Calorimeter Shower Superresolution with Conditional Normalizing Flows: Implementation and Statistical Evaluation

TL;DR

This paper explores the application of a generative normalizing flow model to enhance calorimeter data resolution in high energy physics, aiming to reduce computational costs while maintaining accuracy.

Contribution

It re-implements a state-of-the-art generative model for calorimeter superresolution and evaluates its effectiveness using a rigorous statistical framework.

Findings

Model successfully reproduces reference distributions in calorimeter data.

The approach offers a promising data-driven method for high-resolution calorimeter reconstruction.

Statistical evaluation confirms the model's ability to capture detailed energy deposit patterns.

Abstract

In High Energy Physics, detailed calorimeter simulations and reconstructions are essential for accurate energy measurements and particle identification, but their high granularity makes them computationally expensive. Developing data-driven techniques capable of recovering fine-grained information from coarser readouts, a task known as calorimeter superresolution, offers a promising way to reduce both computational and hardware costs while preserving detector performance. This thesis investigates whether a generative model originally designed for fast simulation can be effectively applied to calorimeter superresolution. Specifically, the model proposed in arXiv:2308.11700 is re-implemented independently and trained on the CaloChallenge 2022 dataset based on the Geant4 Par04 calorimeter geometry. Finally, the model's performance is assessed through a rigorous statistical evaluation framework, following the methodology introduced in arXiv:2409.16336, to quantitatively test its ability to reproduce the reference distributions.