A Rad-hard CMOS Active Pixel Sensor for Electron Microscopy

Monolithic CMOS pixel sensors offer unprecedented opportunities for fast nano-imaging through direct electron detection in transmission electron microscopy. We present the design and a full characterisation of a CMOS pixel test structure able to withstand doses in excess of 1 MRad. Data collected with electron beams at various energies of interest in electron microscopy are compared to predictions of simulation and to 1.5 GeV electron data to disentagle the effect of multiple scattering. The point spread function measured with 300 keV electrons is (8.1 +/- 1.6) micron for 10 micron pixel and (10.9 +/- 2.3) micron for 20 micron pixels, respectively, which agrees well with the values of 8.4 micron and 10.5 micron predicted by our simulation.Comment: 16 pages, 9 figures, submitted to Nucl. Instr and Meth

The Irish and policing in Islington

A report to Safer CitiesSIGLEAvailable from British Library Document Supply Centre-DSC:OP-LG/8564 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation