Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAnalytical computation of process noise matrix in Kalman filter for fitting curved tracks in magnetic field within dense, thick scatterers
99
0
0.0
(
0
)
Added by
Kolahal Bhattacharya
Publication date
2015
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
In the context of track fitting problems by a Kalman filter, the appropriate functional forms of the elements of the random process noise matrix are derived for tracking through thick layers of dense materials and magnetic field. This work complements the form of the process noise matrix obtained by Mankel[1].
rate research
Read More
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 finding and fitting particle tracks 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 and SIMT architectures that are now prevalent in high-performance hardware. Previously we observed significant parallel speedups, with physics performance comparable to CMS standard tracking, on Intel Xeon, Intel Xeon Phi, and (to a limited extent) NVIDIA GPUs. While early tests were based on artificial events occurring inside an idealized barrel detector, we showed subsequently that our mkFit software builds tracks successfully from complex simulated events (including detector pileup) occurring inside a geometrically accurate representation of the CMS-2017 tracker. Here, we report on advances in both the computational and physics performance of mkFit, as well as progress toward integration with CMS production software. Recently we have improved the overall efficiency of the algorithm by preserving short track candidates at a relatively early stage rather than attempting to extend them over many layers. Moreover, mkFit formerly produced an excess of duplicate tracks; these are now explicitly removed in an additional processing step. We demonstrate that with these enhancements, mkFit becomes a suitable choice for the first iteration of CMS tracking, and eventually for later iterations as well. We plan to test this capability in the CMS High Level Trigger during Run 3 of the LHC, with an ultimate goal of using it in both the CMS HLT and offline reconstruction for the HL-LHC CMS tracker.
ARIADNE is a nuclear-magnetic-resonance-based experiment that will search for novel axion-induced spin-dependent interactions between an unpolarized source mass rotor and a nearby sample of spin-polarized $^3$He gas. To detect feeble axion signals at the sub-atto-Tesla level, the experiment relies on low magnetic background and noise. We measure and characterize the magnetic field background from a prototype tungsten rotor. We show that the requirement is met with our current level of tungsten purity and demagnetization process. We further show that the noise is dominantly caused by a few discrete dipoles, likely due to a few impurities trapped inside the rotor during manufacturing. This is done via a numerical optimization pipeline which fits for the locations and magnetic moments of each dipole. We find that under the current demagnetization, the magnetic moment of trapped impurities is bounded at $10^{-9} mathrm{A}mathrm{m}^2$.
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 results of tests of 1 vacuum phototriodes in a magnetic field up to 4.5 T are presented. It was found that output amplitude decreases by about 6 % per tesla in the magnetic field range from 2.0 to 4.0 T. For devices with an anode mesh pitch of 16$mu$m, the output amplitude at 4.0 T is 30 % lower than that at zero field.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
