Optimizing momentum resolution with a new fitting method for silicon-strip detectors

TL;DR

This paper introduces a new fitting method for silicon-strip detector track momentum reconstruction, significantly improving resolution by using realistic hit distributions and maximum likelihood techniques, especially as the number of layers increases.

Contribution

The paper presents a novel fitting approach that enhances momentum resolution in silicon-strip detectors by incorporating heteroscedasticity and maximum likelihood methods, outperforming standard fits.

Findings

Momentum resolution improves linearly with number of layers using new methods.

Standard fits' resolution grows as the square root of layers, while new methods grow linearly.

Significant resolution gains are achieved with increased layers and realistic hit modeling.

Abstract

A new fitting method is explored for momentum reconstruction of tracks in a constant magnetic field for a silicon-strip tracker. Substantial increases of momentum resolution respect to standard fit is obtained. The key point is the use of a realistic probability distribution for each hit (heteroscedasticity). Two different methods are used for the fits, the first method introduces an effective variance for each hit, the second method implements the maximum likelihood search. The tracker model is similar to the PAMELA tracker. Each side, of the two sided of the PAMELA detectors, is simulated as momentum reconstruction device. One of the two is similar to silicon micro-strip detectors of large use in running experiments. Two different position reconstructions are used for the standard fits, the $\eta$-algorithm (the best one) and the two-strip center of gravity. The gain obtained in momentum resolution is measured as the virtual magnetic field and the virtual signal-to-noise ratio required by the two standard fits to reach an overlap with the best of two new methods. For the best side, the virtual magnetic field must be increased 1.5 times respect to the real field to reach the overlap and 1.8 for the other. For the high noise side, the increases must be 1.8 and 2.0. The signal-to-noise ratio has similar increases but only for the $\eta$-algorithm. The signal-to-noise ratio has no effect on the fits with the center of gravity. Very important results are obtained if the number N of detecting layers is increased, our methods provide a momentum resolution growing linearly with N, much higher than standard fits that grow as the $\sqrt{N}$.