TY - JOUR
T1 - Anomaly detection search for new resonances decaying into a Higgs boson and a generic new particle X in hadronic final states using √s=13 TeV pp collisions with the ATLAS detector
AU - ATLAS Publications
AU - Clark, Philip James
AU - Farrington, Sinead
AU - Gao, Yanyan
AU - Leonidopoulos, Christos
AU - Martin, Victoria Jane
AU - Mijovic, Liza
AU - Wynne, Benjamin
AU - Alderweireldt, Sara
AU - Carter, T.M.
AU - Gonzalez Andana, R.Y.
AU - Hasib, A.
AU - Heath, M.P.
AU - Parrish, V.A.
AU - Pender, E.A.
AU - Takeva, E.P.
AU - Themistokleous, N.
AU - Villhauer, E.M.
AU - Zaid, E.
PY - 2023/9/18
Y1 - 2023/9/18
N2 - A search is presented for a heavy resonance Y decaying into a Standard Model Higgs boson H and a new particle X in a fully hadronic final state. The full Large Hadron Collider run 2 dataset of proton-proton collisions at √s=13 TeV collected by the ATLAS detector from 2015 to 2018 is used and corresponds to an integrated luminosity of 139 fb−1. The search targets the high Y-mass region, where the H and X have a significant Lorentz boost in the laboratory frame. A novel application of anomaly detection is used to define a general signal region, where events are selected solely because of their incompatibility with a learned background-only model. It is constructed using a jet-level tagger for signal-model-independent selection of the boosted X particle, representing the first application of fully unsupervised machine learning to an ATLAS analysis. Two additional signal regions are implemented to target a benchmark X decay into two quarks, covering topologies where the X is reconstructed as either a single large-radius jet or two small-radius jets. The analysis selects Higgs boson decays into b¯b, and a dedicated neural-network-based tagger provides sensitivity to the boosted heavy-flavor topology. No significant excess of data over the expected background is observed, and the results are presented as upper limits on the production cross section σ(pp→Y→XH→q¯qb¯b) for signals with mY between 1.5 and 6 TeV and mX between 65 and 3000 GeV.
AB - A search is presented for a heavy resonance Y decaying into a Standard Model Higgs boson H and a new particle X in a fully hadronic final state. The full Large Hadron Collider run 2 dataset of proton-proton collisions at √s=13 TeV collected by the ATLAS detector from 2015 to 2018 is used and corresponds to an integrated luminosity of 139 fb−1. The search targets the high Y-mass region, where the H and X have a significant Lorentz boost in the laboratory frame. A novel application of anomaly detection is used to define a general signal region, where events are selected solely because of their incompatibility with a learned background-only model. It is constructed using a jet-level tagger for signal-model-independent selection of the boosted X particle, representing the first application of fully unsupervised machine learning to an ATLAS analysis. Two additional signal regions are implemented to target a benchmark X decay into two quarks, covering topologies where the X is reconstructed as either a single large-radius jet or two small-radius jets. The analysis selects Higgs boson decays into b¯b, and a dedicated neural-network-based tagger provides sensitivity to the boosted heavy-flavor topology. No significant excess of data over the expected background is observed, and the results are presented as upper limits on the production cross section σ(pp→Y→XH→q¯qb¯b) for signals with mY between 1.5 and 6 TeV and mX between 65 and 3000 GeV.
U2 - 10.1103/PhysRevD.108.052009
DO - 10.1103/PhysRevD.108.052009
M3 - Article
SN - 2470-0010
VL - 108
SP - 1
EP - 33
JO - Physical Review D
JF - Physical Review D
IS - 5
M1 - 052009
ER -