2011 QCD and High Energy Interactions - Rencontres de Moriond ...

Figure 1: Dimuon invariant mass spectra. Full squares are opposite-sign dimuons, while the empty circle shows a
unique like-sign dimuon candidate. The histogram shows the corresponding distribution measured in pp collisions
at 7 TeV within 60-120 GeV/c 2 , scaled to the 39 PbPb candidates.
4 Correction factors
In order to estimate the Z corrected spectrum, one can typically divide the measured one by the
acceptance x efficiency, the latter being the product of trigger, identification and reconstruction
efficiency. The corrections are computed directly from PYTHIA 7 -simulation by generating Z
distribution in pT and y embedded in real data. We estimated separately those various contributions
: the acceptance (78%), the trigger efficiency (94%) , the reconstruction efficiency (72%)
and the identification efficiency (98%). For those efficiencies, we compare different techniques
systematically. The final corrections are based on peak counting from Z signal embedded in
data. This overall method is in the same spirit of the one used for the p+p 8 , in which there was
twice the statistics. The most significant systematic uncertainty is estimated to be 9.8% and is
driven by the tracking efficiency, followed by the uncertainty on dimuon trigger efficiency (4.5%)
and then by the un-subtracted background (4%). The uncertainty associated with the selection
criteria is considered to be equal to the 2.6% loss of events. The MB trigger efficiency as well
as the uncertainties coming from the acceptance correction are around 3%. Other contributions
for systematic uncertainties are estimated to sum to less than 1.5%. By summing in quadrature
those contributions, we obtain a total systematic uncertainties of 13%.
5 Results
We then calculate the yield of Z → µ + µ − per minimum bias event,
dN
dy =
N
α ɛ NMB ∆y
where N = 39 is the number of measured Z candidates counted in a mass window of 60 −
120 GeV/c 2 , NMB = 55 × 10 6 is the number of corresponding minimum bias events, α and ɛ
are the acceptance and the overall efficiency corrections mentioned above, and ∆y = 4.0 is the
rapidity bin width. We find a yield per event of dN/dy(|y| < 2.0) = (33.8 ± 5.5 ± 3.4) × 10 −8 ,
where the first uncertainty is statistical and the second is systematic. Given the small number
of observed Z candidates, we choose to separate the data into three kinematic bins in y, pT ,
and centrality a . The full data sample is divided into the following centrality bins: 30-100%
(most peripheral), 10-30% (intermediate) and 0-10% (most central); these are ordered from the
lowest to highest fractional HF energy deposition. In figure (a) the differential dN/dy yield
a Centrality is defined as the fraction of energy deposited in the HF calorimeter.
(1)