In July 2018 an optimization run for the proposed charm cross section measurement for SHiP was performed at the CERN SPS. A heavy, moving target instrumented with nuclear emulsion films followed by a silicon pixel tracker was installed in front of the Goliath magnet at the H4 proton beam-line. Behind the magnet, scintillating-fibre, drift-tube and RPC detectors were placed. The purpose of this run was to validate the measurement's feasibility, to develop the required analysis tools and fine-tune the detector layout. In this paper, we present the track reconstruction in the pixel tracker and the track matching with the moving emulsion detector. The pixel detector performed as expected and it is shown that, after proper alignment, a vertex matching rate of 87% is achieved.

Track reconstruction and matching between emulsion and silicon pixel detectors for the SHiP-charm experiment

W. M. Bonivento;L. Congedo;G. D'Appollonio;G. De Lellis;M. de Magistris;D. Domenici;G. Galati;D. Grenier;G. Iaselli;
2022-01-01

Abstract

In July 2018 an optimization run for the proposed charm cross section measurement for SHiP was performed at the CERN SPS. A heavy, moving target instrumented with nuclear emulsion films followed by a silicon pixel tracker was installed in front of the Goliath magnet at the H4 proton beam-line. Behind the magnet, scintillating-fibre, drift-tube and RPC detectors were placed. The purpose of this run was to validate the measurement's feasibility, to develop the required analysis tools and fine-tune the detector layout. In this paper, we present the track reconstruction in the pixel tracker and the track matching with the moving emulsion detector. The pixel detector performed as expected and it is shown that, after proper alignment, a vertex matching rate of 87% is achieved.
2022
Particle tracking detectors (Solid-state detectors); Pattern recognition; Detector alignment and calibration methods
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/369883
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