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Geometry-aware Autoregressive Models for Calorimeter Shower Simulations
Junze Liu · Aishik Ghosh · Dylan Smith · Pierre Baldi · Daniel Whiteson

Calorimeter shower simulations are often the bottleneck in simulation time for particle physics detectors. A lot of effort is currently spent on optimising generative architectures for specific detector geometries, which generalise poorly. We develop a geometry-aware autoregressive model on a range of calorimeter geometries such that the model learns to adapt its energy deposition depending on the size and position of the cells. This is a key proof-of-concept step towards building a model that can generalize to new unseen calorimeter geometries with little to no additional training. Such a model can replace the hundreds of generative models used for calorimeter simulation a Large Hadron Collider experiment. For the study of future detectors, such a model will dramatically reduce the large upfront investment usually needed in generating simulations.

Author Information

Junze Liu (University of California, Irvine)
Junze Liu

I am a fourth-year Ph.D. student in computer science at University of California, Irvine. My advisor is Prof. Pierre Baldi. My research interests are in computer vision, deep learning, and its applications in physical science. In particular, my recent projects include: - generative models for calorimeter simulation; - reconstruction of neutrino features using deep learning; - machine learning in high energy physics - computational methods for bioimaging and biomedical informatics

Aishik Ghosh (UC Irvine)
Dylan Smith (University of California, Irvine)
Pierre Baldi (UC Irvine)
Daniel Whiteson (UC Irvine)

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