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A search is presented for a heavy resonance $Y$ decaying into a StandardModel 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$\sqrt{s}= 13$ TeV collected by the ATLAS detector from 2015 to 2018 is used,and corresponds to an integrated luminosity of 139 fb$^{-1}$. The searchtargets the high $Y$-mass region, where the $H$ and $X$ have a significantLorentz boost in the laboratory frame. A novel signal region is implementedusing anomaly detection, where events are selected solely because of theirincompatibility with a learned background-only model. It is defined using ajet-level tagger for signal-model-independent selection of the boosted $X$particle, representing the first application of fully unsupervised machinelearning to an ATLAS analysis. Two additional signal regions are implemented totarget 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 $b\bar{b}$, and a dedicatedneural-network-based tagger provides sensitivity to the boosted heavy-flavortopology. No significant excess of data over the expected background isobserved, and the results are presented as upper limits on the production crosssection $\sigma(pp \rightarrow Y \rightarrow XH \rightarrow q\bar{q}b\bar{b}$)for signals with $m_Y$ between 1.5 and 6 TeV and $m_X$ between 65 and 3000 GeV.

cornell university

Anomaly detection search for new resonances decaying into a Higgs boson  and a generic new particle $X$ in hadronic final states using $\sqrt{s} = 13$  TeV $pp$ collisions with the ATLAS detector