Abstract: The inner tracker of the ATLAS detector is scheduled to be replaced by a completely new silicon-based inner tracker (ITk) for the Phase-II of the CERN LHC (HL-LHC). The silicon strip detector covers the volume 40 < R < 100 cm in the radial and vertical bar z vertical bar <300 cm in the longitudinal directions. The silicon sensors for the detector will be fabricated using the n(+)-on-p 6-inch wafer technology, for a total of 22,000 wafers. Intensive studies were carried out on the final prototype sensors ATLAS17LS fabricated by Hamamatsu Photonics (HPK). The charge collection properties were examined using penetrating Sr-90 beta-rays and the ALIBAVA fast readout system for the miniature sensors of 1 cm xl cm in area. The samples were irradiated by protons in the 27 MeV Birmingham Cyclotron, the 70 MeV CYRIC at Tohoku University, and the 24 GeV CERN-PS, and by neutrons at Ljubljana TAIGA reactor for fluence values up to 2 x 10(15) n(eq)/cm(2). The change in the charge collection with fluence was found to be similar to the previous prototype ATLAS12, and acceptable for the ITk. Sensors with two active thicknesses, 300 μm (standard) and 240 μm (thin), were compared and the difference in the charge collection was observed to be small for bias voltages up to 500 V. Some samples were also irradiated with gamma radiation up to 2 MGy, and the full depletion voltage was found to decrease with the dose. This was caused by the Compton electrons due to the( 60)Co gamma radiation. To summarize, the design of the ATLAS17LS and technology for its fabrication have been verified for implementation in the ITk. We are in the stage of sensor pre-production with the first sensors already delivered in January of 2020.

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.