Speaker
Description
Jet quenching modifies the energy and substructure of jets in heavy-ion collisions, but the corresponding inverse problem remains largely unexplored at the constituent level: given a medium-modified jet, infer the vacuum jet associated with the same parton shower. We formulate this low-level inverse jet-quenching task using paired HYBRID simulations, in which each quenched shower is obtained by modifying a known vacuum shower and therefore provides direct jet-by-jet supervision. Central to this construction is, to our knowledge, the first constituent-level treatment of HYBRID medium response. Positive and negative wake contributions are handled event-wise through constituent subtraction, conceptually paralleling JEWEL’s 4MomSub approach while retaining a physical constituent list for downstream analysis. We subsequently add and subtract a realistic underlying event according to the Apples-to-Apples prescription, ensuring that residual background contamination enters the learning problem consistently. Our deterministic baseline combines the pretrained PET v2 body of OmniLearned with a cross-attention decoder that predicts variable constituent multiplicity, identified-particle labels, and an on-shell four-vector representation. We compare fine-tuning of pretrained representations with training the same architecture from scratch and evaluate constituent-level reconstruction together with the closure of jet kinematics and substructure observables. The inferred vacuum jet enables jet-by-jet comparisons of kinematics and substructure in settings where a paired vacuum shower is unavailable, including standard JEWEL samples and, subject to domain and uncertainty validation, experimental data. This provides a controlled baseline for extending the inverse map to probabilistic conditional transport.