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Register a new userTesla : an application for real-time data analysis in High Energy Physics
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Upgrades to the LHCb computing infrastructure in the first long shutdown of the LHC have allowed for high quality decay information to be calculated by the software trigger making a separate offline event reconstruction unnecessary. Furthermore, the storage space of the triggered candidate is an order of magnitude smaller than the entire raw event that would otherwise need to be persisted. Tesla, following the LHCb renowned physicist naming convention, is an application designed to process the information calculated by the trigger, with the resulting output used to directly perform physics measurements.
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One of the biggest challenges in the High-Luminosity LHC (HL- LHC) era will be the significantly increased data size to be recorded and analyzed from the collisions at the ATLAS and CMS experiments. ServiceX is a software R&D project in the area of Data Organization, Management and Access of the IRIS- HEP to investigate new computational models for the HL- LHC era. ServiceX is an experiment-agnostic service to enable on-demand data delivery specifically tailored for nearly-interactive vectorized analyses. It is capable of retrieving data from grid sites, on-the-fly data transformation, and delivering user-selected data in a variety of different formats. New features will be presented that make the service ready for public use. An ongoing effort to integrate ServiceX with a popular statistical analysis framework in ATLAS will be described with an emphasis of a practical implementation of ServiceX into the physics analysis pipeline.
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.
The upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4~TB/s in real time. Because LHCbs physics objectives require that the full detector information for every LHC bunch crossing is read out and made available for real-time processing, this bandwidth challenge is equivalent to that of the ATLAS and CMS HL-LHC software read-out, but deliverable five years earlier. Over the past six years, the LHCb collaboration has undertaken a bottom-up rewrite of its software infrastructure, pattern recognition, and selection algorithms to make them better able to efficiently exploit modern highly parallel computing architectures. We review the impact of this reoptimization on the energy efficiency of the real-time processing software and hardware which will be used for the upgrade of the LHCb detector. We also review the impact of the decision to adopt a hybrid computing architecture consisting of GPUs and CPUs for the real-time part of LHCbs future data processing. We discuss the implications of these results on how LHCbs real-time power requirements may evolve in the future, particularly in the context of a planned second upgrade of the detector.
CMOS pixel sensors (CPS) represent a novel technological approach to building charged particle detectors. CMOS processes allow to integrate a sensing volume and readout electronics in a single silicon die allowing to build sensors with a small pixel pitch ($sim 20 mu m$) and low material budget ($sim 0.2-0.3% X_0$) per layer. These characteristics make CPS an attractive option for vertexing and tracking systems of high energy physics experiments. Moreover, thanks to the mass production industrial CMOS processes used for the manufacturing of CPS the fabrication construction cost can be significantly reduced in comparison to more standard semiconductor technologies. However, the attainable performance level of the CPS in terms of radiation hardness and readout speed is mostly determined by the fabrication parameters of the CMOS processes available on the market rather than by the CPS intrinsic potential. The permanent evolution of commercial CMOS processes towards smaller feature sizes and high resistivity epitaxial layers leads to the better radiation hardness and allows the implementation of accelerated readout circuits. The TowerJazz $0.18 mu m$ CMOS process being one of the most relevant examples recently became of interest for several future detector projects. The most imminent of these project is an upgrade of the Inner Tracking System (ITS) of the ALICE detector at LHC. It will be followed by the Micro-Vertex Detector (MVD) of the CBM experiment at FAIR. Other experiments like ILD consider CPS as one of the viable options for flavour tagging and tracking sub-systems.
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 %.
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