Trackers at the future high-luminosity LHC, designed to have triggering capability, will feature layers of stacked modules with a small stack separation. This will allow the reconstruction of track ...stubs or vector hits with position and direction information, but lacking precise curvature information. This opens up new possibilities for track finding, online and offline. Two track finding methods, the Kalman filter and the convergent Hough transform are studied in this context. Results from a simplified fast simulation are presented. It is shown that the performance of the methods depends to a large extent on the size of the stack separation. We conclude that the detector design and the choice of the track finding algorithm(s) are strongly coupled and should proceed conjointly.
Analysis of beam test data by global optimization methods Frühwirth, R.; Bergauer, T.; Friedl, M. ...
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
12/2013, Letnik:
732
Journal Article
Recenzirano
Successful track reconstruction in a silicon tracking device depends on the quality of the alignment, on the knowledge of the sensor resolution, and on the knowledge of the amount of material ...traversed by the particles. We describe algorithms for the concurrent estimation of alignment parameters, sensor resolutions and material thickness in the context of a beam test setup. They are based on a global optimization approach and are designed to work both with and without prior information from a reference telescope. We present results from simulated and real beam test data.
•We describe a method to estimate the resolution of detectors in a beam test.•The method is based on the global optimization of a suitable objective function.•The width of individual components of the error distribution can be estimated.•Under suitable conditions, sensor thickness (and other material) can be estimated as well.
A vertex reconstruction algorithm that is based on the Gaussian-sum filter (GSF) was developed and implemented in the framework of the CMS reconstruction program. While linear least-square estimators ...are optimal in case all observation errors are Gaussian distributed, the GSF offers a better treatment of non-Gaussian distributions of track parameter errors when these are modeled by Gaussian mixtures. The algorithm has been verified and evaluated with simulated data. The results are compared to the Kalman filter and to an adaptive vertex estimator.
Recently, a new Riemann track fit which operates on translated and scaled measurements has been proposed. This study shows that the new Riemann fit is virtually as precise as popular approaches such ...as the Kalman filter or an iterative non-linear track fitting procedure, and significantly more precise than other, non-iterative circular track fitting approaches over a large range of measurement uncertainties. The fit is then extended in two directions: first, the measurements are allowed to lie on plane sensors of arbitrary orientation; second, the full error propagation from the measurements to the estimated circle parameters is computed. The covariance matrix of the estimated track parameters can therefore be computed without recourse to asymptotic properties, and is consequently valid for any number of observation. It does, however, assume normally distributed measurement errors. The calculations are validated on a simulated track sample and show excellent agreement with the theoretical expectations.
Finding and fitting circles from a set of points is a frequent problem in the data analysis of high-energy physics experiments. In a tracker immersed in a homogeneous magnetic field, tracks are close ...to perfect circles if projected to the bending plane. In a ring-imaging Cherenkov (RICH) detector, circles of photons around the crossing point of charged particles have to be found and their radii estimated. In both cases, non-negligible background may be present that tends to complicate the pattern recognition and to bias the circle fit. In this contribution we present a robust circle fit based on a modified Riemann fit that removes or significantly reduces the effect of background points. As in the standard Riemann fit, the measured points are projected to the Riemann sphere or paraboloid, and a plane is fitted to the projected points. The fit is made robust by replacing the usual least-squares regression by a least median of squares (LMS) regression. Because of the high breakdown point of the LMS estimator, the fit is insensitive to background points. The LMS plane is used to initialize the weights of an M-estimator that refits the plane in order to suppress eventual remaining outliers and to obtain the final circle parameters. The method is demonstrated on three sets of artificial data: points on a circle plus a comparable number of background points; points on two overlapping circles with additional background; and points obtained by the simulation of tracks in a drift chamber with mirror points and additional background. The results show high circle finding efficiency and small contamination of the final fitted circles.
A new Riemann fit for circular tracks Frühwirth, R; Strandlie, A
Journal of physics. Conference series,
10/2016, Letnik:
762, Številka:
1
Journal Article
Recenzirano
Odprti dostop
We present in this contribution a new Riemann track fit which operates on centered and scaled measurements. With these transformations, the fit becomes invariant under translations and similarity ...transforms of the measurements. We show in a simulation study in a generic, cylindrical detector that the modified Riemann fit is more precise than the standard Riemann fit, in particular if the hit resolution is large.
Non-normally distributed data are ubiquitous in many areas of science, including high-energy physics. We present a general formalism for constrained fits, also called data reconciliation, with data ...that are not normally distributed. It is based on Bayesian reasoning and implemented via MCMC sampling. We show how systems of both linear and non-linear constraints can be efficiently treated. We also show how the fit can be made robust against outlying observations. The method is demonstrated on a couple of examples ranging from material flow analysis to the combination of non-normal measurements. Finally, we discuss possible applications in the field of event reconstruction, such as vertex fitting and kinematic fitting with non-normal track errors.
The application of sparse model-based clustering to the problem of primary vertex finding is discussed. The observed z-positions of the charged primary tracks in a bunch crossing are modeled by a ...Gaussian mixture. The mixture parameters are estimated via Markov Chain Monte Carlo (MCMC). Sparsity is achieved by an appropriate prior on the mixture weights. The results are shown and compared to clustering by the expectation-maximization (EM) algorithm.
A z-Vertex Trigger for Belle II Skambraks, S.; Abudinen, F.; Chen, Y. ...
IEEE transactions on nuclear science,
08/2015, Letnik:
62, Številka:
4
Journal Article
Recenzirano
Odprti dostop
The Belle II experiment will go into operation at the upgraded SuperKEKB collider in 2016. SuperKEKB is designed to deliver an instantaneous luminosity L = 8 ×10 35 cm - 2 s - 1 . The experiment will ...therefore have to cope with a much larger machine background than its predecessor Belle, in particular from events outside of the interaction region. We present the concept of a track trigger, based on a neural network approach, that is able to suppress a large fraction of this background by reconstructing the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger uses the hit information from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum ("sectors"), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track. Within the sector, the z-vertex is estimated by a specialized neural network, with the drift times from the CDC as input and a continuous output corresponding to the scaled z-vertex. The neural algorithm will be implemented in programmable hardware. To this end a Virtex 7 FPGA board will be used, which provides at present the most promising solution for a fully parallelized implementation of neural networks or alternative multivariate methods. A high speed interface for external memory will be integrated into the platform, to be able to store the O(10 9 ) parameters required. The contribution presents the results of our feasibility studies and discusses the details of the envisaged hardware solution.
Peelle's Pertinent Puzzle denotes the occurrence of unexpected mean values and variances when experimental data affected by statistical and systematic uncertainties are combined by weighing with an ...experimental covariance matrix. It was shown for an arbitrary number of experimental data points for the same physical quantity that this puzzle is primarily caused by an improper estimate of the experimental covariance matrix. In this contribution, the solution is tested for more than one physical quantity and is applied to a nuclear data problem.
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