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 ffiffi s p ¼ 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 smallradius 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 σðpp → Y → XH → qqb¯ b¯) for signals with mY between 1.5 and 6 TeV and mX between 65 and 3000 GeV.

Aad, G., Abbott, B., Abbott, D., Abeling, K., Abidi, S., Aboulhorma, A., et al. (2023). 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. PHYSICAL REVIEW D, 108(5), 1-33 [10.1103/PhysRevD.108.052009].

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

Alfonsi, F.;Bindi, M.;Boscherini, D.;Cabras, G.;Carratta, G.;Cavalli, N.;De Castro, S.;Fabbri, F.;Fabbri, L.;Franchini, M.;Gabrielli, A.;Lasagni Manghi, F.;Massa, L.;