Update on Diffractive DijetsAnalysis Hardeep Bansil

University of Birmingham

Soft QCD / Diffraction WG Meeting28/10/2013

Contents

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• Introduction• Analysis– Event selection– Jet selection– Diffractive quantities– Unfolding

• Systematics• Results and conclusions• Further plans

Diffractive dijets• Look for single diffractive events (pppX)

– Involve a rapidity gap due to colourless exchange with vacuum quantum numbers: “pomeron”

• Search for diffraction with a hard scale set by 2 jets– Described by diffractive PDFs + pQCD cross-sections

• Previous measurements of hard diffractive processes at HERA and Tevatron– At Tevatron, ratio of yields of single diffractive to inclusive dijets ≈ 1%– Likely to be smaller than this at LHC

• 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

• Measure the ratio of the single diffractive to inclusive dijet events

• Gap Survival Probability – the chance of the gap between the intact proton and diffractive system being lost due to scattering– Tevatron have Gap Survival Probability of 0.1 relative to H1 predictions– Khoze, Martin and Ryskin predict LHC to have GSP of ~ 0.03

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Rescatter with p?

ξ

Analysis

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• 2010 Period B data with GRL (L1Calo and MinBias streams)– Low pile-up (Peak <μ> for selected runs < 0.15)

• PYTHIA8 samples of ND, SD and DD events with ATLAS UE Tune AU2-CT10– Samples produced with jets in different pT ranges

– No pile-up

– CTEQ10 PDFs for proton, H1 2006 DPDF LO Fit B for pomeron

– Schuler-Sjöstrand for IP flux (ε = 0), unconventional

– No rapidity gap destruction built in

• Generated using separate filters to produce:– Flat leading jet pT spectra

– Flat forward gap spectra up to threshold (primary samples)

• At least 2 anti-kT jets (R=0.4 or R=0.6) with pT > 20 GeV, |η| < 4.4 – Require medium quality jet cleaning cuts in data

Reconstructed event vertex

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• Require exactly 1 primary vertex in eventwith at least 5 associated tracks

• No pile-up vertices with at least 2 assoc. tracks

• Excludes beam-induced background

• Applied to data and MC samples

• MinBias stream (~29% events not passed)– Mainly not meeting primary vertex requirement

• L1Calo stream (~10% events not passed)– Mainly not meeting pile-up requirement

• MC designed to have no pile-up so much smallereffect (~3%)

• Correction to effective luminosity for all data/MC samples

Combined Pythia8 ND+SD+DD, Gap Filtered

Combined 2010 Period B data

Trigger

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• Adapted from SM 2010 inclusive dijet analysis– Assign trigger for both jets (L1_MBTS_1 or L1_J5 for Period B)

– Trigger on MBTS_1 in MinBias stream, J5 for L1Calo stream

• Check if triggers passed• Avoid overlap in streams

– If one or more jets passes MBTS_1 then use MinBiasstream

• Luminosity based on triggers– J5 (unprescaled) – GRL gives 6.753 nb-1 6.117 nb-1 after vertex

– MBTS_1 (av. prescale = 50) – GRL gives 0.303 nb-1 0.215 nb-1

– Total int. luminosity = 6.332 nb-1 (±3.5% - 2010 final lumi determination)

Jet |n| < 2.9

Jet pT < ~30 GeV

J5

MBTS_1

Jet |n| > 3.3 MBTS_1

Jet 2.9 < |n| < 3.3 J5 or MBTS_1(match to RoI)

YES

YES

YES

YES

NO

NO

NOJet |n| < 2.9

Jet pT < ~30 GeV

J5

MBTS_1

Jet |n| > 3.3 MBTS_1

Jet 2.9 < |n| < 3.3 J5 or MBTS_1(match to RoI)

YES

YES

YES

YES

NO

NO

NO

Jet 1Jet 2

Trigger efficiencies

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• L1_MBTS_1 taken to be fully efficient

• L1_J5 efficiency measured using independently triggered sample (L1_MBTS_1) for data and MC samples

• Match offline jet to L1 Jet RoI with ΔR = √(ηRoI-ηJet)2+(φRoI-φJet)2 < 0.5

• Treat EM barrel-transition (1.3<|η|<1.6 as separate region to study)

• Fit with sigmoid - f(x) = a0(1 + Erf((x − a1)/a2) where a0, a1, a2 are fit parameters

• Fit slightly over-estimates rise to plateau but excellent elsewhere

• Select events down to 80% with L1_J5 (27 GeV for R=0.4, 32 GeV for R=0.6)

J5 efficiencyMinBias DataExcl. EM Transition

J5 efficiencyMinBias DataEM Transition only

Trigger efficiencies

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• Use PYTHIA8 ND to study any potential strong dependencies on J5 efficiency

• No strong dependencies other than knowndecrease in efficiency around EMbarrel-endcap transition region

Jet pT Correction

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• EM+JES scale: pT of recon and truth jets equal on average

– Optimised for higher pT jets (no rapidity gaps)

• Observed more recon jets passing selection than truth level

• Match recon to truth jets in all MC samples and determine pT shift = (pTrecon − pT

truth)/pTtruth

– Determine potential pT, η dependence when recon jets pass cuts

– Largest for central and low pT jets

• Typically 5% over-reconstruction – used as single value correction to data/MC jet pT scale

– Really both pT and η dependent but would be more complicated to implement

Forward gap algorithm

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• Adapted • Track cuts remain unchanged:

• |eta| < 2.5, At least 1 Pixel hit, At least 1 B-layer hit, At least 1 SCT hitPt > 200 MeV & at least 4 SCT hits, Pt > 300 MeV & at least 6 SCT hitsd0 wrt PV >= 1.5, z0*sin(theta) >= 1.5

• (Stable) Truth particles:|η| < 4.8, pneutral > 200 MeV, pcharged > 500 MeV to better reflect what will be observed in the calorimeter

• Clusters (EM calibration):|η| < 4.8• For clusters, with 1.3 < |η| < 1.32, reject if Ehad/E > 0.4No cut on p (looked at cut with pcluster > 200 MeV but made negligible difference)For gap definition – not in Tile calorimeter, significance > threshold remains the

same

Systematics• Jet energy scale

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Results and conclusions• Differential cross sections calculated as for variable X

• Nweighted accounts for trigger efficiency per data event and unfolding

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XLN

dXd weighted

Results•IP content in terms of quarks and gluons still not well known•Study of diffractive dijets using early ATLAS 2010 data•Compared to Monte Carlo simulations of ND, SD and DD events (PYTHIA8)

•Measurement (with full systematic treatment) performed as function of:•ΔηF - largest forward gap from either side of ATLAS detector acceptance•ξ - fraction of momentum of proton transferred to pomeron•Transverse momentum (pT) and pseudorapidity (η) of jets

•Data shape described by mix of SD+DD and ND jets after large gap•MC normalisation inadequate → needs further investigation

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IP

X

Further Plans: Monte Carlo production• Analysis uses only one set of Monte Carlo samples generated using PYTHIA8

• Need samples created by other generators

• Want model independent conclusions

• Unfold additional variables (zIP)

• PYTHIA8 diffractive samples use unconventional model (Schuler-Sjöstrand)in generation

• Plans:

• PYTHIA8 - correct existing samplesor generate new models?

• Generate samples with completelydifferent MCs

• POMWIG

• Other ND, SD models if HERWIG++ not useable?

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Further Plans• Correction process uses RooUnfold software to unfold individual distributions separately but

there are multiple exponentially falling distributions in this analysis: ∆ηF and jet pT

• Both produce a net migration to larger rapidity gaps

• Improve by using simultaneous unfolding procedure for both variables

• Would allow pT correction to be removed

• Waiting to hear back about modified RooUnfold package used for W/Z analysis

• Could also be used to unfold variables (e.g. jet pT) as function of gap size

• Compare results with Prague colleagues consistency checks etc.

• Aim is to get analysis published in e.g. Physics Review Letters or Physics Letters B

• Get editorial board as soon as possible

• Start working on paper + supporting material

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