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 are optimized to produce a faithful realizations of the impact point distributions of 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.

Optimizing Momentum Resolution with a New Fitting Method for Silicon-Strip Detectors

“…2.6 are illustrated in ref. [9] as ε-functions. It should be evident the enormous number of different PDFs that can be generated by eq.…”

“…The simulated hits are generated (ref. [9]) with a uniform random distribution on a strip, they are collected in groups of six to produce the tracks. The exact impact position ε of each hit is subtracted from its reconstructed η 2 (x g2 ) position (as defined in refs.…”

“…The exact impact position ε of each hit is subtracted from its reconstructed η 2 (x g2 ) position (as defined in refs. [9,8,7]) and it is added the value of the fiducial track for the corresponding detector layer. In this way each group of six hits defines a track with our geometry and the error distribution of η 2 -algorithm.…”

“…Recent papers [1,2] (to mention in ref. [9]. The limitation to straight tracks was mainly due to the complexity of the method we applied for the first time.…”

“…The confidence gained in ref. [9] with straight tracks allow us to deal with more complex and appealing tasks, i.e. the reconstruction of tracks in a magnetic field and the measure of their momenta.…”