Search for Narrow Resonance Decaying to Muon Pairs in 2.3 fb -1 Chris Hays 1, Ashutosh Kotwal 2, Ye Li 3, Oliver Stelzer-Chilton 1 1 Oxford University

Search for Narrow Search for Narrow Resonance Decaying to Resonance Decaying to Muon Pairs in 2.3 fbMuon Pairs in 2.3 fb-1-1

Chris Hays1, Ashutosh Kotwal2, Ye Li3, Oliver Stelzer-Chilton1

1 Oxford University2 Duke University

3 University of Wisconsin-Madison

APS April Meeting, St. Louis - 14 April 2008 2Ye Li

MotivationMotivation Theory Driven

Standard Model successful but incompleteStrong discovery potential in dimuon

channelNew models predict narrow neutral

resonance, e.g. • additional U(1) symmetry: Z’ • extra space-time dimension: Randall-Sundrum

graviton

The present analysis focuses on Z’ → channel


MotivationMotivation Experiment Driven

Last CDF and DØ dimuon resonance searches performed with integrated luminosity 200 pb-1

→ Our search: L ≈ 2.3 fb-1 of CDF Run II dataSignificant increase of sensitivity to

dielectron and diphoton channelsExcellent tracking resolution (Central Outer

Tracker, Drift Chamber)


MethodologyMethodology Model Drell-Yan background and

signal resonance with PYTHIA + fast simulation for W mass measurement

Use Z region for normalization Remove uncertainty on luminosityEasy accounting

Compare CDF fast simulation (FastSim) to full Geant simulation (CDFSim) and data for acceptance and efficiency study


MethodologyMethodology Inverse Mass (1/m) Scan

Excellent angular resolution → negligibleTrack curvature (~1/PT) resolution constant

for high PT → constant 1/m resonance width

1/m ≈ 0.16/TeV


MethodologyMethodology

Fit for NZ’ (number of Z’ candidate)Calculate Binned Poisson likelihood

L(NZ’;MZ’) for region 1/m < 10/TeVConstruct the narrowest possible interval in

NZ’ at 95% C.L.

Scan 1/m spectrum for Z’ resonanceUse Monte-Carlo Pseudo-experiments to

determine the significance


Dataset & SelectionDataset & Selection Dataset from high PT muon trigger The dimuon event selection

The muon identification requirement • EM energy cut

tuned for high efficiency of Z

• High identification efficiency ~ 95%


EfficiencyEfficiency Mass dependence

Assume track and muon-hit cuts independent of mass

Momentum dependence Only consider P dependence, due to the

normalization of background expectationAssume no P dependence of trigger

efficiency for PT > 30 GeV

Separate the sample into signal and normalization (Z-pole) regions


EfficiencyEfficiency EM and Hadronic Cut Efficiency

Signal region: constant ratio between FastSim and CDFSim (no inefficiency of Had cut for FastSim → 2% const. offset)

Z-pole region: ratio between FastSim and Data drops at low P (due to incomplete modeling)

• insufficient data for signal region

• compute uncertainty from data-simulation difference


AcceptanceAcceptance Implement detector Geometric

information on FastSimMap angular distribution of CDFSim to

FastSim; W → data and FastSim agree reasonably

Muon for 0.6 < || < 1.0 (CMX)

Muon for || < 0.6 (CMUP)


AcceptanceAcceptance Mass-dependent Acceptance

Larger mass → Lower boost → More central events → Larger acceptance

Constant Ratio between FastSim and CDFSim

→ Validate acceptance calculation from FastSim

Uncertainty from the small slope of the ratio


BackgroundBackground Drell-Yan */Z →

PYTHIA + FastSim

WW and tt-barCDF Simulation (PYTHIA + CDFSim)

Cosmic Rays Identified Cosmic-ray samples

QCD Jets and Decays-in-FlightData


Drell-YanDrell-Yan Dominant source for background Mass spectrum affected by higher-

order correctionsCalculate up to next-to-next-to leading order

(NNLO) correction → k-factorDifferent Calculations give different k-

factorsAverage k-factor; Difference as uncertainty


Drell-YanDrell-Yan

• The Stirling and Hamburg, van Neervan and Matsuura (HNM) calculations of the k-factor

• About 6% difference ( ~3% systematic uncertainty)


WW tt & Cosmic RayWW tt & Cosmic Ray WW, tt → + missing ET : Simulate

PYTHIA samples using CDFSim to compute background

Cosmic Ray : Use timing information of Drift Chamber to estimateBackground fraction

~ 1.2 X 10-6


QCD & DIFQCD & DIF Assumtions

QCD jets faking muons: same-sign dimuon (SS) and Opposite-sign

dimuon background (OS) distribution have similar shape, i.e. constant OS/SS ratio

Decay-in-flight muons:flat distribution of DIF muons at small curvature (high PT → small 1/m)

Track 2 cut reduces DIF events Same-sign samples contains both

jet fakes and decays-in-flight


QCD & DIFQCD & DIF

• SS dimuon obtained from jet triggered data • SS dimuon obtained from signal dataset, with 2 cut removed• SS dimuon obtained from signal dataset, with 2 cut on


Other IssuesOther Issues Momentum Scale & Resolution

Momentum scale measurement done by fitting Z peak using templates made with FastSim

Resolution tuned on the width of the Z peak

Systematic UncertaintiesDominant uncertainties:

• Parton distribution functions• Mass-dependent of the NNLO k-factor

Other uncertainties:• Arise from PT-dependent acceptance and efficiency

• Affect the signal and background prediction at high mass


Signal ScanSignal Scan Pseudo-experiment: Standard

Model process


Signal ScanSignal Scan Pseudo-experiment: MZ’ = 250 GeV


Signal ScanSignal Scan Expected limits on NZ’ from 1000

pseudo-experiments on 50 Z’ masses

Data: to be implemented …


SummarySummary Use 1/m distribution for constant

resolution Fitter and Simulation in place to study

signal acceptance and identification efficiency

Analysis on different background fractions

Systematic uncertainties to be determined

Signal scan performed on pseudo-experiments

Documents

Search for Narrow Resonance Decaying to Muon Pairs in 2.3 fb -1 Chris Hays 1, Ashutosh Kotwal 2, Ye Li 3, Oliver Stelzer-Chilton 1 1 Oxford University