinguishes quenching signatures even after realistic detector effects are applied.
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