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.
Particle-level pileup subtraction for jets and jet shapes
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abstract
We present an extension to the jet area-based pileup subtraction for both jet kinematics and jet shapes. A particle-level approach is explored whereby the jet constituents are corrected or removed using an extension of the methods currently being employed by the LHC experiments. Several jet shapes and nominal jet radii are used to assess the performance in simulated events with pileup levels equivalent to approximately 30 and 100 interactions per bunch crossing, which are characteristic of both the LHC Run I and Run II conditions. An improved performance in removing the pileup contributions is found when using the new subtraction method. The performance of the new procedure is also compared to other existing methods.
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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.