1. Update on Diffractive Dijets Analysis Hardeep Bansil University of Birmingham Soft QCD / Diffraction WG Meeting 31/03/2014

2. Contents 2 Updates to the analysis Latest results Further plans Birmingham internal note now available on CDS and on Soft QCD Twiki Also available on Diffractive Dijets SVN https://cds.cern.ch/record/1670320?ln=en Slightly updated version put up today

3. Diffractive dijets Single diffractive events (pp  pX) Rapidity gap from colourless exchange with vacuum quantum numbers “pomeron” Search for hard 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 Measure the ratio of the diffractive to inclusive dijet events Gap Survival Probability – the chance of the gap between the intact proton and diffractive system staying intact 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 3 Rescatter with p ? ξ 3

4. Analysis 4 2010 Period B data with GRL ( L1Calo and MinBias streams ) - ∫L dt = 6.753 nb -1 – Low pile-up (Peak for selected runs < 0.15) – Trigger using mixture of L1_MBTS_1 (prescaled) and L1_J5 (unprescaled) – Vertex requirement - 1 PV (5+ associated tracks), no additional vertices (2+ tracks) 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 – SD+DD reweighted to use super-critical Berger-Streng Pomeron flux POMWIG SD samples generated for alternate diffractive model 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 Calculate forward gap (Δη F ) and xi (ξ ± ) in range |η|<4.8 Using “hybrid” p and p T cuts for clusters / stable truth particles

5. Reweighting Pythia8 SD/DD + Pomwig PYTHIA8 diffractive samples use unconventional model (Schuler-Sjöstrand) in generation Reweight SD based on ξ distribution obtained from inclusive diffractive PYTHIA8 samples Used fit to Berger-Streng model (3 rd order poly) Could also use Donnachie-Landshoff (little difference over measured range) Similar process also for DD using ξ = M X 2 /s POMWIG samples 111112 samples in each dissociation direction for 5 different pT ranges up to 280 GeV Generated over kinematic range 10 -6 < ξ < 0.1 and 10 -6 < |t| < 10 GeV 2 5

6. Differential cross sections Differential cross sections calculated as for given variable X N weighted accounts for trigger efficiency per data event, prescales and unfolding 2D unfolding (X v leading jet p T ) for Δη F and ξ ± Latest update – previously accounted for prescales in L1_MBTS_1 on an event by event basis, led to highly weighted bins with large statistical uncertainties Changed based on iLumiCalc calculation: effective luminosity for L1_MBTS_1 over data studied is ∫L dt = 0.303 nb -1 – use single factor to scale MinBias data back to 6.753 nb -1 Better job of controlling statistical uncertainties Believe it also matches Prague Different systematics considered – jet related, diffraction related, luminosity, etc. More details in note 6

7. Differential cross sections Differential cross sections calculated as for given variable X N weighted accounts for trigger efficiency per data event, prescales and unfolding Forward Gap Size (Δη F ) – R=0.4/0.6 jets Cross sections slightly higher for R=0.6 but maintain the same shape For PYTHIA8, ND ~1.3x larger in first bin then SD+DD and ND fairly even for Δη F >2.5 POMWIG ~3x larger csx than data for Δη F >3, slightly higher for R=0.4 7 R=0.4 R=0.6

8. Differential cross sections Differential cross sections calculated as for given variable X N weighted accounts for trigger efficiency per data event, prescales and unfolding Proton Fraction Momentum Loss (ξ ± ) – R=0.4/0.6 jets, no forward gap requirement POMWIG peaks at lower ξ ± than PYTHIA8, partly due to generation choices POMWIG results significantly above data, PYTHIA8 roughly equal or smaller than data csx 8 R=0.4 R=0.6

9. Rapidity gap survival 9 R=0.4 R=0.6 Use similar process to CMS to estimate rapidity gap survival probability Compare diffractive models to data in last bin Cannot properly calculate from PYTHIA8 as diffractive model results are similar to data POMWIG (LO) R=0.4, GSP = 0.04±0.03 without/with forward gap requirement (3.0 < Δη F < 6.5) R=0.6, GSP = 0.14±0.08 without FRG, 0.18±0.11 with FRG KMR model predicts GSP = 0.03, CMS found 0.12±0.05 at LO (comparison to POMWIG/POMPYT with R=0.5)

10. Further plans 10 Complete comparison of Birmingham / Prague analysis Combine documentation from both groups Aim for editorial board soon Currently have 2 months left of funding available so will need to push analysis along as quickly as possible