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 F D 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)(ξ)

Event display of candidate event 5 Pictures courtesy of T. Martin

Interesting variables Calculate M X 2 ≈ E p ·(E±p z ) X  ξ X = M X 2 /s Calculate z IP ≈ (E±p z ) jj /(E±p z ) X Look at jet (η, E T, M jj ) and gap properties Determine cross sections as a function of z IP 6 M jj MxMx ξX ξX

M x, z IP, x P reconstruction Based on E±p z method, which uses energy-momentum conservation and fact that in SD, the intact proton loses almost none of its momentum Calculate M x, x P and z IP using jets and calorimeter clusters on the correct side of the gap If X system goes to +z and intact proton to -z M X 2 = E p ·(E+p z ) clus z IP = (E+p z ) jj /(E+p z ) clus x P = (E-p z ) jj /(E-p z ) clus If X system goes to –z and intact proton to +z M X 2 = E p ·(E-p z ) clus z IP = (E-p z ) jj /(E-p z ) clus x P = (E+p z ) jj /(E+p z ) clus 7

Monte Carlo Generators Currently using Pomwig LO generator Modifies Herwig ep photoproduction so that e  e+γ becomes p  p+IP with CTEQ proton PDF and H1 predictive pomeron flux & PDF No rapiditygap destruction built in Generates QCD 2  2 process within diffractive system in different p T 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 p T range (5-8 GeV) – Event generation: AP (MC10JobOptions) – Simulation: AP – Reconstruction: AP Will need to get official Monte Carlo production done soon 8

Monte Carlo Generators Pomwig – scattered parton E T distribution scaled by csx Csxs agree with each other but not necessarily correct? – Still also see some events where partons generated out of p T 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 ABCDEEDCBA pi -pi |Gap Start| = 5 Gap Size = 4 Largest Gap Example Single Diffractive Topology

Analysis Anti-Kt jets with R=0.6: Require >= 2 jets with E T > 7 GeV – Currently no requirement to ask about jet quality cuts – Currently no asymmetric jet E T cuts (NLO) e.g. E T1 > 10, E T2 > 7 Ask for a forward gap: |start| = 5, gap ≥ 2 units Using first seven runs of data10 period A1 (MinBias stream, latest reprocessing) – , , , , , , – Total ∫L dt = nb -1 (half of total lumi for period A1) – calculated using online iLumiCalc tool with L1_MBTS_2 ref. trigger – Average 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. M X Then went on to truth hadron level with reconstruction (in particular applying cuts to pick out z IP, x P more easily) 12 M X parton v hadron M X 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 z IP, x P more easily) 13 ξ hadron v reconstructed

Resolutions (Jet E T & M jj ) 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 E T, 15% for sub-leading jet & 15% for M jj 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 (z IP, x P ) Resolutions calculated as (Truth – Recon)/Truth used to determine bin widths for variables z IP shows some correlation but x P does not really work - (E±p z ) method less sensitive in opposite direction to dissociation 16 z IP xPxP

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 17 η Jet 1 M jj MinBias Data SD SD+DD SD+DD+ND MinBias Data SD SD+DD SD+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 18 z IP Gap size MinBias Data SD SD+DD SD+DD+ND MinBias Data SD SD+DD SD+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 E T Jet 1 η Jet 1 z IP Gap size MinBias Data SD SD+DD+ND MinBias Data SD SD+DD+ND MinBias Data SD SD+DD+ND MinBias Data SD SD+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 E T along with acceptance 20 MC/Data ratio suggests GSF of 3 MinBias Data SD SD+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 Data SD SD+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 Data SD SD+DD

Differential Cross Sections Differential cross section – as a function of z IP < z IP < 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 Data SD SD+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±p z 26