h1

% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@PHDTHESIS{Scham:1004474,
      author       = {Scham, Moritz Alfons Wilhelm},
      othercontributors = {Borras, Kerstin and Krämer, Michael and Kasieczka, Gregor},
      title        = {{D}evelopment of a tree-based model for the fast generation
                      of large point clouds representing particle showers in
                      calorimeters},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2025-01456},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2025},
      abstract     = {In High-Energy Physics, detailed and time-consuming
                      simulations are needed to describe particle showers in
                      calorimeters. These particle showers are recorded as energy
                      deposits (hits) in the cells of the detector. To mitigate
                      the computational demands of such simulations, surrogate
                      models are widely studied. In this thesis, Generative
                      Adversarial Networks (GANs) are investigated as a fast and
                      flexible approach. If the cells of the detector could be
                      represented by a regular grid, a GAN model would usually use
                      (De-)Convolution layers to up/down-scale the number of
                      voxels. However, due to the often irregular geometry in
                      modern high-granular calorimeters and the small fraction of
                      cells with a hit in such detectors, a grid representation is
                      often not feasible. By representing the shower as a point
                      cloud (PC), i. e. , a set of real vectors, these issues can
                      be addressed. In PCs, the complex dependencies between the
                      points must be correctly modeled. Particle showers are
                      inherently tree-like processes, as each particle is produced
                      by the decay or the detector interaction of a particle of
                      the previous generation. With this inductive bias, a GAN has
                      been developed, that generates such PCs in a tree-based
                      manner. For this model, numerous new components for Graph
                      Neural Networks (GNNs) have been developed that allow
                      up/down-scaling of PCs. This model is applied to two popular
                      benchmark datasets, which both can be represented as PCs:
                      JetNet, a dataset containing jet constituents, and
                      CaloChallenge, a dataset containing particle showers in
                      calorimeters. The novel model achieves a good fidelity on
                      both datasets.},
      cin          = {131910 / 130000},
      ddc          = {530},
      cid          = {$I:(DE-82)131910_20160614$ / $I:(DE-82)130000_20140620$},
      pnm          = {ZT-I-PF-5-3 - Deep Generative models for fast and precise
                      physics Simulation (DeGeSim) $(2020_ZT-I-PF-5-3)$ / Impuls-
                      und Vernetzungsfonds},
      pid          = {$G:(DE-HGF)2020_ZT-I-PF-5-3$ / G:(DE-HGF)IVF-20140101},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2025-01456},
      url          = {https://publications.rwth-aachen.de/record/1004474},
}