Data-driven Alignment of the Tracker for the Mu2e Experiment

Abstract

An alignment of the Mu2e Tracker is investigated using the Mu2e Offline Software and Millepede-II. Simulated misalignments of Tracker planes were inserted between Monte-Carlo event generation and before reconstruction using randomly generated (x,y,z)-translations and rotations of the rigid-body planes in the detector model. Cosmic ray muons, with no magnetic field present in the Detector Solenoid, will follow a straight-line trajectory. A maximum-likelihood fit is used to reconstruct a track and determine its parameters. Tracks traversing at least four planes, with ten registered straw hits, and a maximum time residual of 20 ns are selected to be used in an alignment sample. The cuts were chosen to discard tracks with low information content, such as those with high levels of multiple scattering or those with low numbers of measurements contributing to a single track fit. Millepede-II produces the resulting alignment constants according to a simultaneous least-squares fit of the alignment parameters and the track parameters for all input tracks. The improved alignment constants are used to repeat the process and recover an increased number of suitable tracks, further improving the estimate of the alignment constants in subsequent iterations. A performance study of the process was conducted on a misaligned Tracker in the case of translation-only, and translation plus rotational misalignments. In both cases, the correct alignment constants were recovered to within statistical error after three global fit iterations. Additionally, a continuous integration system was developed for the Mu2e/Offline GitHub repository, introducing a convenient automated testing process for all proposed changes ("pull requests"). The tests developed included a full build of the software, followed by tests of the validation routines, geometry overlap checking, and multi-threading mode. Additionally, code quality checks and low-statistics physics comparison checks are also available.

Date of Award	1 Aug 2021
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Mark Lancaster (Supervisor) & Marco Gersabeck (Supervisor)