Aad, G.Abbott, B.Abbott, D. C.Abeling, K.Abidi, S. H.Aboulhorma, A.Çetin, Serkant Ali2024-05-192024-05-1920232470-00102470-0029https://doi.org10.1103/PhysRevD.108.052009https://hdl.handle.net/20.500.12713/5822A 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 root 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 bb, 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 sigma(pp -> Y -> XH -> qqbb) for signals with m(Y) between 1.5 and 6 TeV and m(X) between 65 and 3000 GeV.eninfo:eu-repo/semantics/openAccessBootstrapArticle1085WOS:0010884483000012-s2.0-85175428199N/A10.1103/PhysRevD.108.052009Q1