CERN Accelerating science

002878408 001__ 2878408
002878408 005__ 20240109015719.0
002878408 0248_ $$aoai:cds.cern.ch:2878408$$pcerncds:FULLTEXT$$pcerncds:THESES$$pcerncds:CERN:FULLTEXT$$pINIS$$pcerncds:CERN
002878408 037__ $$aCERN-THESIS-2023-222
002878408 035__ $$9Inspire$$a2722970
002878408 041__ $$aeng
002878408 088__ $$a10.18154/RWTH-2023-09302
002878408 088__ $$ahttps://publications.rwth-aachen.de/record/969743/files/969743.pdf
002878408 100__ $$0AUTHOR|(CDS)2706657$$0AUTHOR|(SzGeCERN)845381$$aRehm, Florian$$mflorian.matthias.rehm@cern.ch$$uRheinisch Westfaelische Tech. Hoch. (DE)
002878408 245__ $$aDeep Learning and Quantum Generative Models for High Energy Physics Calorimeter Simulations
002878408 300__ $$a306 p
002878408 500__ $$aPresented 25 Aug 2023
002878408 502__ $$aPhD$$bRWTH Aachen University$$c2023-04-19
002878408 520__ $$aThe aim of this thesis is to explore Deep Learning and Quantum Computing approaches to reduce the challenging computing resource requirements of future High Energy Physics detector simulations while maintaining the necessary accuracy. In the forthcoming High Luminosity phase at the Large Hadron Collider at CERN, the number of collisions is increasing drastically. The demand for detector simulations to analyze the collision products is growing by the same scale. This causes an acute shortage in computing resources in both hardware and efficient software. Therefore, resource-optimizing simulation alternatives are needed, and novel technologies to be explored.  In this thesis, Deep Learning Generative Adversarial Networks (GANs) are investigated as a first option to be deployed for a future representative high-granularity calorimeter. Novel neural network architectures are developed and reduced precision computing techniques applied to optimize the GAN models. The results are realistic and accurate models that are about five orders of magnitude faster than the current Monte Carlo simulation.  The second study of this thesis addresses the novel and rapidly evolving field of Quantum Computing for a simplified calorimeter. The initial attempt employs a model available in the IBM software suite that has been successfully adapted and modified. It runs in a hybrid GAN mode employing a quantum generator and a conventional discriminator. However, this model learned only average distributions and suffers from fluctuations. Therefore, a new fully quantum model was designed to produce individual images. The in this thesis developed Quantum Angle Generator achieves extremely precise results. Due to its noise robustness and lightweight training, it is successfully executed on current noisy quantum devices with a limited number of qubits. The superior expressive power of quantum circuits compared to classical neural networks, combined with a potential exponential scaling, makes Quantum Computing a promising future computing approach. This thesis has pioneered and conducted studies which confirm that this potential has a realistic chance to materialize.
002878408 536__ $$aCERN Doctoral Student Program
002878408 595__ $$aCERN EDS
002878408 65017 $$2SzGeCERN$$aDetectors and Experimental Techniques
002878408 65017 $$2SzGeCERN$$aQuantum Technology
002878408 693__ $$aNot applicable$$eNot applicable
002878408 690C_ $$aCERN
002878408 690C_ $$aTHESIS
002878408 701__ $$aBorras, Kerstin$$edir.$$uRWTH Aachen University, DESY
002878408 701__ $$aPooth, Oliver$$edir.$$uRWTH Aachen University
002878408 710__ $$5IT
002878408 859__ $$fflorian.matthias.rehm@cern.ch
002878408 8564_ $$82489141$$s46761899$$uhttp://cds.cern.ch/record/2878408/files/CERN-THESIS-2023-222.pdf
002878408 916__ $$sn$$w202344$$ya2023
002878408 963__ $$aPUBLIC
002878408 960__ $$a14
002878408 980__ $$aTHESIS