Abstract: Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures.

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: A search for the pair-production of vector-like quarks optimized for decays into a Z boson and a third-generation Standard Model quark is presented, using the full Run 2 dataset corresponding to 139 fb-1 of pp collisions at & RADIC;s = 13 TeV, collected in 2015-2018 with the ATLAS detector at the Large Hadron Collider. The targeted final state is characterized by the presence of a Z boson with high transverse momentum, reconstructed from a pair of same-flavour leptons with opposite-sign charges, as well as by the presence of b-tagged jets and high-transverse-momentum large-radius jets reconstructed from calibrated smaller-radius jets. Events with exactly two or at least three leptons are used, which are further categorized by the presence of boosted W, Z, and Higgs bosons and top quarks. The categorization is performed using a neural-network-based boosted object tagger to enhance the sensitivity to signal relative to the background. No significant excess above the background expectation is observed and exclusion limits at 95% confidence level are set on the masses of the vector-like partners T and B of the top and bottom quarks, respectively. The limits depend on the branching ratio configurations and, in the case of 100% branching ratio for T-+ Zt and 100% branching ratio for B-+ Zb, this search sets the most stringent limits to date, allowing mT > 1.60 TeV and mB > 1.42 TeV, respectively.

Abstract: Using data recorded in 2011 with the ATLAS detector at the Large Hadron Collider, a search for evidence of extra spatial dimensions has been performed through an analysis of the diphoton final state. The analysis uses data corresponding to an integrated luminosity of 2.12 fb(-1) of root s = 7 TeV proton-proton collisions. The diphoton invariant mass (m(gamma gamma)) spectrum is observed to be in good agreement with the expected Standard Model background. In the large extra dimension scenario of Arkani-Hamed, Dimopoulos and Dvali, the results provide 95% CL lower limits on the fundamental Planck scale between 2.27 and 3.53 TeV, depending on the number of extra dimensions and the theoretical formalism used. The results also set 95% CL lower limits on the lightest Randall-Sundrum graviton mass of between 0.79 and 1.85 TeV, for values of the dimensionless coupling k/(M) over bar (Pl) varying from 0.01 to 0.1. Combining with previously published ATLAS results from the dielectron and dimuon final states, the 95% CL lower limit on the Randall-Sundrum graviton mass for k/(M) over bar (Pl) = 0.01 (0.1) is 0.80 (1.95) TeV.

Abstract: This Letter describes a model-independent search for the production of new resonant states in photon + jet events in 2.11 fb(-1) of proton-proton collisions at root s = 7 TeV. We compare the photon + jet mass distribution to a background model derived from data and find consistency with the background-only hypothesis. Given the lack of evidence for a signal, we set 95% credibility level limits on generic Gaussian-shaped signals and on a benchmark excited-quark (q*) model, excluding 2 TeV Gaussian resonances with cross section times branching fraction times acceptance times efficiency near 5 fb and excluding q* masses below 2.46 TeV, respectively.