Abstract: Additional Higgs bosons appear in many extensions of the Standard Model (SM). While most existing searches for additional Higgs bosons concentrate on final states consisting of SM particles, final states containing beyond the SM (BSM) particles play an important role in many BSM models. In order to facilitate future searches for such final states, we develop a simplified model framework for heavy Higgs boson decays to a massive SM boson as well as one or more invisible particles. Allowing one kind of BSM mediator in each decay chain, we classify the possible decay topologies for each final state, taking into account all different possibilities for the spin of the mediator and the invisible particles. Our comparison of the kinematic distributions for each possible model realization reveals that the distributions corresponding to the different simplified model topologies are only mildly affected by the different spin hypotheses, while there is significant sensitivity for distinguishing between the different decay topologies. As a consequence, we point out that expressing the results of experimental searches in terms of the proposed simplified model topologies will allow one to constrain wide classes of different BSM models. The application of the proposed simplified model framework is explicitly demonstrated for the example of a mono-Higgs search. For each of the simplified models that are proposed in this paper we provide all necessary ingredients for performing Monte-Carlo simulations such that they can readily be applied in experimental analyses.

Abstract: We report measurements of the flux-integrated (nu) over bar (mu) and (nu) over bar (mu) + nu(mu) charged-current cross -sections on water and hydrocarbon targets using the T2K anti-neutrino beam with a mean beam energy of 0.86 GeV. The signal is defined as the (anti -)neutrino charged-current interaction with one induced mu(+/-) and no detected charged pion or proton. These measurements are performed using a new WAGASCI module recently added to the T2K setup in combination with the INGRID Proton Module. The phase space of muons is restricted to the high-detection efficiency region, p(mu) > 400 MeV/c and theta(mu) < 30 degrees, in the laboratory frame. An absence of pions and protons in the detectable phase spaces of p(pi) > 200 MeV/c, theta(pi) < 70 degrees and p(p) > 600 MeV/c, theta(p) < 70 degrees is required. In this paper, both the <(nu)over bar>(mu), cross-sections and (nu) over bar (mu) + nu(mu), cross-sections on water and hydrocarbon targets and their ratios are provided by using the D'Agostini unfolding method. The results of the integrated (nu) over bar (mu), cross-section measurements over this phase space are sigma(H2O) = (1.082 +/- 0.068(stat.)(+0.145)(-0.128)(syst.)) x 10(-39) cm(2)/nucleon, sigma(CH) = (1.096 +/- 0.054 (stat.)(+0.132)(-0.117)(syst.)) x 10(-39) cm(2) /nucleon, and sigma(H2O)/sigma(CH) = 0.987 +/- 0.078 (stat.)(+0.093)(-0.090)(syst.). The (nu) over bar (mu), + nu(mu), cross-section is sigma(H2O) = (1.155 +/- 0.064(stat.)(+0.148)(-0.129)(syst.)) x 10(-39) cm(2)/nucleon, sigma(CH) = (1.159 +/- 0.049(stat.)(+0.129)(-0.115)(syst.)) x 10(-39) cm(2)/nucleon, and sigma(H2O)/sigma(CH) = 0.996 +/- 0.069(stat.)(+0.083)(-0.078)(syst.).

Abstract: We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.