Three-dimensional (3D) silicon detectors are emerging as one of the most promising technologies for the innermost layers of tracking devices for the foreseen upgrades of the LHC. 3D sensors ...compatible with the CMS readout, fabricated at FBK (Trento, Italy), were tested in the laboratory and with a 120GeV/c proton beam at the FNAL test beam facility, before and after irradiation up to a fluence of 3.5×1015neq/cm2. Preliminary results of the data analysis are presented.
•3D characterized in laboratory, tested with beam and irradiated with 800MeV protons.•Leakage current: few hundred nA before irradiation, ∼10μA after irradiation.•Depletion voltage: 20V. Breakdown voltage: 25–35V, not increasing after irradiation.•Efficiency: 97.5%, increasing when tilting sensors with respect to the beam.•Radiation effect: lower efficiency and lower collected charge.
Abstract
In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering ...compressed versions of the ENet convolutional neural network architecture, we demonstrate a fully-on-chip deployment with a latency of 4.9 ms per image, using less than 30% of the available resources on a Xilinx ZCU102 evaluation board. The latency is reduced to 3 ms per image when increasing the batch size to ten, corresponding to the use case where the autonomous vehicle receives inputs from multiple cameras simultaneously. We show, through aggressive filter reduction and heterogeneous quantization-aware training, and an optimized implementation of convolutional layers, that the power consumption and resource utilization can be significantly reduced while maintaining accuracy on the Cityscapes dataset.
In preparation for the tenfold luminosity upgrade of the Large Hadron Collider (the HL-LHC) around 2020, three-dimensional (3D) silicon pixel sensors are being developed as a radiation-hard candidate ...to replace the planar ones currently being used in the CMS pixel detector. This study examines an early batch of FBK sensors (named ATLAS08) of three 3D pixel geometries: 1E, 2E, and 4E, which respectively contain one, two, and four readout electrodes for each pixel, passing completely through the bulk. We present electrical characteristics and beam test performance results for each detector before and after irradiation. The maximum fluence applied is 3.5×1015 n eq/cm2.
3D-FBK pixel sensors with CMS readout: First test results Obertino, M.; Solano, A.; Vilela Pereira, A. ...
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
08/2013, Letnik:
718
Journal Article
Recenzirano
Silicon 3D detectors consist of an array of columnar electrodes of both doping types which penetrate entirely in the detector bulk, perpendicularly to the surface. They are emerging as one of the ...most promising technologies for innermost layers of tracking devices for the foreseen upgrades of the LHC. Until recently, properties of 3D sensors have been investigated mostly with ATLAS readout electronics. 3D pixel sensors compatible with the CMS readout were first fabricated at SINTEF (Oslo, Norway), and more recently at FBK (Trento, Italy) and CNM (Barcelona, Spain). Several sensors with different electrode configurations, bump-bonded with the CMS pixel PSI46 readout chip, were characterized in laboratory and tested at Fermilab with a proton beam of 120GeV/c. Preliminary results of the data analysis are presented.
We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals ...of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to \(10~\rm{fb}^{-1}\) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.
A group of Early-Career Researchers (ECRs) has been given a mandate from the European Committee for Future Accelerators (ECFA) to debate the topics of the current European Strategy Update (ESU) for ...Particle Physics and to summarise the outcome in a brief document 1. A full-day debate with 180 delegates was held at CERN, followed by a survey collecting quantitative input. During the debate, the ECRs discussed future colliders in terms of the physics prospects, their implications for accelerator and detector technology as well as computing and software. The discussion was organised into several topic areas. From these areas two common themes were particularly highlighted by the ECRs: sociological and human aspects; and issues of the environmental impact and sustainability of our research.
In this community review report, we discuss applications and techniques for
machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data ...processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.
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