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

6

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

10

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)

11

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)

12

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

19

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

21

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

25

Meaning of E±pz

26