Track Fit Hypothesis Testing and Kink Selection using Sequential Correlations

TL;DR

This paper explores how sequential correlations in residuals from trajectory fits can serve as a more powerful goodness-of-fit test, especially for detecting common trajectory errors in charged particle tracking.

Contribution

It introduces a method utilizing sequential correlations for hypothesis testing and kink selection in trajectory analysis, enhancing detection of deviations from assumed trajectories.

Findings

Sequential correlations improve goodness-of-fit testing.

Method detects trajectory errors like decays in flight.

Enhanced accuracy in charged particle tracking.

Abstract

Deviations between the form of trajectory assumed in a fit to a set of measurements and the actual form of the trajectory can give rise to sequential correlations in the residuals from the fit. These correlations can provide a more powerful goodness-of-fit test than that based on the minimum chi-square from a least squares fit. The use of this additional information is explored in the context of several common trajectory errors (e.g. decays in flight) encountered in charged particle tracking.