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

Abstract

This Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on a 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 $A\to BC$, for $m_A\sim \mathcal{O}(\text{TeV})$, $m_B,m_C\sim \mathcal{O}(100\text{ GeV})$ 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 $m_B\gtrsim 200$ 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. These results are complementary to the dedicated searches for the case that $B$ and $C$ are Standard Model bosons.

32 pages in total, author list starting page 16, 3 figures, published in Phys. Rev. Lett. 125 (2020) 131801. All figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/HDBS-2018-59/

References in corpus (11)

• PYTHIA 6.4 Physics and Manual
• An Introduction to PYTHIA 8.2
• Classification without labels: Learning from mixed samples in high energy physics
• Search for additional heavy neutral Higgs and gauge bosons in the ditau final state produced in 36 fb${}^{-1}$ of $pp$ collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector
• Extending the Bump Hunt with Machine Learning
• A generic anti-QCD jet tagger
• Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at sqrt(s) = 13 TeV
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Search for heavy particles decaying into a top-quark pair in the fully hadronic final state in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Search for heavy resonances decaying to tau lepton pairs in proton-proton collisions at sqrt(s) = 13 TeV
• Search for low-mass resonances decaying into two jets and produced in association with a photon using $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

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