Quadfasel, T. (Corresponding author)Extern* ; Kasieczka, G. (Thesis advisor)Extern* ; Schleper, P. (Thesis advisor)Extern*

2024

229 pp. (2024)2024 = Dissertation, University of Hamburg, 2024  

Abstract: Despite significant efforts to search for new beyond the Standard Model phenomena at the LHC physics program and beyond, no evidence has been found so far. Model-agnostic searches complement current search efforts, allowing for the detection of potential new physics anomalies without targeting a particular signal model.This thesis discusses the development and application of novel data-driven methods for model-agnostic anomaly detection. In particular, a new method based on neural density estimation and weak classification – named Cathode – is developed, achieving state-of-the-art performance on a commonly used benchmark data set. The first application of Cathode and other Machine Learning-based methods on proton-proton collision data taken at the CMS experiment from 2016 to 2018 at a centre-of-mass energy of √𝑠 = 13 TeV is discussed. Specifically, hadronic resonances in the two-jet final state are targeted. A generic search for new physics in the invariant mass spectrum of the two jets revealed no significant excess. In terms of cross-section limits, Cathode achieves the most sensitive results for several of the tested mass hypotheses of an 𝑋 → 𝑌 𝑌 ′ → 4𝑞 signal model, with an optimal improvement over an inclusive search of factor 1.9.Several improvements to the original Cathode algorithm are proposed. Latent Cathode allows for signal extraction in the case of strong correlations between input features and mass by conducting the weak classification in latent space. Using Gradient Boosting classifiers instead of Deep Learning-based methods in weak classification is shown to be considerably more robust against uninformative features, allowing for model-agnostic searches without prior feature selection.

1. PHGS, VH-GS-500 - PIER Helmholtz Graduate School (2015_IFV-VH-GS-500) (2015_IFV-VH-GS-500)