Landscape around the vacuum

This project aims to develop innovative analysis techniques using machine learning and simulation-based statistical inference to measure the Higgs boson self-interaction in the ATLAS experiment at CERN´s Large Hadron Collider (LHC).

This measurement of the Higgs boson self-interaction is crucial for testing the Standard Model, making it one of the highest-priority tasks in particle physics. Deviations from the prediction could shed light on some of the unresolved questions about the Universe. Two main approaches will be pursued.

Firstly, fast machine-learning algorithms will be developed to improve the efficiency of selecting proton-proton collisions creating pairs of Higgs bosons in real time. These algorithms will be implemented in the ATLAS trigger system until 2029 in parallel with the LHC upgrade.

Secondly, simulation-based statistical inference methods will be employed to develop an optimal analysis for probing the Higgs boson self-interaction with the ATLAS data collected so far.

The importance of this research lies in its potential to advance our understanding of the Universe´s fundamental properties.

The project deliverables will increase the experimental sensitivity to the  Higgs boson self-interaction, allowing us to truly probe the Standard Model.

Furthermore, developing novel analysis techniques contributes to the broader research and strengthens our ability to extract meaningful insights from complex experimental data.