Alignment of the CLAS12 central hybrid tracker with a Kalman Filter
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Alignment, Detector, Kalman filter
Several factors can contribute to the difficulty of aligning the sensors of tracking detectors, including a large number of modules, multiple types of detector technologies, and non-linear strip patterns on the sensors. The latter two of these three factors apply to the CLAS12 CVT, which is a hybrid detector consisting of planar silicon sensors with non-parallel strips, and cylindrical micromegas sensors with longitudinal and arc-shaped strips located within a 5 T superconducting solenoid. To align this detector, we used the Kalman Alignment Algorithm, which accounts for correlations between the alignment parameters without requiring the time-consuming inversion of large matrices. This is the first time that this algorithm has been adapted for use with hybrid technologies, non-parallel strips, and curved sensors. We present the results for the first alignment of the CLAS12 CVT using straight tracks from cosmic rays and from a target with the magnetic field turned off. After running this procedure, we achieved alignment at the level of 10μm, and the widths of the residual spectra were greatly reduced. These results attest to the flexibility of this algorithm and its applicability to future use in the CLAS12 CVT and other hybrid or curved trackers, such as those proposed for the future Electron-Ion Collider.
Paul, S., Peck, A., Arratia, M., Gotra, Y., Ziegler, V., De Vita, R., Bossù, F., Defurne, M., Atac, H., Gayoso, C., Baashen, L., Baltzell, N., Barion, L., Bashkanov, M., Battaglieri, M., Bedlinskiy, I., Benkel, B., Benmokhtar, F., & Bianconi, A. (2023). Alignment of the CLAS12 central hybrid tracker with a Kalman Filter. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1049. https://doi.org/10.1016/j.nima.2023.168032