Inaccuracy of coordinate determined by several detectors' signals

Samedov, Victor V.

doi:10.1088/1748-0221/7/06/c06002

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…All the other parameters of the simulation are kept fixed, thus we can follow the noise effect in each track and the modifications of distributions of the track parameters. The noise values explored are 4, 6, 8, 10, 12, 14 ADC counts ( or better for an average signal to noise ratio (S/N) of 40, 27, 20,16,13,11). The FWHM of the distributions for γ and β increases with increasing the noise, as expected, but the ratios of the FWHM for the two approaches (MIN-LOG and least squares) are almost constant with a slight degradation at increasing noise.…”

Section: Different Noise Amplitudesmentioning

confidence: 62%

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Improvements of track fitting with well tuned probability distributions for silicon strip detectors

Landi

2014

J. Inst.

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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.

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Section: Different Noise Amplitudesmentioning

confidence: 62%

“…[1] (σ x ∝ τ/(S/N)) tends to be very optimistic in the two sensor sides discussed here, confirming the general suggestion of ref. [20]. In fact, the large majority of σ e f f -values are well above the line at 0.03 in fig.…”

Section: Confronts and Generalizationsmentioning

confidence: 64%

Improvements of track fitting with well tuned probability distributions for silicon strip detectors

Landi

2014

J. Inst.

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Probability Distributions for Track Fitting and the Advanced Lucky Model

Landi¹,

Landi²

2022

Physics

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Probability distributions for the center of gravity are fundamental tools for track fitting. The center of gravity is a widespread algorithm for position reconstruction in tracker detectors for particle physics. Its standard use is always accompanied by an easy guess (Gaussian) for the probability distribution of the positioning errors. This incorrect assumption degrades the results of the fit. The explicit error forms evident Cauchy–Agnesi tails that render the use of variance minimizations problematic. Therefore, it is important to report probability distributions for some combinations of random variables essential for track fitting: x=ξ/(ξ+μ), y=(ξ−μ)/[2(ξ+μ)], w=ξ/μ, x=θ(x3−x1)(−x3)/(x3+x2)+θ(x1−x3)x1/(x1+x2) and x=(x1−x3)/(x1+x2+x3). The first two are partial forms of the two strip center of gravity. The fourth is the complete two strip center of gravity, and the fifth is a partial form of the three strip center of gravity. For the complexity of the forth equation, only approximate expressions of the probability are allowed. Analytical expressions are calculated assuming ξ, μ, x1, x2 and x3 independent Gaussian random variables. The analytical form of the probability for the two strip center of gravity allows one to construct an approximate proof for the lucky model of our previous paper. This proof also suggests how to complete the lucky model by its absence of a scaling constant, relevant to combine different detector types. This advanced lucky model (the super-lucky model) can be directly used in trackers composed of non-identical detectors. The construction of the super-lucky model is very simple. Simulations with this upgraded tool also show resolution improvements for a combination of two types of very different detectors, near to the resolutions of the schematic model.

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Inaccuracy of coordinate determined by several detectors' signals

Cited by 2 publications

References 14 publications

Improvements of track fitting with well tuned probability distributions for silicon strip detectors

Improvements of track fitting with well tuned probability distributions for silicon strip detectors

Probability Distributions for Track Fitting and the Advanced Lucky Model

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