Improvements of Track Fitting with Well Tuned Probability Distributions for Silicon Strip Detectors

Abstract

The construction of a well tuned probability distributions is illustrated in synthetic way, these probability distributions produce faithful realizations of the impact point distributions for particles in silicon strip detector. Their use for track fitting shows a drastic improvements of a factor two, for the low noise case, and a factor three, for the high noise case, respect to the standard approach. The tracks are well reconstructed even in presence of hits with large errors, with a surprising effect of hit discarding. The applications illustrated are simulations of the PAMELA tracker, but other type of trackers can be handled similarly. The probability distributions are calculated for the center of gravity algorithms, and they are very different from gaussian probabilities. These differences are crucial to accurately reconstruct tracks with high error hits and to produce the effective discarding of the too noisy hits (outliers). The similarity of our distributions with the Cauchy distribution forced us to abandon the standard deviation for our comparisons and instead use the full width at half maximum. A set of mathematical approaches must be developed for these applications, some of them are standard in wide sense, even if very complex. One is essential and, in its absence, all the others are useless. Therefore, in this paper, we report the details of this critical approach. It extracts physical properties of the detectors, and allows the insertion of the functional dependence from the impact point in the probability distributions. Other papers will be dedicated to the remaining parts.

30 pages, 19 figures with captions

References in corpus (5)

• Observation of an anomalous positron abundance in the cosmic radiation
• PAMELA - A Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics
• Commissioning and Performance of the CMS Silicon Strip Tracker with Cosmic Ray Muons
• Properties of the Center of Gravity as an Algorithm for Position Measurements: Two-Dimensional Geometry
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Cited by in corpus (6)

• The Cramer-Rao inequality to go beyond the $\sqrt{\mathbf{N}}$-limit of the standard least-squares method in track fitting
• Optimizing momentum resolution with a new fitting method for silicon-strip detectors
• Probability Distributions of Positioning Errors for Some Forms of Center-of-Gravity Algorithms
• Proofs of non-optimality of the standard least-squares method for track reconstructions
• Beyond the $\sqrt{\mathrm{N}}$ limit of the least squares resolution and the lucky model
• Positioning Error Probability for Some Forms of Center-of-Gravity Algorithms Calculated with the Cumulative Distributions. Part I