We present 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 3-dimensional search A → BC, for mA ∼ O(TeV), mB,mC ∼ O(100 GeV) and B,C → large-radius jet, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full Run 2 √s = 13 TeV pp collision dataset of 139 fb−1 recorded by the ATLAS detector at the Large Hadron Collider is used for the search. There is no significant evidence for a localized excess in the dijet invariant mass spectrum between 1.8 and and 8.2 TeV. Cross section limits for narrow-width A, B, and C particles vary with mA, mB, and mC. For example, when mA = 3 TeV and mB ≥ 200 GeV, a production cross section between 1 and 5 fb is excluded, depending on mC. These limits are up to 20 times more sensitive than those obtained by the inclusive dijet search.
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Direct link https://cern.zoom.us/j/99990161911?pwd=Wk5xVzhzSTd4MXV2ZHllMHl0TDVoUT09
Meeting ID 999 9016 1911