Abstract: This paper describes the electronics of NEXT-White (NEW) detector PMT plane, a high pressure xenon TPC with electroluminescent amplification (HPXe-EL) currently operating at the Laboratorio Subterraneo de Canfranc (LSC) in Huesca, Spain. In NEXT-White the energy of the event is measured by a plane of photomultipliers (PMTs) located behind a transparent cathode. The PMTs are Hamamatsu R11410-10 chosen due to their low radioactivity. The electronics have been designed and implemented to fulfill strict requirements: an overall energy resolution below 1% and a radiopurity budget of 20 mBq unit(-1) in the chain of Bi-214. All the components and materials have been carefully screened to assure a low radioactivity level and at the same time meet the required front-end electronics specifications. In order to reduce low frequency noise effects and enhance detector safety a grounded cathode connection has been used for the PMTs. This implies an AC-coupled readout and baseline variations in the PMT signals. A detailed description of the electronics and a novel approach based on a digital baseline restoration to obtain a linear response and handle AC coupling effects is presented. The final PMT channel design has been characterized with linearity better than 0.4% and noise below 0.4 mV.

Abstract: The BABAR detector operated successfully at the PEP-Il asymmetric e(+) e(-) collider at the SLAC National Accelerator Laboratory from 1999 to 2008. This report covers upgrades, operation, and performance of the collider and the detector systems, as well as the trigger, online and offline computing, and aspects of event reconstruction since the beginning of data taking.

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.