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Diffractive Dijet Production
Hardeep Bansil University of Birmingham
SM Soft QCD topical meeting: Diffraction and Forward Detectors
24/05/2011
Contents• Theory & Motivation• MC Generators• Analysis• Plots• Next steps
2
Diffractive dijets• A mix of single diffractive events (with rapidity gap due to colour singlet exchange – “pomeron”)
• With dijet events
• To get diffractive dijet events– Hard diffraction– Two high pT jets amongst other hadronic
activity + gap on one side
• Studied at HERA and Tevatron to understand pomeron structure (diffractive parton density functions)
3
Motivation• Understand the structure of the diffractive exchange by
comparison with predictions from electron-proton data and be able to get a measure of FD
jj
• Gap Survival Probability – the chance of the gap between the intact proton and diffractive system being lost due to scattering (affects measured structure function e.g. Tevatron results a factor of 10 smaller than H1 predictions)
4
Rescatter with p?
(ξ)
Event display of candidate event
5
Pictures courtesy
of T. Martin
Interesting variables• Calculate MX
2 ≈ Ep·(E±pz)X ξX = MX2 /s
• Calculate zIP ≈ (E±pz)jj/(E±pz)X • Look at jet (η, ET, Mjj) and gap properties
• Determine cross sections as a function of zIP
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Mjj Mx
ξX
Mx, zIP, xP reconstruction• Based on E±pz method, which uses energy-momentum conservation
and fact that in SD, the intact proton loses almost none of its momentum
• Calculate Mx, xP and zIP using jets and calorimeter clusters on the correct side of the gap
• If X system goes to +z and intact proton to -zMX
2 = Ep·(E+pz)clus
zIP = (E+pz)jj/(E+pz)clus xP = (E-pz)jj/(E-pz)clus
• If X system goes to –z and intact proton to +zMX
2 = Ep·(E-pz)clus
zIP = (E-pz)jj/(E-pz)clus xP = (E+pz)jj/(E+pz)clus 7
Monte Carlo Generators• Currently using Pomwig LO generator• Modifies Herwig ep photoproduction so that ee+γ
becomes pp+IP with CTEQ proton PDF and H1 predictive pomeron flux & PDF
• No rapidity gap destruction built in• Generates QCD 22 process within diffractive system in
different pT ranges (8-17, 17-35, 35-70, 70+ GeV) for SD (system dissociating in ±z direction) + DD
• Only available files on Grid have √s = 10 TeV and old reconstruction so generated new MC samples (1000 events of each) of as well as in a new pT range (5-8 GeV)– Event generation: AP-15.6.13.9 (MC10JobOptions)– Simulation: AP-15.6.13.9– Reconstruction: AP-16.0.3.5
• Will need to get official Monte Carlo production done soon8
Monte Carlo Generators• Pomwig – scattered parton ET distribution scaled by csx
• Csxs agree with each other but not necessarily correct?– Still also see some events where partons generated out of pT range
• Rapgap - Used a lot at HERA but not implemented in Athena– Still trying to get this set up with Rivet– R. Zlebcik (Prague) looking at this from theory perspective and looking to do NLO
calculations• Have Pythia 6, Pythia 8 and Phojet SD and DD samples so can try to find
diffractive dijets within them• Also told Herwig++ can do this as well but very little information on this
available at the moment
9
Analysis• Gap finding based on earlier B’ham/Prague analysis• Divides calorimeter into 10 rings of unit rapidity • Identifies calorimeter cells where energy significance
(= cell energy/noise) large enough that probability of noise cell studied in event is small
• Where no cells with high energy significance found in ring is determined to be ‘empty’
• Determine the biggest gap and where it starts
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A B C D E E D C B A
-5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5
+pi
-pi
|Gap Start| = 5Gap Size = 4 Largest Gap
Example Single Diffractive Topology
Analysis
• Anti-Kt jets with R=0.6: Require >= 2 jets with ET > 7 GeV– Currently no requirement to ask about jet quality cuts– Currently no asymmetric jet ET cuts (NLO) e.g. ET1 > 10, ET2 > 7
• Ask for a forward gap: |start| = 5, gap ≥ 2 units
• Using first seven runs of data10 period A1 (MinBias stream, latest reprocessing)– 152166, 152214, 152221, 152345, 152409, 152441, 152508– Total ∫L dt = 0.198 nb-1 (half of total lumi for period A1) – calculated
using online iLumiCalc tool with L1_MBTS_2 ref. trigger– Average <μ> for selected runs < 0.01 currently ignore pile-up
• Using Pomwig Single + Double Diffractive MC• Also using Pythia 6 Non Diffractive MC for an extra missing
contribution in some regions e.g. small gap sizes (where possible)
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First Truth Level Comparisons• Compared truth parton level with truth hadron level (final
state particles) e.g. MX• Then went on to truth hadron level with reconstruction (in
particular applying cuts to pick out zIP, xP more easily)
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MX parton v hadron MX hadron v reconstructed
First Truth Level Comparisons• Compared truth parton level with truth hadron level (final
state particles) e.g. ξ• Then went on to truth hadron level with reconstruction (in
particular applying cuts to pick out zIP, xP more easily)
13
ξ hadron v reconstructed
Resolutions (Jet ET & Mjj)• Resolutions calculated with Pomwig as (Truth – Recon)/Truth
then fit with Gaussian distribution to determine appropriate bin widths for variables
• Fitted RMS around 12% for leading jet ET, 15% for sub-leading jet & 15% for Mjj
14
Resolutions (Jet η, Gap Size)• Resolutions calculated with Pomwig as (Truth – Recon) then
fit with Gaussian distribution to determine bin widths • Resolutions in η have fitted RMS of 0.04 for both jets – need
to also investigate jet mismatches (|Δη|>1)• Gap size fitted RMS of 0.79, reconstruction making gap bigger
15
Resolutions (zIP, xP)• Resolutions calculated as (Truth – Recon)/Truth used to
determine bin widths for variables
• zIP shows some correlation but xP does not really work - (E±pz) method less sensitive in opposite direction to dissociation
16
zIPxP
Uncorrected Data• Combined Pomwig SD+DD, Pythia 6 ND weighted relative to
luminosity of data runs used and then plotted (stacked) against data
• Ratio of ΣMonte Carlo to data suggests a Gap Survival Factor of around 3 – small
• Pythia 6 ND distributions make significant contribution
17η Jet 1 Mjj
MinBias DataSDSD+DDSD+DD+ND
MinBias DataSDSD+DDSD+DD+ND
Uncorrected Data• Combined Pomwig SD+DD, Pythia 6 ND weighted relative to
luminosity of data runs used and then plotted (stacked) against data
• Ratio of ΣMonte Carlo to data suggests a Gap Survival Factor of around 3 – small
• Pythia 6 ND distributions make significant contribution suggests using tighter gap size requirement
18zIP Gap size
MinBias DataSDSD+DDSD+DD+ND
MinBias DataSDSD+DDSD+DD+ND
Shape Comparison• SD & Combined SD+DD+ND weighted relative to luminosity
of data runs used and then scaled to data integral (from plot) to make comparison of distribution shape
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ET Jet 1 η Jet 1
zIPGap size
MinBias DataSDSD+DD+ND
MinBias DataSDSD+DD+ND
MinBias DataSDSD+DD+ND
MinBias DataSDSD+DD+ND
Note that first bin should actually start from 7 GeV
Differential Cross Sections• Combined Pomwig SD+DD weighted to lumi of data runs -
Differential cross section as a function of leading jet ET along with acceptance
20MC/Data ratio suggests GSF of 3
MinBias DataSDSD+DD
Note that first bin should actually start from 7 GeV
Differential Cross Sections• Differential cross section – as a function of leading jet η
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Acceptance higher in negative η compared to positive η difference in MC simulation?Would this be observed with official MC prod?
MC/Data ratio suggests GSF of 3
MinBias DataSDSD+DD
Differential Cross Sections• Differential cross section – as a function of gap size• Suggests to look at events with gap size ≤ 6
22
Acceptance really high in one bin compared to rest?
No more space for jets with a gap requirement as well?
MC/Data ratio suggests GSF of 3
MinBias DataSDSD+DD
Differential Cross Sections• Differential cross section – as a function of zIP
23
0.8 < zIP < 1.0 has a big acceptance → likely to be due to big migration (also seen in resolution plots)
MC/Data ratio suggests GSF of 3
MinBias DataSDSD+DD
Next steps• Get official production of Pomwig MC• Get cross sections from Rapgap / Herwig++ and NLO
theory to compare with Pomwig• Run over remaining data in 2010 Period A1• Run over inclusive jet samples for background
• Improvements to analysis– Improve resolutions between truth and reconstruction levels
for important variables– Gap selection – use updated B’ham/Prague algorithm
• Apply tighter gap cuts?– Jet cuts – testing quality of jets, asymmetric cuts
• Both necessary but determine how much signal lost– Jet reconstruction – better to use AntiKt with R=0.4 or 0.6?– Pile-up – how to deal with events with 2+ primary vertices– Evaluate various systematics
24
Back up slides
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Meaning of E±pz
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