Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation
Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains. We develop a generative surrogate, focusing on Normalizing Flows (NFs). Through transfer learning, we pre-train on the full imbalanced dataset and fine-tune specialized models for different particle types ($γ$, $n$, $Λ$, $K_S^0$, $Σ^+$) using two gradual-unfreezing schemes. As standard ZDC metrics like Wasserstein distance overlook conditional structure, we introduce refined metrics: conditional weighted MAE, dispersion ratio, and Jaccard co-activation error, that better capture physics-relevant input-output dependencies and response ...
| Authors | Emilia Majerz, Jacek Otwinowski, Witold Dzwinel et al. |
|---|---|
| Primary category | physics.ins-det |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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