EPJ Techn Instrum (2019) 6: 1
https://doi.org/10.1140/epjti/s40485-019-0050-z

Research article

Selection and processing of calibration samples to measure the particle identification performance of the LHCb experiment in Run 2

¹ Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France
² LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France
³ Université Pierre et Marie Curie, Université Paris Diderot, CNRS/IN2P3, Paris, France
⁴ Istituto Nazionale di Fisica Nucleare, Sezione di Bologna, Bologna, Italy
⁵ Istituto Nazionale di Fisica Nucleare, Sezione di Firenze, Florence, Italy
⁶ Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Frascati, Frascati, Italy
⁷ European Organization for Nuclear Research (CERN), Meyrin, Switzerland
⁸ Nikhef, Amsterdam, Netherlands
⁹ University of Cambridge, Cambridge, United Kingdom
¹⁰ Department of Physics, University of Warwick, Coventry, United Kingdom
¹¹ Imperial College London, London, United Kingdom
¹² University of Oxford, Oxford, United Kingdom

^* e-mail: marianna.fontana@cern.ch

Received: 24 August 2018
Accepted: 1 February 2019
Published online: 28 February 2019

Abstract

Since 2015, with the restart of the LHC for its second run of data taking, the LHCb experiment has been empowered with a dedicated computing model to select and analyse calibration samples to measure the performance of the particle identification (PID) detectors and algorithms. The novel technique was developed within the framework of the innovative trigger model of the LHCb experiment, which relies on online event reconstruction for most of the datasets, reserving offline reconstruction to special physics cases. The strategy to select and process the calibration samples, which includes a dedicated data-processing scheme combining online and offline reconstruction, is discussed. The use of the calibration samples to measure the detector PID performance, and the efficiency of PID requirements across a large range of decay channels, is described. Applications of the calibration samples in data-quality monitoring and validation procedures are also detailed.