Abstract: The first search for the doubly heavy Omega(0)(bc) baryon and a search for the Xi(0)(bc) baryon are performed using collision data collected via the experiment from 2016 to 2018 at a centre-of-mass energy of, corresponding to an integrated luminosity of 5.2 fb(-1). The baryons are reconstructed via their decays to Lambda(+)(-)(c)(pi) and Xi(+)(c)pi(-). No significant excess is found for invariant masses between 6700 and 7300 MeV/c(2), in a rapidity range from 2.0 to 4.5 and a transverse momentum range from 2 to 20 MeV/c. Upper limits are set on the ratio of the Omega(0)(bc) and Xi(0)(bc) production cross-section times the branching fraction to Lambda(+)(c)pi(-)(Xi(+)(c)pi(-)) relative to that of the Lambda(0)(b)(Xi(0)(b)) baryon, for different lifetime hypotheses, at 95% confidence level. The upper limits range from 0.5x10(-4) to 2.5x10(-4) for the Omega(0)(bc) -> Lambda(+)(c)pi(-) (Xi(0)(bc) -> Lambda(+)(c)pi(-)) decay, pending on the considered mass and lifetime of the Omega(0)(bc) (Xi(0)(bc)) baryon.

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: We study the forward-backward asymmetry A(FB) in pp -> l(+)l(-) at the Z peak within the Standard Model Effective Field Theory (SMEFT). We find that this observable provides per mille level constraints on the vertex corrections of the Z boson to quarks, which close a flat direction in the electroweak precision SMEFT fit. Moreover, we show that current A(FB) data is precise enough so that its inclusion in the fit improves significantly LEP bounds even in simple New Physics setups. This demonstrates that the LHC can compete with and complement LEP when it comes to precision measurements of the Z boson properties.

Abstract: A search for supersymmetry in events with four or more charged leptons (electrons, muons and tau-leptons) is presented. The analysis uses a data sample corresponding to 139 fb(-1) of proton-proton collisions delivered by the Large Hadron Collider at root s = 13 TeV and recorded by the ATLAS detector. Four-lepton signal regions with up to two hadronically decaying tau-leptons are designed to target several supersymmetric models, while a general five-lepton signal region targets any new physics phenomena leading to a final state with five charged leptons. Data yields are consistent with Standard Model expectations and results are used to set upper limits on contributions from processes beyond the Standard Model. Exclusion limits are set at the 95% confidence level in simplified models of general gauge-mediated supersymmetry, excluding higgsino masses up to 540 GeV. In R-parity-violating simplified models with decays of the lightest supersymmetric particle to charged leptons, lower limits of 1.6 TeV, 1.2 TeV, and 2.5 TeV are placed on wino, slepton and gluino masses, respectively.

Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.