اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدApplications of Many-Core Technologies to On-line Event Reconstruction in High Energy Physics Experiments
136
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Interest in many-core architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of many-core devices when applied to a typical HEP online task: the selection of events based on the trajectories of charged particles. We use as benchmark a scaled-up version of the algorithm used at CDF experiment at Tevatron for online track reconstruction - the SVT algorithm - as a realistic test-case for low-latency trigger systems using new computing architectures for LHC experiment. We examine the complexity/performance trade-off in porting existing serial algorithms to many-core devices. We measure performance of different architectures (Intel Xeon Phi and AMD GPUs, in addition to NVidia GPUs) and different software environments (OpenCL, in addition to NVidia CUDA). Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the many-core devices.
قيم البحث
اقرأ أيضاً
Interest in parallel architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of Graphic Processing Units (GPUs) and Intel Many Integrated Core architecture
(MIC) when applied to a typical HEP online task: the selection of events based on the trajectories of charged particles. We use as benchmark a scaled-up version of the algorithm used at CDF experiment at Tevatron for online track reconstruction - the SVT algorithm - as a realistic test-case for low-latency trigger systems using new computing architectures for LHC experiment. We examine the complexity/performance trade-off in porting existing serial algorithms to many-core devices. Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the parallel devices.
Development of optical links with 850 nm multi-mode vertical-cavity surface-emitting lasers (VCSELs) has advanced to 25 Gbps in speed. For applications in high-energy experiments, the transceivers are required to be tolerant in radiation and particle
fields. We report on prototyping of a miniature transmitter named MTx+, which is developed for high speed transmission with the dual-channel laser driver LOCld65 and 850 nm VCSELs packaged in TOSA format. The LOCld65 is fabricated in the TSMC 65 nm process and is packaged in the QFN-40 for assembly. The MTx+ modules and test kits were first made with PCB and components qualified for 10 Gbps applications, and were tested for achieving 14 Gbps. The data transfer rate of the MTx+ module is investigated further for the speed of up to 25 Gbps. The LOCld65 is examined with post-layout simulation and the module design upgraded with components including the TOSA qualified for 25 Gbps applications. The PCB material is replaced by the Panasonic MEGTRON6. The revised MTx+ is tested at 25 Gbps and the eye-diagram shows a mask margin of 22 %.
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy
collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.
Charge trapping degrades the energy resolution of germanium (Ge) detectors, which require to have increased experimental sensitivity in searching for dark matter and neutrinoless double-beta decay. We investigate the charge trapping processes utilizi
ng nine planar detectors fabricated from USD-grown crystals with well-known net impurity levels. The charge collection efficiency as a function of charge trapping length is derived from the Shockley-Ramo theorem. Furthermore, we develop a model that correlates the energy resolution with the charge collection efficiency. This model is then applied to the experimental data. As a result, charge collection efficiency and charge trapping length are determined accordingly. Utilizing the Lax model (further developed by CDMS collaborators), the absolute impurity levels are determined for nine detectors. The knowledge of these parameters when combined with other traits such as the Fano factor serve as a reliable indicator of the intrinsic nature of charge trapping within the crystals. We demonstrate that electron trapping is more severe than hole trapping in a p-type detector and the charge collection efficiency depends on the absolute impurity level of the Ge crystal when an adequate bias voltage is applied to the detector. Negligible charge trapping is found when the absolute impurity level is less than 1.0$times$10$^{11}/$cm$^{3}$ for collecting electrons and 2.0$times$10$^{11}/$cm$^{3}$ for collecting holes.
We present a procedure for reconstructing particle cascades from event data measured in a high energy physics experiment. For evaluating the hypothesis of a specific physics process causing the observed data, all possible reconstructi
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
