000004197 001__ 4197
000004197 005__ 20240326132016.0
000004197 037__ $$aBELLE2-PTHESIS-2024-006
000004197 041__ $$aeng
000004197 100__ $$aBaran Hashemi
000004197 245__ $$aDeep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation
000004197 260__ $$aMunich$$bLMU$$c2023
000004197 300__ $$a234
000004197 500__ $$aPresented on 22 11 2023
000004197 502__ $$aPhD$$bMunich, LMU$$c2023
000004197 520__ $$aSimulating ultra-high-granularity detector responses in Particle Physics represents a critical yet computationally demanding task. This thesis aims to overcome this challenge for the Pixel Vertex Detector (PXD) at the Belle II experiment, which features over 7.5M pixel channels-the highest spatial resolution detector simulation dataset ever analysed with generative models. This thesis starts off by a comprehensive and taxonomic review on generative models for simulating detector signatures. Then, it presents the Intra-Event Aware Generative Adversarial Network (IEA-GAN), a new geometry-aware generative model that introduces a relational attentive reasoning and Self-Supervised Learning to approximate an "event" in the detector. This study underscores the importance of intra-event correlation for downstream physics analyses. Building upon this, the work drifts towards a more generic approach and presents YonedaVAE, a Category Theory-inspired generative model that tackles the open problem of Out-of-Distribution (OOD) simulation. YonedaVAE introduces a learnable Yoneda embedding to capture the entirety of an event based on its sensor relationships, formulating a Category theoretical language for intra-event relational reasoning. This is complemented by introducing a Self-Supervised learnable prior for VAEs and an Adaptive Top-q sampling mechanism, enabling the model to sample point clouds with variable intra-category cardinality in a zero-shot manner. Variable Intra-event cardinality has not been approached before and is vital for simulating irregular detector geometries. Trained on an early experiment data, YonedaVAE can reach a reasonable OOD simulation precision of a later experiment with almost double luminosity. This study introduces, for the first time, the results of using deep generative models for ultra-high granularity detector simulation in Particle Physics.
000004197 700__ $$aThomas Kuhr$$edir.
000004197 8560_ $$fgh.hashemi@physik.uni-muenchen.de
000004197 8564_ $$uhttps://docs.belle2.org/record/4197/files/BELLE2-PTHESIS-2024-006.pdf
000004197 980__ $$aTHESIS