Speaker
Mr
David Primor
(Tel Aviv University, ISRAEL (CERN))
Description
This talk presents new methods to address the problem of muon track identification
in the monitored drift tube chambers (MDT) of the ATLAS Muon Spectrometer. Pattern
recognition techniques, employed by the current reconstruction software suffer when
exposed to the high background rates expected at the LHC. We propose new techniques,
exploiting existing knowledge of the detector performance in the presence of
background, in order to improve tracking efficiency. The efficiency of the MDT tubes
is very high. However, in a high background environment, there are two possible
cases, for which a signal might not be registered when a particle has passed through
an active tube: the existence of a previous background hit, giving rise to electronic
dead time, and insufficient ionization in cases where the track crosses (very close
to) the tube wall. Taking this into account, we derive a mathematical expression for
the effective muon hit probability. We then model the track identification problem as
a two-hypothesis problem, and base the decision on a generalized likelihood ratio
test (GLRT). Since the effective muon hit probability is very high, we can choose a
higher likelihood threshold, reducing the probability of finding false tracks without
reducing the probability of track detection. In order to solve the track detection
problem, we employ a novel modification of the Hough transform, with several values
in each cell, different for each potential case. These values are then used for
calculating the muon track likelihood. Examining data from beam tests with realistic
background levels, we show that the use of this technique results in a significant
improvement of the muon track detection performance of the MDT.
Primary authors
Mr
David Primor
(Tel Aviv University, ISRAEL (CERN))
Markus Elsing
(CERN)
Co-authors
Prof.
Giora Mikenberg
(Weizmann institute, ISRAEL (CERN))
Prof.
Hagit Messer
(Tel Aviv University, ISRAEL)
