Speaker
I. Belikov
(CERN)
Description
One of the main features of the ALICE detector at LHC is the capability to identify particles in a very broad
momentum range from 0.1 GeV/c up to 10 GeV/c. This can be achieved only by combining, within a common
setup, several detecting systems that are efficient in some narrower and complementary momentum sub-
ranges. The situation is further complicated by the amount of data to be processed (about 10^7 events with
about 10^4 tracks in each). Thus, the particle identification (PID) procedure should satisfy the following
requirements:
1) It should be as much as possible automatic.
2) It should be able to combine PID signals of different nature (e.g. dE/dx and TOF measurements).
3) When several detectors contribute to the PID, the procedure must profit from this situation by providing an
improved PID.
4) When only some detectors identify a particle, the signals from the other detectors must not affect the
combined PID.
5) It should take into account the fact that the PID depends, due to different track selection, on the kind of
analysis.
In this report we will demonstrate how combining the single detector PID signals in the Bayesian way
satisfies these requirements. We will also discuss how one can obtain the needed probability distribution
functions and a priory probability from the experimental data. The approach has been implemented within the
ALICE offline framework, and the algorithm efficiency and PID contamination have been estimated using the
ALICE simulation.
