Unifying positioning corrections and random number generations in silicon micro-strip trackers

TL;DR

This paper presents a unified algorithm that combines positioning corrections and random number generation for silicon micro-strip trackers, improving simulation accuracy and efficiency.

Contribution

It introduces an extension of a center of gravity correction algorithm to generate various random distributions directly linked to detector data.

Findings

Successful generation of sample distributions verified by Kolmogorov-Smirnov test

Unified method simplifies simulation processes for silicon strip detectors

Enhanced noise modeling capabilities in simulations

Abstract

The optimizations of the track fittings require complex simulations of silicon strip detectors to be compliant with the fundamental properties of the hit heteroscedasticity. Many different generations of random numbers must be available with distributions as similar as possible to the test-beam data. A fast way to solve this problem is an extension of an algorithm of frequent use for the center of gravity positioning corrections. Such extension gives a single method to generate the required types of random numbers. Actually, the starting algorithm is a random number generator, useful in a reverse mode: from non uniform sets of data to uniform ones. The inversion of this operation produces random numbers of given distributions. Many methods have been developed to generate random numbers, but none of those methods is directly connected with this positioning corrections. Hence, the adaptation of the correction algorithm to operate in both mode is illustrated. A sample distribution is generated and its consistency is verified with the Kolmogorov-Smirnov test. As final step, the elimination of the noise is explored, in fact, simulations require noiseless distributions to be modified by given noise models.