Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHybridSeeding: A standalone track reconstruction algorithm for scintillating fibre tracker at LHCb
84
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We describe the Hybrid seeding, a standalone pattern recognition algorithm aiming at finding charged particle trajectories for the LHCb upgrade. A significant improvement to the charged particle reconstruction efficiency is accomplished by exploiting the knowledge of the LHCb magnetic field and the position of energy deposits in the scintillating fibre tracker detector. Moreover, we achieve a low fake rate and a small contribution to the overall timing budget of the LHCb real-time data processing.
rate research
Read More
The Muon Ionization Cooling Experiment (MICE) will demonstrate the principle of muon beam phase-space reduction via ionization cooling. Muon beam cooling will be required for the proposed Neutrino Factory or Muon Collider. The phase-space before and after the cooling cell must be measured precisely. This is achieved using two scintillating-fibre trackers, each placed in a solenoidal magnetic field. This paper describes the software reconstruction for the fibre trackers: the GEANT4 based simulation; the implementation of the geometry; digitisation; space-point reconstruction; pattern recognition; and the final track fit based on a Kalman filter. The performance of the software is evaluated by means of Monte Carlo studies and the precision of the final track reconstruction is evaluated.
We present an alternative implementation of the Kalman filter employed for track fitting within the LHCb experiment. It uses simple parametrizations for the extrapolation of particle trajectories in the field of the LHCb dipole magnet and for the effects of multiple scattering in the detector material. A speedup of more than a factor of four is achieved while maintaining the quality of the estimated track quantities. This Kalman filter implementation could be used in the purely software-based trigger of the LHCb upgrade.
One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core SIMD architectures that are now prevalent in high-performance hardware. In this paper, we discuss the design and performance of the improved tracking algorithm, referred to as mkFit. A key piece of the algorithm is the Matriplex library, containing dedicated code to optimally vectorize operations on small matrices. The physics performance of the mkFit algorithm is comparable to the nominal CMS tracking algorithm when reconstructing tracks from simulated proton-proton collisions within the CMS detector. We study the scaling of the algorithm as a function of the parallel resources utilized and find large speedups both from vectorization and multi-threading. mkFit achieves a speedup of a factor of 6 compared to the nominal algorithm when run in a single-threaded application within the CMS software framework.
The upgraded CERN LHCb detector, due to start data taking in 2021, will have to reconstruct 4 TB/s of raw detector data in real time using commodity processors. This is one of the biggest real-time data processing challenges in any scientific domain. We present an intrinsically parallel reconstruction algorithm for the vertex detector of the LHCb experiment designed to optimally exploit multi-core general purpose architectures. We compare it to previous state-of-the-art scalar pattern recognition algorithms and show significantly faster processing and in some cases increased physics performance over all current alternatives. We evaluate the algorithm on two high-end architectures from two different vendors and discuss in detail the impact of different SIMD Instruction Set Architecture extensions on the performance.
We present the results of an R&D study of a specialized processor capable of precisely reconstructing events with hundreds of charged-particle tracks in pixel detectors at 40 MHz, thus suitable for processing LHC events at the full crossing frequency. For this purpose we design and test a massively parallel pattern-recognition algorithm, inspired by studies of the processing of visual images by the brain as it happens in nature. We find that high-quality tracking in large detectors is possible with sub-$mu$s latencies when this algorithm is implemented in modern, high-speed, high-bandwidth FPGA devices. This opens a possibility of making track reconstruction happen transparently as part of the detector readout.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
