Update on Diffractive Dijets Analysis Hardeep Bansil University of Birmingham Soft QCD / Diffraction WG Meeting 28/10/2013

Contents 2 Introduction Analysis – Event selection – Jet selection – Diffractive quantities – Unfolding Systematics Results and conclusions Further plans

Diffractive dijets Look for single diffractive events (pp  pX) – 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 F D 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 ~ Rescatter with p? ξ

Analysis Period B data with GRL ( L1Calo and MinBias streams ) – Low pile-up (Peak 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 p T ranges – No pile-up – CTEQ10 PDFs for proton, H 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 p T spectra – Flat forward gap spectra up to threshold (primary samples) At least 2 anti- k T jets ( R =0.4 or R =0.6) with p T > 20 GeV, |η| < 4.4 – Require medium quality jet cleaning cuts in data

Reconstructed event vertex 5 Require exactly 1 primary vertex in event with 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 smaller effect (~3%) Correction to effective luminosity for all data/MC samples Combined Pythia8 ND+SD+DD, Gap Filtered Combined 2010 Period B data

Trigger 6 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 MinBias stream Luminosity based on triggers – J5 (unprescaled) – GRL gives nb -1  nb -1 after vertex – MBTS_1 (av. prescale = 50) – GRL gives nb -1  nb -1 – Total int. luminosity = nb -1 (±3.5% final lumi determination) Jet |n| < 2.9 Jet p T < ~30 GeV J5 MBTS_1 Jet |n| > 3.3MBTS_1 Jet 2.9 < |n| < 3.3 J5 or MBTS_1 (match to RoI) YES NO Jet |n| < 2.9 Jet p T < ~30 GeV J5 MBTS_1 Jet |n| > 3.3MBTS_1 Jet 2.9 < |n| < 3.3 J5 or MBTS_1 (match to RoI) YES NO Jet 1 Jet 2

Trigger efficiencies 7 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 = √(η RoI -η Jet ) 2 +(φ RoI -φ Jet ) 2 < 0.5 Treat EM barrel-transition (1.3<|η|<1.6 as separate region to study) Fit with sigmoid - f(x) = a 0 (1 + Erf((x − a 1 )/a 2 ) where a 0, a 1, a 2 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 efficiency MinBias Data Excl. EM Transition J5 efficiency MinBias Data EM Transition only

Trigger efficiencies 8 Use PYTHIA8 ND to study any potential strong dependencies on J5 efficiency No strong dependencies other than known decrease in efficiency around EM barrel-endcap transition region

Jet p T Correction 9 EM+JES scale: p T of recon and truth jets equal on average – Optimised for higher p T jets (no rapidity gaps) Observed more recon jets passing selection than truth level Match recon to truth jets in all MC samples and determine p T shift = (p T recon − p T truth )/p T truth – Determine potential p T, η dependence when recon jets pass cuts – Largest for central and low p T jets Typically 5% over-reconstruction – used as single value correction to data/MC jet p T scale – Really both p T and η dependent but would be more complicated to implement

Forward gap algorithm 10 Adapted Track cuts remain unchanged: |eta| 200 MeV & at least 4 SCT hits, Pt > 300 MeV & at least 6 SCT hits d 0 wrt PV >= 1.5, z0*sin(theta) >= 1.5 (Stable) Truth particles: |η| 200 MeV, p charged > 500 MeV to better reflect what will be observed in the calorimeter Clusters (EM calibration): |η| < 4.8 For clusters, with No cut on p (looked at cut with p cluster > 200 MeV but made negligible difference) For gap definition – not in Tile calorimeter, significance > threshold remains the same

Systematics Jet energy scale 11

Results and conclusions Differential cross sections calculated as for variable X N weighted accounts for trigger efficiency per data event and unfolding 12

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: Δ η F - largest forward gap from either side of ATLAS detector acceptance ξ - fraction of momentum of proton transferred to pomeron Transverse momentum (p T ) 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 13 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 (z IP ) PYTHIA8 diffractive samples use unconventional model (Schuler-Sjöstrand) in generation Plans: PYTHIA8 - correct existing samples or generate new models? Generate samples with completely different MCs POMWIG Other ND, SD models if HERWIG++ not useable? 14

Further Plans Correction process uses RooUnfold software to unfold individual distributions separately but there are multiple exponentially falling distributions in this analysis: ∆η F and jet p T Both produce a net migration to larger rapidity gaps Improve by using simultaneous unfolding procedure for both variables Would allow p T 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 p T ) 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 15