English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Kalman Filter is a widely used approach for the linear estimation ofdynamical systems and is frequently employed within nuclear and particlephysics experiments for the reconstruction of charged particle trajectories,known as tracks. Implementations of this formalism often make assumptions onthe linearity of the underlying dynamic system and the Gaussian nature of theprocess noise, which is violated in many track reconstruction applications.This paper introduces an implementation of a Non-Linear Kalman Filter (NLKF)within the ACTS track reconstruction toolkit. The NLKF addresses the issue ofnon-linearity by using a set of representative sample points during its trackstate propagation. In a typical use case, the NLKF outperforms an ExtendedKalman Filter in the accuracy and precision of the track parameter estimatesobtained, with the increase in CPU time below a factor of two. It is thereforea promising approach for use in applications where precise estimation of trackparameters is a key concern.

A Non-Linear Kalman Filter for track parameters estimation in High Energy Physics