Dijet Resonance Search with Weak Supervision Using s =13 TeV pp Collisions in the ATLAS Detector

The ATLAS collaboration

Результат исследования: Научные публикации в периодических изданияхстатьярецензирование

10 Цитирования (Scopus)

Аннотация

This Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. 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→BC, for mA∼O(TeV), mB,mC∼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 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 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 mA, mB, and mC. For example, when mA=3 TeV and mBâ200 GeV, a production cross section between 1 and 5 fb is excluded at 95% confidence level, depending on mC. For certain masses, these limits are up to 10 times more sensitive than those obtained by the inclusive dijet search. These results are complementary to the dedicated searches for the case that B and C are standard model bosons.

Язык оригинала английский 131801 131801 23 Physical Review Letters 125 13 https://doi.org/10.1103/PhysRevLett.125.131801 Опубликовано - 25 сен 2020

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