Speaker
Description
Simulating high-resolution detector responses is a
storage-costly and computationally intensive process that has long
been challenging in particle physics. Despite the ability of deep
generative models to make this process more cost-efficient,
ultra-high-resolution detector simulation still proves to be difficult
as it contains correlated and fine-grained mutual information within
an event. To overcome these limitations, we propose Intra-Event Aware
GAN (IEA-GAN), a novel fusion of Self-Supervised Learning and
Generative Adversarial Networks. IEA-GAN presents a Relational
Reasoning Module that approximates the concept of an ''event'' in
detector simulation, allowing for the generation of correlated
layer-dependent contextualized images for high-resolution detector
responses with a proper relational inductive bias. IEA-GAN also
introduces a new intra-event aware loss and a Uniformity loss,
resulting in significant enhancements to image fidelity and diversity.
We demonstrate IEA-GAN's application in generating sensor-dependent
images for the high-granularity Pixel Vertex Detector (PXD), with more
than 7.5M information channels and a non-trivial geometry, at the
Belle II Experiment. Applications of this work include controllable
simulation-based inference and event generation, high-granularity
detector simulation such as at the HL-LHC (High Luminosity LHC), and
fine-grained density estimation and sampling. To the best of our
knowledge, IEA-GAN is the first algorithm for faithful
ultra-high-resolution detector simulation with event-based reasoning
