Efficient Analysis of Test-beam Data with the Corryvreckan Framework

Abstract

Stringent requirements are posed on the next generations of vertex and tracking detectors for highenergy physics experiments to reach the foreseen physics goals. A large variety of silicon pixel sensors targeting the specific needs of each use case are developed and tested both in laboratory and test-beam measurement campaigns. Corryvreckan is a flexible, fast and lightweight test-beam data reconstruction and analysis framework based on a modular concept of the reconstruction chain. It is designed to fulfil the requirements for offline event building in complex data-taking environments combining detectors with different readout schemes. Its modular architecture separates the framework core from the implementation of reconstruction, analysis and detector specific algorithms. In this paper, a brief overview of the software framework and the reconstruction and analysis chain is provided. This is complemented by an example analysis of a data set using the offline event building capabilities of the framework and an improved event building scheme allowing for a more efficient usage of test-beam data exploiting the pivot pixel information of the Mimosa26 sensors.

Silicon pixel sensor R&D for the CLIC tracking detector

“…In addition, the flexible and modular test-beam data reconstruction framework Corryvreckan [15] has been developed. It fulfils the requirements of a complex offline event building in data taking environments combining detector subsystems with different readout architectures.…”

R&D for the CLIC vertex and tracking detectors

“…Track building, clustering, and DUT-track association algorithms can all use timing information to decrease combinatorics and recover clusters accurately in high-rate environments. A v1.0 release is available, as detailed on the Corryvreckan website [20], and the v1.0 users manual can be found as a published note [21].…”