Abstract: Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests.

Abstract: This paper describes a study of techniques for identifying Higgs bosons at high transverse momenta decaying into bottom-quark pairs, H -> b (b) over bar, for proton-proton collision data collected by the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy root s = 13 TeV. These decays are reconstructed from calorimeter jets found with the anti-k(t) R = 1.0 jet algorithm. To tag Higgs bosons, a combination of requirements is used: b-tagging of R = 0.2 track-jets matched to the large-R calorimeter jet, and requirements on the jet mass and other jet substructure variables. The Higgs boson tagging efficiency and corresponding multijet and hadronic top-quark background rejections are evaluated using Monte Carlo simulation. Several benchmark tagging selections are defined for different signal efficiency targets. The modelling of the relevant input distributions used to tag Higgs bosons is studied in 36 fb(-1) of data collected in 2015 and 2016 using g -> b (b) over bar and Z(-> b (b) over bar)gamma event selections in data. Both processes are found to be well modelled within the statistical and systematic uncertainties.

Abstract: Differential and double-differential distributions of kinematic variables of leptons from decays of top-quark pairs (t (t) over bar) are measured using the full LHC Run 2 data sample collected with the ATLAS detector. The data were collected at a pp collision energy of root s = 13TeV and correspond to an integrated luminosity of 140 fb(-1). The measurements use events containing an oppositely charged e μpair and b-tagged jets. The results are compared with predictions from several Monte Carlo generators. While no prediction is found to be consistent with all distributions, a better agreement with measurements of the lepton p(T) distributions is obtained by reweighting the t (t) over bar sample so as to reproduce the top-quark p(T) distribution from an NNLO calculation. The inclusive top-quark pair production cross-section is measured as well, both in a fiducial region and in the full phase-space. The total inclusive cross-section is found to be sigma(t (t) over bar) = 829 +/- 1 (stat) +/- 13 (syst) +/- 8 (lumi) +/- 2 (beam) pb, where the uncertainties are due to statistics, systematic effects, the integrated luminosity and the beam energy. This is in excellent agreement with the theoretical expectation.