Abstract: We have been developing a novel radiation tolerant n(+)-in-p silicon microstrip sensor for very high radiation environments, aiming for application in the high luminosity large hadron collider. The sensors are fabricated in 6 in., p-type, float zone wafers, where large area strip sensor designs are laid out together with a number of miniature sensors. Radiation tolerance has been studied with ATLAS07 sensors and with independent structures. The ATLAS07 design was developed into new ATLAS12 designs. The ATLAS12A large-area sensor is made towards an axial strip sensor and the ATLAS12M towards a stereo strip sensor. New features to the ATLAS12 sensors are two dicing lines: standard edge space of 910 pm and slim edge space of 450 pm, a gated punch-through protection structure, and connection of orphan strips in a triangular corner of stereo strips. We report the design of the ATLAS12 layouts and initial measurements of the leakage current after dicing and the resistivity of the wafers.

Abstract: MoEDAL is designed to identify new physics in the form of long-lived highly ionizing particles produced in high-energy LHC collisions. Its arrays of plastic nuclear-track detectors and aluminium trapping volumes provide two independent passive detection techniques. We present here the results of a first search for magnetic monopole production in 13 TeV proton-proton collisions using the trapping technique, extending a previous publication with 8 TeV data during LHC Run 1. A total of 222 kg of MoEDAL trapping detector samples was exposed in the forward region and analyzed by searching for induced persistent currents after passage through a superconducting magnetometer. Magnetic charges exceeding half the Dirac charge are excluded in all samples and limits are placed for the first time on the production of magnetic monopoles in 13 TeV pp collisions. The search probes mass ranges previously inaccessible to collider experiments for up to five times the Dirac charge.

Abstract: The MoEDAL experiment at Point 8 of the LHC ring is the seventh and newest LHC experiment. It is dedicated to the search for highly-ionizing particle avatars of physics beyond the Standard Model, extending significantly the discovery horizon of the LHC. A MoEDAL discovery would have revolutionary implications for our fundamental understanding of the Microcosm. MoEDAL is an unconventional and largely passive LHC detector comprised of the largest array of Nuclear Track Detector stacks ever deployed at an accelerator, surrounding the intersection region at Point 8 on the LHC ring. Another novel feature is the use of paramagnetic trapping volumes to capture both electrically and magnetically charged highly-ionizing particles predicted in new physics scenarios. It includes an array of TimePix pixel devices for monitoring highly-ionizing particle backgrounds. The main passive elements of the MoEDAL detector do not require a trigger system, electronic readout, or online computerized data acquisition. The aim of this paper is to give an overview of the MoEDAL physics reach, which is largely complementary to the programs of the large multipurpose LHC detectors ATLAS and CMS.

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: 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.