FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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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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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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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.