Abstract
One of the key requirements for Higgs physics at the International Linear Collider ILC is excellent track reconstruction with very good momentum and impact parameter resolution. ILD is one of the two detector concepts at the ILC. Its central tracking system comprises of an outer Si-tracker, a highly granular TPC, an intermediate silicon tracker and a pixel vertex detector, and it is complemented by silicon tracking disks in the forward direction. Large hit densities from beam induced coherent electron-positron pairs at the ILC pose an additional challenge to the pattern recognition algorithms. We present the recently developed new ILD tracking software, the pattern recognition algorithms that are using clustering techniques, Cellular Automatons and Kalman filter based track extrapolation. The performance of the ILD tracking system is evaluated using a detailed simulation including dead material, gaps and imperfections.
Highlights
We present the recently developed new ILD tracking software, the pattern recognition algorithms that are using clustering techniques, Cellular Automatons and Kalman filter based track extrapolation
Higgs physics precision measurements are among the highlights of the physics program at the International Linear Collider ILC [1] and pose stringent requirements on the detector performance
In this paper we describe the ILD tracking software that has recently been developed in order to study the performance of the ILD detector [2] - one of two detector concepts planned for the ILC
Summary
Higgs physics precision measurements are among the highlights of the physics program at the International Linear Collider ILC [1] and pose stringent requirements on the detector performance. 3. Pattern recognition algorithms Finding and reconstructing charged particle tracks in ILD is split up into standalone track finding in the TPC, in the VTX and SIT and in the FTD, followed by a final process of merging compatible track segments. The Cellular Automaton, originally developed for modeling biological systems, is used for pattern recognition by identifying short track segments in consecutive layers with cells and applying a quality criterion, consistent with charged particle tracks, as a cell state. In this step the tracks are merged based on consistency of their track states, transformed to position and momentum at the IP. After a final refit with the Kalman filter the tracks are written to disk, preserving the pointers to the original tracks segments
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