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.

arXiv comment32 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 (96)

• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• The anti-k_t jet clustering algorithm
• An Introduction to PYTHIA 8.2
• A Brief Introduction to PYTHIA 8.1
• FastJet user manual
• Asymptotic formulae for likelihood-based tests of new physics
• Parton distributions with LHC data
• The ATLAS Simulation Infrastructure
• Dispelling the N^3 myth for the Kt jet-finder
• Performance of the ATLAS Trigger System in 2015
• Jet Trimming
• Production and Integration of the ATLAS Insertable B-Layer
• Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1
• Anomaly Detection with Density Estimation
• Classification without labels: Learning from mixed samples in high energy physics
• Searching for New Physics with Deep Autoencoders
• Anomaly Detection for Resonant New Physics with Machine Learning
• 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
• Heavy Vector Triplets: Bridging Theory and Data
• Learning New Physics from a Machine
• Extending the Bump Hunt with Machine Learning
• Search for narrow resonances decaying to dijets in proton-proton collisions at sqrt(s) = 13 TeV
• Search for New Phenomena in Dijet Mass and Angular Distributions from $pp$ Collisions at $\sqrt{s}$ = 13 TeV with the ATLAS Detector
• Search for new resonances in mass distributions of jet pairs using 139 fb${}^{-1}$ of $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• QCD or What?
• A generic anti-QCD jet tagger
• Search for Dijet Resonances in 7 TeV pp Collisions at CMS
• Simulation Assisted Likelihood-free Anomaly Detection
• Novelty Detection Meets Collider Physics
• Search for New Particles in Two-Jet Final States in 7 TeV Proton-Proton Collisions with the ATLAS Detector at the LHC
• Adversarially-trained autoencoders for robust unsupervised new physics searches
• Guiding New Physics Searches with Unsupervised Learning
• Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
• Variational Autoencoders for New Physics Mining at the Large Hadron Collider
• Search for resonant t t-bar production in proton-proton collisions at sqrt(s) = 8 TeV
• Uncovering latent jet substructure
• Learning to Classify from Impure Samples with High-Dimensional Data
• Search for low-mass dijet resonances using trigger-level jets with the ATLAS detector in $pp$ collisions at $\sqrt{s}=13$ TeV
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at sqrt(s) = 13 TeV
• Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
• Weakly Supervised Classification in High Energy Physics
• Search for pair production of Higgs bosons in the $b\bar{b}b\bar{b}$ final state using proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Global Search for New Physics with 2.0/fb at CDF
• Learning to Pivot with Adversarial Networks
• Search for the third-generation scalar leptoquarks and heavy right-handed neutrinos in final states with two tau leptons and two jets in proton-proton collisions at sqrt(s) = 13 TeV
• Search for a heavy Higgs boson decaying into a $Z$ boson and another heavy Higgs boson in the $\ell \ell bb$ final state in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Combination of searches for heavy resonances decaying into bosonic and leptonic final states using 36 fb${}^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV with the ATLAS detector
• Search for long-lived particles produced in $pp$ collisions at $\sqrt{s}=13$ TeV that decay into displaced hadronic jets in the ATLAS muon spectrometer
• DisCo Fever: Robust Networks Through Distance Correlation
• In situ calibration of large-$R$ jet energy and mass in 13 TeV proton-proton collisions with the ATLAS detector
• Search for heavy neutrinos and third-generation leptoquarks in hadronic states of two $\tau$ leptons and two jets in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Machine Learning Uncertainties with Adversarial Neural Networks
• Search for resonant and non-resonant Higgs boson pair production in the $b\bar{b}{\tau }^{+}{\tau }^{-}$ decay channel in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• A robust anomaly finder based on autoencoders
• Model-Independent and Quasi-Model-Independent Search for New Physics at CDF
• Search for New Particles in the Two-Jet Decay Channel with the D0 Detector
• Search for New Physics in e mu X Data at D0 Using Sleuth: A Quasi-Model-Independent Search Strategy for New Physics
• (Machine) Learning to Do More with Less
• Search for long-lived neutral particles in $pp$ collisions at $\sqrt{s}$ = 13 TeV that decay into displaced hadronic jets in the ATLAS calorimeter
• Search for light resonances decaying to boosted quark pairs and produced in association with a photon or a jet in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• A general search for new phenomena in ep scattering at HERA
• 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
• A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment
• A Quasi-Model-Independent Search for New Physics at Large Transverse Momentum
• Search for heavy resonances decaying to tau lepton pairs in proton-proton collisions at sqrt(s) = 13 TeV
• Search for resonant $\mathrm{t}\overline{\mathrm{t}}$ production in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• A General Search for New Phenomena at HERA
• 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
• Search for diboson resonances in hadronic final states in 139 fb${}^{-1}$ of $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers
• Search for Higgs boson pair production in the $b\bar{b}W{W}^{\ast }$ decay mode at $\sqrt{s}=13$ TeV with the ATLAS detector
• Search for low mass vector resonances decaying to quark-antiquark pairs in proton-proton collisions at sqrt(s) = 13 TeV
• Search for resonances in the mass distribution of jet pairs with one or two jets identified as $b$-jets in proton--proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• A Quasi-Model-Independent Search for New High p_T Physics at DZero
• A multi-dimensional search for new heavy resonances decaying to boosted WW, WZ, or ZZ boson pairs in the dijet final state at 13 TeV
• The performance of the jet trigger for the ATLAS detector during 2011 data taking
• Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Search for low-mass resonances decaying into bottom quark-antiquark pairs in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
• Mass Agnostic Jet Taggers
• Search for production of Higgs boson pairs in the four b quark final state using large-area jets in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Search for low-mass quark-antiquark resonances produced in association with a photon at $\sqrt{s}=$ 13 TeV
• QBDT, a new boosting decision tree method with systematic uncertainties into training for High Energy Physics
• Search for resonances in the mass distribution of jet pairs with one or two jets identified as $b$-jets in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Reducing the dependence of the neural network function to systematic uncertainties in the input space
• Search for heavy resonances decaying into two Higgs bosons or into a Higgs boson and a W or Z boson in proton-proton collisions at 13 TeV
• Search for resonances decaying to a pair of Higgs bosons in the $\mathrm{b}\overline{\mathrm{b}}\mathrm{q}{\overline{\mathrm{q}}}^{\prime }\ell \nu$ final state in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• A search for resonances decaying into a Higgs boson and a new particle $X$ in the $XH\to qqbb$ final state with the ATLAS detector
• Nonparametric semisupervised classification for signal detection in high energy physics
• Search for pairs of highly collimated photon-jets in $pp$ collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector
• The motivation and status of two-body resonance decays after the LHC Run 2 and beyond
• Search for a heavy resonance decaying to a top quark and a vector-like top quark in the lepton+jets final state in pp collisions at $\sqrt{s}=$ 13 TeV
• Study of the B${}^{+}$ $\to$ J$/\psi \overline{\Lambda }$p decay in proton-proton collisions at $\sqrt{s}=$ 8 TeV
• Search for a W' boson decaying to a vector-like quark and a top or bottom quark in the all-jets final state

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• Weakly-Supervised Anomaly Detection in the Milky Way
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