Speaker
Lara Calic
(Lund University)
Description
In high energy physics (HEP), accurately reconstructing charged particle
trajectories is a challenging task. The classical Kalman filter estimates track
parameters with uncertainties through prediction, filter, and smoother steps.
Precisely aligning detector components increases sensitivity to new physics and
improves particle identification efficiency. The ATLAS alignment solver requires
input information from the Kalman Filter. This project aims to implement a
Kalman Filter-based alignment strategy for the larger structures that will group
individual sensors in the ATLAS tracking framework to align the Inner Tracking
Detector (ITk).
Primary author
Lara Calic
(Lund University)
