Simulation of the ATLAS Electromagnetic Calorimeter using Generative Adversarial Networks and Likelihood-Free Inference of the Offshell Higgs Boson Couplings at the LHC

Since the discovery of the Higgs boson in 2012, experiments at the LHC have been testing Standard Model predictions with high precision measurements. Measurements of the off-shell couplings of the Higgs boson will remove certain degeneracies that cannot be resolved with the current on-shell measurements, such as probing the Higgs boson width, which may lead to hints for new physics. One part of this thesis focuses on the measurement of the off-shell couplings of the Higgs boson produced by vector boson fusion and decaying to four leptons. This decay channel provides a unique opportunity to probe the Higgs in its off-shell regime due to enhanced cross-sections beyond 2M_z (twice the mass of the Z boson) region of the four lepton mass. The significant quantum interference between the signal and background processes renders the concept of 'class labels' ill-defined, and poses a challenge to traditional methods and generic machine learning classification models used to optimise a signal strength measurement. A new family of machine learning based likelihood-free inference strategies, which leverage additional information that can be extracted from the simulator, were adapted to a signal strength measurement problem. The study shows promising results compared to baseline techniques on a fast simulated Delphes dataset. Also introduced in this context is the aspiration network, an improved adversarial algorithm for training while maintaining invariance with respect to chosen features. Measurements in the ATLAS experiment rely on large amounts of precise simulated data. The current Geant4 simulation software is computationally too expensive to sustain the large amount of simulated data required for planned future analyses. The other part of this thesis focuses on a new approach to fast simulation using a Generative Adversarial Network (GAN). The cascading shower simulation of the complex ATLAS calorimeter is the slowest part of the simulation chain using Geant4. Replacing it with a neural network that has learnt the probability distribution of the particle showers as a function of the incident particle properties and local detector geometry increases the simulation speed by several orders of magnitude, even on single core CPUs, and opens to door the further speed up on GPUs. The integration into the ATLAS software allows for the first time to make realistic comparisons to hand-designed fast simulation frameworks. The study is performed on a small section of the detector (0.20 < |η| < 0.25) using photons and compares distributions using samples simulated by the model standalone as well as after integration into the ATLAS software against fully simulated Geant4 samples. Important lessons on the merits and demerits of various strategies, benefit the ultimate goal of simulating the entire ATLAS calorimeter with a few deep generative models. The study also reveals an inherent problem with the popular gradient penalty based Wasserstein GAN, and proposes a solution. (author)