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Particle Physics Kalman Filter Implementation

2026 · 2026 Competition

Category: Research

Project Overview

One Liner: Implementing a Kalman Filter to reconstruct the trajectories of particles through a magnetic gradient

Abstract

The Kalman filter is a filter used to estimate the values of physical measurements provided time-discrete, noisy data. This technique is performed in steps, weighting the data measured at these discrete times with a prediction function used to estimate these measurements and thus accounting for noise in the measured data. The Kalman filter will be used to reconstruct the paths of particles through a magnetic field gradient induced by a dipole magnet. This reconstruction is part of an experiment designed to measure hadron scattering and production cross sections.

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Team Members

Abby Hatcher
Abby Hatcher
abby.hatcher@drexel.edu

Team Lead

Abby Hatcher
Abby Hatcher
abby.hatcher@drexel.edu

Advisors

Mark Boady
Mark Boady
mwb33@drexel.edu

Stakeholders

Michelle Dolinksi
Michelle Dolinksi
mjd396@drexel.edu