Performance evaluation of energy reconstruction methods for the ATLAS hadronic calorimeter using collision data. (2nd March 2023)

• Record Type: Journal Article
• Authors: Marin, Juan Lieber
• Is Part Of: International journal of innovative computing and applications. Volume 14:Number 1/2(2023)
• Page Start: 3
• Page End: 11
• Publication Date: 2023-03-02
• Subjects: high-energy physics -- signal estimation -- optimal filtering -- signal pile-up; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Particle accelerators are machines that collide particle beams next to the speed of light. The large hadron collider, the largest and most powerful beam collider, operates at a centre-of-mass energy of 13 TeV with a 40 MHz collision rate. In the ATLAS experiment, optimal filter techniques can be applied on energy estimation even in the occurrence of noise and signal overlapping. In this context, the present work compares the performance of the energy estimation in the main hadronic ATLAS calorimeter, for two methods: the baseline algorithm optimal filter (OF2) and constrained optimal filter (COF). The work proposes an adaptive estimator of the energy signal pedestal in the ATLAS experiment at the LHC. For performance comparisons, the statistics from the energy estimation distributions are employed. The results show that the COF method presents a better performance than the OF2 method in terms of estimation error. The proposed signal baseline estimator properly improves the COF efficiency.