Investigating and Leveraging Machine Learning Techniques for High Energy Particle Identification and Reconstruction

Recent advances in artificial intelligence have revolutionised data analysis across

scientific disciplines, and high energy physics is no exception. This thesis ex-

plores advancements in experimental particle physics through the integration of

machine learning techniques in high energy physics experiments and simulations.

The Standard Model of particle physics and the general theory of relativity are

representing our current understanding of physics, however there are observa-

tions left unexplained. Phenomena, such as the matter-antimatter asymmetry,

neutrino oscillations and the presence of dark matter, motivate the search for

physics beyond the Standard Model. These searches are often heavily relying on

advanced simulation tools and novel AI technologies, providing and classifying

key signatures. The ATLAS experiment at the Large Hadron Collider plays a

crucial role in searching for physics beyond the Standard Model, such as Super-

symmetry, by applying sophisticated data acquisition and analysis techniques.

A major contribution of this work is the application of the NeuralRinger algo-

rithm for forward electron identification in ATLAS. By extending this machine

learning based approach to regions of high pseudorapidity, electron reconstruction

can be significantly improved, enhancing event selection for physics analyses. To

further refine its performance, the NeuralRinger was integrated into the Loren-

zetti Showers framework, a novel and highly flexible calorimetry simulation tool.

Facilitating the NeuralRinger for forward regions in that framework allowed for

further developments of the simulation tool and for direct comparison with the

corresponding ATLAS studies. Finally, this thesis presents a search for pair pro-

duction of the supersymmetric top squark in all-hadronic final states, utilising

signatures identified by a novel graph neural network tool, recently developed

within ATLAS. The promising results of the studies presented in this thesis,

which are collectively relying on machine learning techniques, demonstrate the

growing role of AI-driven methodologies in experimental particle physics. They

o!er improved detection capabilities and enhance the search for new physics be-

yond the Standard Model.