Abstract: A search for heavy resonances decaying into a Higgs boson (H) and a new particle (X) is reported, utilizing 36.1 fb(-1) of proton-proton collision data at root s = 13 TeV collected during 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider. The particle Xis assumed to decay to a pair of light quarks, and the fully hadronic final state XH -> q (q) over bar 'b (b) over bar is analysed. The search considers the regime of high XH resonance masses, where the X and H bosons are both highly Lorentz-boosted and are each reconstructed using a single jet with large radius parameter. A two-dimensional phase space of XH mass versus X mass is scanned for evidence of a signal, over a range of XH resonance mass values between 1 TeV and 4 TeV, and for X particles with masses from 50 GeV to 1000 GeV. All search results are consistent with the expectations for the background due to Standard Model processes, and 95% CL upper limits are set, as a function of XH and X masses, on the production cross-section of the XH -> q (q) over bar 'b (b) over bar resonance.

Abstract: A search for long-lived particles, which have come to rest within the ATLAS detector, is presented. The subsequent decays of these particles can produce high-momentum jets, resulting in large out-of-time energy deposits in the ATLAS calorimeters. These decays are detected using data collected during periods in the LHC bunch structure when collisions are absent. The analysed dataset is composed of events from proton-proton collisions produced by the Large Hadron Collider at a centre-of-mass energy of root s = 13TeV and recorded by the ATLAS experiment during 2017 and 2018. The dataset used for this search corresponds to a total live time of 579 hours. The results of this search are used to derive lower limits on the mass of gluino R-hadrons, assuming a branching fraction B((g) over tilde -> q (q) over bar(chi) over tilde (0)(1)) = 100%, with masses of up to 1.4TeV excluded for gluino lifetimes of 10(-5) to 10(3) s.

Abstract: A search for the Z gamma decay of the Higgs boson, with Z boson decays into pairs of electrons or muons is presented. The analysis uses proton-proton collision data at root s = 13 TeV corresponding to an integrated luminosity of 139 fb(-1) recorded by the ATLAS detector at the Large Hadron Collider. The observed data are consistent with the expected background with a p-value of 1.3%. An upper limit at 95% confidence level on the production cross-section times the branching ratio for pp -> H -> Z gamma is set at 3.6 times the Standard Model prediction while 2.6 times is expected in the presence of the Standard Model Higgs boson. The best-fit value for the signal yield normalised to the Standard Model prediction is 2.0(-0.9)(+1.0) where the statistical component of the uncertainty is dominant.

Abstract: A search for the Xi(++)(cc) baryon through the Xi(++)(cc) -> D(+)pK(-)pi(+) decay is performed with a data sample corresponding to an integrated luminosity of 1.7 fb(-1) recorded by the LHCb experiment in pp collisions at a centre-of-mass energy of 13 TeV. No significant signal is observed in the mass range from the kinematic threshold of the decay to 3800 MeV/c(2). An upper limit is set on the ratio of branching fractions R = B(Xi(++)(cc) -> D(+)pK(-)pi(+))/B(Xi(++)(cc) -> A(c)(+) K- pi(+)pi(+)) with R < 1.7 (2.1) x 10(-2) at the 90% (95%) confidence level at the known mass of the Xi(++)(cc) state.

Abstract: The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the `anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods.