Searching for supersymmetry using deep learning with the ATLAS detector

The Standard Model of particle physics (SM) is a fundamental theory of nature whose validity has been extensively confirmed by experiments. However, some theoretical and experimental problems subsist, which motivates searches for alternative theories to supersede it. Supersymmetry (SUSY), which associate new fundamental particles to each SM particle, is one of the best-motivated such theory and could solve some of the biggest outstanding problems with the SM. For example, many SUSY scenarios predict stable neutral particles that could explain observations of dark matter in the universe. The discovery of SUSY would also represent a huge step towards a unified theory of the universe. Searches for SUSY are at the heart of the experimental program of the ATLAS collaboration, which exploits a state-of-the-art particle detector installed at the Large Hadron Collider (LHC) at CERN in Geneva. The probability to observe many supersymmetric particles went up when the LHC ramped up its collision energy to 13 TeV, the highest ever achieved in laboratory, but so far no evidence for SUSY has been recorded by current searches, which are mostly based on well-known simple techniques such as counting experiments. This thesis documents the implementation of a novel deep learning-based approach using only the four-momenta of selected physics objects, and its application to the search for supersymmetric particles using the full ATLAS 2015-2018 $\sqrt{s}=13$ TeV dataset. Motivated by naturalness considerations as well as by the problem of dark matter, the search focuses on finding evidence for supersymmetric partners of the gluon (the gluino), third generation quarks (the stop and the sbottom), and gauge bosons (the neutralino). Many recently introduced physics-specific machine learning developments are employed, such as directly using detector-recorded energies and momenta of produced particles instead of first deriving a restricted set of physically motivated variables and parametrizing the classification model with the masses of the particles searched for, which allows optimal sensitivity for all mass hypothesis. This method improves the statistical significance of the search by up to 85 times that of the previous ATLAS analysis for some mass hypotheses, after accounting for the luminosity difference. No significant excesses above the SM background are recorded. Gluino masses below 2.45 TeV and neutralino masses below 1.7 TeV are excluded at the 95% confidence level, greatly increasing the previous limit on two simplified models of gluino pair production with off-shell stops and sbottoms, respectively.