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: A detailed description of the integration structures for the barrel region of the silicon strips tracker of the ATLAS Phase-II upgrade for the upgrade of the Large Hadron Collider, the so-called High Luminosity LHC (HL-LHC), is presented. This paper focuses on one of the latest demonstrator prototypes recently assembled, with numerous unique features. It consists of a shortened, shield-less, and double sided stave, with two candidate power distributions implemented. Thermal and electrical performances of the prototype are presented, as well as a description of the assembly procedures and tools.

Abstract: The reconstruction of photons in the ATLAS detector is studied with data taken during the 2004 Combined Test Beam, where a full slice of the ATLAS detector was exposed to beams of particles of known energy at the CERN SPS. The results presented show significant differences in the longitudinal development of the electromagnetic shower between converted and unconverted photons as well as in the total measured energy. The potential to use the reconstructed converted photons as a means to precisely map the material of the tracker in front of the electromagnetic calorimeter is also considered. All results obtained are compared with a detailed Monte-Carlo simulation of the test-beam setup which is based on the same simulation and reconstruction tools as those used for the ATLAS detector itself.

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: 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.