Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation

2026-08-13

AI Summary

This work addresses the high computational cost and low efficiency of traditional Monte Carlo methods in simulating neutron detector responses in the ALICE Zero Degree Calorimeter. To overcome these limitations, the authors propose a generative surrogate model based on normalizing flows, enhanced with transfer learning and conditional fine-tuning. They introduce physics-informed evaluation metrics—including conditional weighted MAE, dispersion ratio, and Jaccard co-activation error—and devise a progressive unfreezing fine-tuning strategy to accurately capture the input–output physical dependencies. Experimental results demonstrate that the proposed model achieves a Wasserstein distance of 1.61 ± 0.02, significantly outperforming baseline approaches while simultaneously improving both fidelity and inference speed.

Abstract

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 variability. Our ensemble of fine-tuned models achieves a Wasserstein distance of $1.61 \pm 0.02$, outperforming baselines across all metrics. This work provides a generalizable NF-based framework for LHC detector simulation, combining NFs, conditional fine-tuning, and physics-motivated evaluation.

Problem

• ALICE Zero Degree Calorimeter
• fast simulation
• computational cost
• Monte Carlo simulation
• detector response

Innovation

• Normalizing Flows
• transfer learning
• conditional fine-tuning
• physics-motivated metrics
• fast detector simulation