TL;DR
This paper introduces a Bayesian method for particle identification in the ALICE experiment, effectively combining detector data to improve identification accuracy and signal significance in high-energy collision analyses.
Contribution
The paper presents a novel Bayesian approach to particle identification that enhances efficiency and background suppression compared to traditional methods in the ALICE experiment.
Findings
Bayesian PID yields consistent results with standard methods.
Higher signal-to-background ratio for D0 meson measurement.
Effective in identifying particles in high-energy collision data.
Abstract
We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian PID approach for charged pions, kaons and protons in the central barrel of ALICE is studied. PID is performed via measurements of specific energy loss () and time-of-flight. PID efficiencies and misidentification probabilities are extracted and compared with Monte Carlo simulations using high-purity samples of identified particles in the decay channels , , and in p-Pb collisions at TeV. In order to thoroughly assess the validity of the…
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