Measurement of the Z boson cross-section in - Harvard University ...

Chapter 4: Data Collection and Event Reconstruction 138
TileMuId was originally designed to run as a Level-2 trigger algorithm, but has
been adapted to run offline [48]. As its name implies, it searches for muon-like en-
ergy deposit signatures in the Tile hadronic calorimeter. It starts by looking at the
outermost radial layer of the TileCal. If it finds an energy deposit compatible with
the passage of a minimum ionizing particle (MIP), it continues the search in the mid-
dle layer along a direction pointing to the interaction region. If this search yields
MIP-like energy deposit, it moves to the inner layer. A MIP-like energy deposit in
this segment confirms a muon, the direction of which is estimated using the average
coordinates of the cells in the three layers.
CaloTrkMuId
CaloTrkMuId provides two algorithms: one which returns a likelihood value, and
the other a tag. The former uses a likelihood ratio function incorporating longitudinal
energy deposits in all layers of the EM and hadronic calorimeters. The tagging
algorithm starts with an inner detector track, propagates it through the calorimetry,
and computes the energy deposit in a cone around the track. If the energy deposit is
consistent with a MIP, the track is tagged as a muon.
4.2.4 Efficiencies in the muon system
Since we are interested in muons in the final state for our analysis, it behooves
us to study the hardware and software efficiencies associated with the muon system.
The most important hardware efficiencies are drift tube efficiency for detecting hits
from single muons and trigger chamber efficiency for triggering on muons passing the
preset thresholds. Crucial software efficiencies include reconstruction efficiency for
muon tracks and efficiencies of the Level-2 and Level-3 trigger algorithms. In this