000003396 001__ 3396
000003396 005__ 20230126163730.0
000003396 037__ $$aBELLE2-MTHESIS-2023-003
000003396 041__ $$aeng
000003396 100__ $$aJonas Eppelt
000003396 245__ $$aAnomaly Detection in Searches for Inelastic Dark Matter with a Dark Higgs at Belle II
000003396 260__ $$aKarlsruhe$$bInsitut for Experimental Particle Physics$$c2022
000003396 300__ $$a187
000003396 500__ $$aPresented on 14 11 2022
000003396 502__ $$aMSc$$bKarlsruhe, Karlsruhe Institut for Technology$$c2022
000003396 520__ $$aIn recent years a complementary search paradigm to classical searches has gained traction in the high energy physics community. While classical searches select regions of interest in the data based on a simulation of potential signal models, anomaly detection seeks to identify data regions, that are anomalous with respect to the data. Such searches offer the benefit of independence from concrete models. This thesis explores three autoencoder architectures as a machine-learning-driven tool for model-independent searches at the Belle II experiment. Autoencoders are types of neural networks that compress information in a lower dimensional latent space and reconstruct the information from it. The three architectures explored in this thesis use unregularised latent spaces, latent spaces with a Gaussian prior and with a Dirichlet prior. With the process of encoding and decoding optimized for certain samples, a higher error in the reconstruction is expected for rare samples or samples not represented during the training. This thesis studies the sensitivities of this error for two free parameters of the inelastic Dark Matter model with Dark Higgs. In a comparison between the three architectures unregularized latent spaces provide the highest sensitivities. Studies on the dimensionality of the latent spaces show varying sensitivities for different mass configurations. The studies yield the highest sensitivities for small-mass configurations with an 8-dimensional latent space and for large-mass configurations with a 9-dimensional latent space. Further studies on using the latent space to identify anomalies and efforts to validate the autoencoders on Belle II data are presented.
000003396 700__ $$aProf. Dr. Torben Ferber$$edir.
000003396 700__ $$aProf. Dr. Günter Quast$$edir.
000003396 8560_ $$fjonas.eppelt@student.kit.edu
000003396 8564_ $$uhttps://docs.belle2.org/record/3396/files/BELLE2-MTHESIS-2023-003.pdf
000003396 980__ $$aTHESIS