Calorimeter Shower Superresolution with Conditional Normalizing Flows: Implementation and Statistical Evaluation
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-implemente...
| Authors | Andrea Cosso |
|---|---|
| Primary category | physics.ins-det |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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