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Simulating high-resolution detector responses is a storage-costly andcomputationally intensive process that has long been challenging in particlephysics. Despite the ability of deep generative models to make this processmore cost-efficient, ultra-high-resolution detector simulation still proves tobe difficult as it contains correlated and fine-grained mutual informationwithin an event. To overcome these limitations, we propose Intra-Event AwareGAN (IEA-GAN), a novel fusion of Self-Supervised Learning and GenerativeAdversarial Networks. IEA-GAN presents a Relational Reasoning Module thatapproximates the concept of an ''event'' in detector simulation, allowing forthe generation of correlated layer-dependent contextualized images forhigh-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. Wedemonstrate IEA-GAN's application in generating sensor-dependent images for thehigh-granularity Pixel Vertex Detector (PXD), with more than 7.5M informationchannels and a non-trivial geometry, at the Belle II Experiment. Applicationsof this work include controllable simulation-based inference and eventgeneration, high-granularity detector simulation such as at the HL-LHC (HighLuminosity LHC), and fine-grained density estimation and sampling. To the bestof our knowledge, IEA-GAN is the first algorithm for faithfulultra-high-resolution detector simulation with event-based reasoning.

Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning