1. Alessandro Tricoli W → en + jets events By
Rutherford-Appleton Laboratory
In collaboration with Monika Wielers
ATLAS-UK SM meeting, 26th February 2007

2. Alessandro Tricoli, RAL
Overview
Long term goal:
W + jets cross section as function of jet multiplicity
Immediate goal:
set up analysis tools for CSC Note W/Z +jets
check ATLAS electron and jet reconstruction performances
estimate systematic uncertainties
The only W->en +jets MC samples available now
are SUSY samples (with very high filter cuts)
however the generation of a SM sample has started.
In this presentation
Monte Carlo’s for multi-jet production: Alpgen
W->en +jets event selection algorithm
show basic distributions, main focus on jets and jet algorithms:
Missing ET
electron ET, h
jet ET, jet multiplicity with different jet algorithms
effect of jet energy scale uncertainty on jet multiplicity
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

3. Alessandro Tricoli, RAL
Multi-jet production
SM multi-jet production processes , i.e W/Z + jets, important background
to new physics searches.
The large energies and luminosity at the LHC make final states
with several hard and well separated jets a rather common phenomenon,
(i.e. from hard QCD radiative processes, from decays of W, Z, top, Higgs, SUSY particles, etc.)
Classical MC generators such as PYTHIA and HERWIG:
parton-level M.E. calculations at LO then combine it
with the partonic evolution given by the parton-shower (LL approx.).
Other recent MC, such as
hard process calculations to NLO then combine it with the LL-parton shower evolution.
Problem of matching between hard process and parton shower.
In the case of large jet multiplicities, the complexity of the matrix element evaluation and of its singularity structure prevents the application of these approaches
New strategies has been introduced for merging the exact matrix
elements at the leading order in QCD and EW with the parton shower.
ALPGEN
CKKW method (applied on SHERPA MC)
&
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

4. Alpgen Multi-parton hard processes MC’s in hadronic collisions:
It performs the calculations for a large set of parton-level processes of interest for the LHC:
For example W+1,2,3,4,5 parton samples are generated separately at LO.
The interface to HERWIG or PYTHIA provides the treatment of higher-order correction
(via parton-showers) and hadronisation.
How to avoid double counting between samples and provide
predictions for inclusive samples of arbitrary jet multiplicity?
Jet-parton matching
(MLM prescription)
MLM (Michelangelo Mangano) prescription:
Matching of matrix-element hard partons and shower generated jets: (for exclusive samples) a given a jet-algorithm the number of jets must be equal to the number of partons.
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

5. Alessandro Tricoli, RAL
Data Sets
SUSY Alpgen + Jimmy: 4 samples
exlcusive W → en + 2 parton,
W → en + 3 partons,
W → en + 4 partons
inclusive W → en + 5 partons
PDF: CTEQ6LL
Reconstruction ATHENA v (and )
Offline Analysis: AOD/AAN-tuples
Very stringent generator filter cuts:
4 jets with pt > 40 GeV
leading jet with pt > 80 GeV
MissEt > 80 GeV
Sample
Dataset
Sim rel
Rec. rel.
N. Gen.
Events
MLMxFilter
Eff.
 (corr.) (pb)
We+1parton NA
We+2partons
5223
12.0.4
2,099
0.67
We+3partons
5224
5,704
0.0278
3.39
We+4partons
5225
6,275
0.0710
2.02
We+5partons
5226
5,827
0.142
0.87
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

6. W → en +jets Event Selection
First identify events with high Et electron from W decay
W → en event offline selection :
Standard Electron Identification: isEM and cluster-track matching requirement
cracks removal h= and |h|<2.4
Electron ET>25 GeV
Missing-ET >25 GeV (redundant in this sample due to filter cuts)
No jet veto cuts
Then look at jets in the events
Electron-Jet overlap removal:
jets selected if there is no electron within DR<0.4
Minimum Jet Pt cut:
due to high generator filter cuts, choice jet Pt>40 GeV
(it will be lowered for SM samples) e n
jet p
Assumed luminosity = 100 pb-1
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

7. Electron ET, h distributions
Compare reconstructed electrons after the W offline selection cuts to
Generator level electrons coming from W decay (no cuts applied at generator level)
12.0.3
Bump due to W+2,3 partons events
12.0.3
Reconstructed Ele.
After offline selection
Generated electrons
From W decay (no cuts)
Electron Et (GeV)
Electron h
Dependency of electron identification eff. on number of jets
Main concern ele isolation cuts (trigger and offline):
the more jets in the events the more likely the electron is not isolated
Dependency of the shape of electron distributions
on the number of jets
Investigation
By Monika
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

8. Missing ET and Missing ET Resolution
MET_Truth
MET_Final
MET_RefFinal
Cell-based:
MET_Final (H1-style)
MET_RefFinal (Refined calibration
a la TDR: apply different weights to cells
owning to different reconstructed objects
(electrons, jets,...) )
ATHENA
Missing ET
MET_Final
MET_RefFinal
ATHENA
Missing ET Resolution:
(misETTruth – misETRec)
Peak shift from MET_Final
to MET_RefFinal
misETTruth–misETRec
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

9. Missing ET: Gaussian Fits
MET_Final
MET_RefFinal
MET_RefFinal Distr.
FITS:
Mean = (5.57 ± 0.19) GeV
= (14.74 ± 0.17) GeV
c2 / Ndof = 3.34
MET_Final
Mean = (0.09 ± 0.18) GeV
= (14.17 ± 0.17) GeV
c2 / Ndof = 3.05
MET_RefFinal
MET_RefFinal
Mean shift from MET_Final to
MET_RefFinal by ~ -5.5 GeV
MET_RefFinal now peaks at ~0 GeV
Double gaussian needed to fit tails
Resolution has only slightly improved from MET_Final to MET_RefFinal
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

10. Missing ET Res. as function of Npartons
MET_RefFinal
Samples are not normalised here
Np2
Np3
Np4
Np5
Mean
0.9±04
0.0±0.3
-0.0±0.3
0.3±0.4 s
12.4±0.3
13.4±0.2
14.6±0.3
17.7±0.4
GeV
(As expected) Missing ET resolution deteriorated with increasing numbers
of partons in the final state
sigma increases by ~5 Gev from 2-parton to 5-parton events
peak is constantly consistent with 0 (within stat uncertainties)
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

11. Accompanying Jet Multiplicity
Jet Pt > 7 GeV
Jet Pt > 40 GeV
CONE R=0.4
KT D=0.4
KT D=0.6
CONE R=0.7
CONE R=0.4
KT D=0.4
KT D=0.6
CONE R=0.7
Njets
Njets
Large discrepancy between Cone and KT algorithms is mainly
due to high multiplicity of low Pt jets
at high Pt CONE and KT algorithms give similar multiplicities
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

12. Accompanying Jet Multiplicity Cumulative Multiplicity >= Njets
Fit lines are exponential
Jet Pt > 7 GeV
Jet Pt > 40 GeV
CONE R=0.4
KT D=0.4
KT D=0.6
CONE R=0.7
CONE R=0.4
KT D=0.4
KT D=0.6
CONE R=0.7
Multiplicity >= Njets
Multiplicity >= Njets
Large discrepancy between Cone and KT algorithms is mainly
due to high multiplicity of low Pt jets
after min pt cut KT and Cone are very close
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

13. How accurately can we measure the jet multiplicity?
(thanks to Chiara Roda)
Many sources of uncertainties: noise suppression, jet clustering, JES etc.
At CDF the largest uncertainty is due to the Jet Energy Scale (JES)
uncertainty
Aim it to use ATLAS data
to estimate JES we look at E/p of single hadrons, jet track multiplicity etc
For the time being we use MC’s:
question: how well the reconstructed jet multiplicity reproduces
the multiplicity of the thuth-jets?
comparisons between Detector and Generator levels
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

14. Reconstructed vs Generated Jets: Jet multiplicity
Detector level
Generator level
Detector level
Generator level
Jet Pt > 7 GeV
CONE R=0.4
CONE R=0.7
Generator:
Jet ET>7GeV
|h|<5
Detector:
Jet ET>7GeV
|h|≤4.8
N jets
N jets
KT D=0.4
KT D=0.6
Truth:
Jet ET>7GeV
|h|<5
Detector:
Jet ET>7GeV
|h|≤4.8
N jets
N jets
Same jet algorithms and cuts applied on reconstructed and generated jets
More jets at Generator wrt Detector
level especially for CONE algorithms
KT algorithms are closer to truth
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

15. Reconstructed vs Generated Jets: Jet multiplicity
Detector level
Generator level
CONE R=0.4
KT D=0.6
KT D=0.4
N jets
Jet Pt > 40 GeV
CONE R=0.7
Same jet algorithms and cuts applied on reconstructed and generated jets
Better agreement between det. and
gen. level multiplicities for higher Pt jets
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

16. Alessandro Tricoli, RAL
Jet ET Resolution varying Npartons (EtREC – EtMatch-Truth ) / EtMatch-Truth
Jet Pt > 7 GeV
Truth Jet Et (GeV)
CONE R=0.4
CONE R=0.7
KT D=0.4
KT D=0.6
Np2
Np3
Np4
Np5
CONE R=0.7 and KT D=0.6 show flattest distributions
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

17. Jet Energy Scale uncertainty
Largest uncertainty at CDF
Assume the uncertainty on Jet Energy Scale is 5% or 10%:
The ET of each jet is miscalibrated by ±5% and ±10% wrt to our
current best calibration
For each jet in the event 4 samples are produced:
for +5%, -5%, +10% and -10% jet ET miscalibrations
+10% Et MisCal
-10% Et Miscal
+5% Et MisCal
-5% Et Miscal
(ETMisCalibration – ETStandardCalibration) / ETStandardCalibration
Leading Jet Et (GeV)
Here you see the ET miscalibration
for the leading jet
Generator filter cut:
Leading Jet Pt > 80 GeV
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

18. Effect of Jet Scale Uncertainty on Jet Multiplicity
Jet Pt > 7 GeV
Jet Pt > 40 GeV
# Events
±10%
± 5%
# Events
KT D=0.4
KT D=0.4
N jets
N jets
If jet energy is miscalibrated by +5% or +10% (solid black and blue lines)
Distribution shifted upwards, i.e. more events with larger jet multiplicity
If jet energy is miscalibrated by -5% or -10% (dashed black and blue lines)
Distribution shifted downwards, i.e. more events with smaller jet multiplicity
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

19. Effect of Jet Scale Uncertainty on Cumulative Jet Multiplicity
Jet Pt > 7 GeV
Jet Pt > 40 GeV
±10%
± 5%
# Events
# Events
KT D=0.4
KT D=0.4
N jets
N jets
Cross Section Uncertainty
Cross Section Uncertainty
±10%
±5%
±10%
±5%
KT D=0.4
KT D=0.4
N jets
N jets
Minimum in uncertainty (probably) due to tight filter cuts on SUSY samples.To be checked with SM sample.
uncertainty on cross-section grows with Njets: from ~6% to ~50% at very high Njets
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

20. Cross Section Uncertainty due to Jet Scale Uncertainty
Samples
& ET Miscal
CDF(’98)
Cone R=0.4
ATLAS
KT D=0.4
KT D=0.6
Cone R=0.7
+D%
-D%
We+ ≥1 jet
±10% (±5%)
6.7
14.4
(6.3)
13.8
(6.4)
10.7
(5.2)
17.4
(9.1)
7.3
(3.5)
We+ ≥2 jets
±10% (±5%)
10.1
11.5
13.3
(5.9)
(6.1)
10.2
(5.1)
15.6
(8.4)
17.1
(8.9)
7.0
(3.4)
7.2
We+ ≥3 jets
12.8
16.7
6.3
(3.0)
(4.3)
5.8
(3.6)
9.0
(4.0)
7.8
(4.7)
12.5
4.3
(2.5)
We+ ≥4 jets
22.6
15.8
(7.1)
18.3
(8.5)
14.7
(7.8)
(7.0)
16.6
(9.2)
20.4
(10.3)
12.4
13.7
(6.6)
We+ ≥5 jets NA
21.4
(11.5)
22.3
(11.4)
20.8
(9.5)
20.5
(10.7)
24.7
(10.6)
22.4
20.2
18.9
We+ ≥6 jets
25.4
(14.3)
23.5
(11.8)
22.2
(10.4)
23.6
(11.9)
25.6
(12.8)
25.5
(14.1)
26.4
(11.1)
ATLAS SM UK Meeting, 26th Feb. 2007
Jet Pt > 15 GeV
Jet Pt > 40 GeV
Alessandro Tricoli, RAL

21. Alessandro Tricoli, RAL
Conclusions
Effect of the jet scale uncertainty on the W+jets cross section
(to be redone with SM sample!!)
is between ~ 3% (6%) and 14% (26%)
for a jet energy scale uncertainty of 5% (10%)
the dependency on the jet algorithm can be ~7%
the challenge is to have a realistic estimate of the jet energy scale from data
Missing ET:
MET_RefFinal is an improvement wrt MET_Final
Missing ET resolution deteriorated with higher parton multiplicity
We have been developing tools to investigate W->en + jets events
so far applied on SUSY samples
will be applied to the SM samples when available
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

22. Alessandro Tricoli, RAL
Outlook
Experimental studies:
study JES with topocluster-jets: different clustering, noise suppression etc
noise simulation and subtraction
QCD background,
UE subtraction
etc
Theoretical studies:
In collaboration with Maria Fiascaris (Oxford student)
and Mandy Cooper-Sarkar we want to estimate the PDF uncertainty
on the jet multiplicity:
is it larger or smaller than the experimental systematic uncertainties?
if larger can we use these events to improve the PDF fits?
(thanks to Chiara Roda)
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

23. Alessandro Tricoli, RAL
EXTRAS
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

24. Alessandro Tricoli, RAL
Multi-parton hard processes MC’s in hadronic collisions:
Alpgen vs Sherpa
New strategies has been introduced for merging the exact matrix
elements at the leading order in QCD and EW with the parton shower.
CKKW method (applied on SHERPA MC): This involves a re-weighting of the matrix element weights with Sudakov form factors (non-emission prob.), and the veto of shower emissions in regions of phase-space already covered by the parton-level configuration
ALPGEN:
Requires long generation time
It performs the calculations for a large set of parton-level processes of interest for the LHC:
For example W+1,2,3,4,5 parton samples are generated separately at LO.
The interface to HERWIG or PYTHIA provides the treatment of higher-order correction
(via parton-showers) and hadronisation.
How to avoid double counting between samples and provide
predictions for inclusive samples of arbitrary jet multiplicity?
Jet-parton matching
(MLM prescription)
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

25. Alessandro Tricoli, RAL
MLM prescription
Matching of matrix-element hard partons and shower generated jets, following the so-called MLM prescription: given a jet-algorithm the number of jets must be equal to the number of partons.
Events passing the matching criterion and having extra jets due to the parton shower evolution can be kept (inclusive mode) or rejected (exclusive mode). The inclusive mode must be used only for the sample with the highest parton multiplcity (e.g. the W+5 jet sample in our case).
The set of showered events which survived the matching should be combined to obtain a fully inclusive result. For example for W + up to 5 jets, we generate exlusive W+1 parton, W+2 partons, W+3 partons, W+4 partons and inclusive W+5 partons
Each of the individual samples will have its own absolute normalization.
Since the definition of jet used by the matching prescription will most
likely not coincide with the jet definition used by the user analysis, events from a given
partonic multiplicity will result in events with a spectrum of jet multiplicities.
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

26. Jet ET Resolution varing Npartons EtREC vs EtMatch-Truth
Jet Pt > 7 GeV
CONE R=0.4
CONE R=0.7
Np2
Np3
Np4
Np5
Np2
Np3
Np4
Np5
Reco Jet Et (GeV)
Reco Jet Et (GeV)
Truth Jet Et (GeV)
Truth Jet Et (GeV)
KT D=0.6
KT D=0.4
Np2
Np3
Np4
Np5
Np2
Np3
Np4
Np5
Reco Jet Et (GeV)
Reco Jet Et (GeV)
Truth Jet Et (GeV)
Truth Jet Et (GeV)
In this energy range there is no clear dependency on parton multiplicity
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

27. Effect of Jet Scale Uncertainty on Jet Multiplicity
Jet Pt > 7 GeV
Jet Pt > 40 GeV
Standard Cal.
-10% Mis-Cal
+10% Mis-Cal
-5% Mis-Cal
+5% Mis-Cal
KT D=0.4
KT D=0.4
N jets
N jets
If jet energy is miscalibrated by +5% or +10% (solid black and blue lines)
Distribution shifted upwards, i.e. more events with larger jet multiplicity
If jet energy is miscalibrated by -5% or -10% (dashed black and blue lines)
Distribution shifted downwards, i.e. more events with smaller jet multiplicity
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

28. Effect of Jet Scale Uncertainty on cross-section of Leading Jet Et
(ds/dETMisCalibration – ds/dETStandardCalibration) / ds/dETStandardCalibration
Leading Jet Et (GeV)
+10%
-10%
+5%
-5%
Cross Section Uncertainty
Jet ET Miss-calibrations
KT D=0.4
Asymmetric uncertainty upwards and downwards
Uncertainty on W+jets cross section is larger for low ET leading jets (~ %)
In the higher ET spectrum (~500 GeV) cross section uncertainty is about 50%,
but stat is poor at high ET
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

29. Jet Scale Uncertainty Summary
All Jet algorithms have similar trends with a minimum at W + ≥3 jets
this must be checked with SM samples with loser cuts
Order of magnitude of cross-section uncertainty is similar to CDF
We have to understand from data what is our jet scale uncertainty:
5%, 10% ?
Changing jet algorithms the effect of the jet energy scale uncertainty
on the cross section can vary by up to ±7% (especially at low multiplicities).
An uncertainty on the jet energy scale of ±5% (±10%)
determines an uncertainty on the cross section which grows with
the multiplicity and is between ~ 3% (6%) and 14% (26%)
(for jet multiplicities from ≥2 to ≥6 jets)
ATLAS SM UK Meeting, 26th Feb. 2007
Alessandro Tricoli, RAL

30. Trigger efficiencies for W+jet events
From Monika
Use simulated csc files for W+2, 3, 4, 5 jets generated with alpgen
Datasets: 5223, 5224, 5225, 5226
For comparison use inclusive We generated with Pythia (DS 5104)
Reconstruct using TriggerRelease
Trigger efficiencies for e25i normalised to electron with ET>25GeV in ||<1.37 or 1.52<||<2.47 at MC level
Eff %
Incl. W
W+2j
W+3j
W+4j
W+5j L1
98.3
92.4
91.9
78.5
76.9 L2
90.5
82.9
82.3
67.1
63.1 EF
80.2
72.3
72.0
58.3
54.5
Offline isem (no TRT cut)
69.0
68.4
55.0
51.8
EF (no L1 isol)
81.2
77.9
77.8
74.3
69.8
isem only
85.2
83.9
82.5
79.3
76.3
Alessandro Tricoli, Oxford University

31. Alessandro Tricoli, Oxford University
Trigger efficiencies
From Monika
The more jets in the events the more likely the electron is not isolated
L1 isolation rejects those events
EM isolation in ring around 2x2 core ≤ 3 GeV
Had core isolation in 2x2 towers behind EM core ≤ 2GeV
Had isolation in ring around 2x2 core ≤ 2GeV
Also other e-identification cuts (trigger and offline) which look at isolated electrons in ‘smaller’ region partly rejects those events as jet very near
Alessandro Tricoli, Oxford University