Dijet resonance search with weak supervision using $\sqrt{s}$ = 13 TeV pp collisions in the ATLAS detector

This talk describes a search for resonant new physics using a machine-learning anomaly detection procedure that does not rely on a signal model hypothesis. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is essentially a three-dimensional search A$\rightarrow$BC, for $m_A$∼O(TeV), $m_B$,$m_C$~O(100 GeV) and B,C are reconstructed as large-radius jets, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full Run 2 $\sqrt{s}$ = 13 TeV pp collision data set of 139 fb$^{−1}$ recorded by the ATLAS detector at the Large Hadron Collider is used for the search. There is no significant evidence of a localized excess in the dijet invariant mass spectrum between 1.8 and 8.2 TeV. Cross-section limits for narrow-width A, B, and C particles vary with $m_A$, $m_B$, and $m_C$. For example, when $m_A$ = 3 TeV and $m_B$ ≥ 200 GeV, a production cross section between 1 and 5 fb is excluded at 95% confidence level, depending on $m_C$. For certain masses, these limits are up to 10 times more sensitive than those obtained by the inclusive dijet search.