1. Searches for Diffractive Dijet Production Hardeep Bansil University of Birmingham SM Soft QCD meeting 24/10/2011

2. Contents Theory & Motivation Analysis Plots Next steps 2

3. CMS Diffractive W/Z search CMS have had difficulty in trying to find diffractive W/Z signals – 300 of 40000 W/Z events have gap – Pythia 6 tunes (ND) plotted v data – No clear signal above ND More luck in studying asymmetry of W being in the same hemisphere as the gap – POMPYT (diffractive) incl. with ND – Determined that 50 ± 10% of events with a forward gap > 1.9 are diffractive – Harder to get result with Z Should have more opportunities studying dijets as source of diffractive hard scattering 3

4. Diffractive dijets Look for single diffractive events (pp  pX) – Involve a rapidity gap due to colourless exchange with vacuum quantum numbers: “pomeron” Then look for dijet system within X – Hard diffraction Sensitive to the diffractive structure function (dPDF) of the proton Studied at HERA and Tevatron – At Tevatron, ratio of yields of SD to inclusive dijets ≈ 1% – Likely to be smaller than this at LHC 4

5. 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 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 (affects measured structure function) – Tevatron have Gap Survival Factor of 10 smaller than H1 predictions – Khoze, Martin and Ryskin predict LHC to have GSF around 30 5 Rescatter with p? (ξ)(ξ) Comparison of Tevatron results to H1 predictions Gap destruction by secondary scattering

6. Event display of candidate event 6 +η MBTS counters filled -η MBTS counters empty Large area of calorimeter empty Two jets, one in FCAL and one in HEC

7. 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 Additionally study jet (η, E T, M jj ) and gap properties Determine differential cross sections for as many of these variables as possible 7 M jj MxMx ξX ξX

8. Gap Finding Algorithm Gap finding based on Soft Diffraction analysis – Divides calorimeter into 49 rings of 0.2 in η – Identifies calorimeter cells where energy significance (= cell energy/noise) large enough that probability of noise cell studied in event is small – Where no cells in ring found above ESig threshold  ring is ‘empty’ – Full details in blue box Determine the size of the biggest forward gap 8 Example Single Diffractive Topology Detector gap definition Calorimeter: no cell above threshold E/σ > S th - probability of noisy cell in ring smaller than 10 -4 (electronic noise only, no pile-up environment) Tracker: no good track above pT > 200 MeV, |η| < 2.5 Truth gap definition No stable particle above pT > 200 MeV Δη F :3.4 |η Start |:4.9 Δη F :3.4 |η Start |:4.9

9. Analysis Using Athena version AtlasProduction-16.6.4.2 Using MinBias stream data10 period A and B ESDs – Run 153030 (period B) excluded (explained later) – Run 155118 (period B) excluded (large trigger prescales) – Total ∫L dt = 8.71 nb -1 - calculated using online iLumiCalc tool with L1_MBTS_2 ref. trigger Average for selected runs < 0.15  currently ignore pile-up Anti-Kt jets with R=0.6 or R=0.4: – Require >= 2 jets in event – E T Jet1,2 |η| < 4.5 – E T Jet1 > 26 GeV, E T Jet2 > 20 GeV for asymmetric jet E T cuts (NLO), cut values suggested based on work by Radek Zlebcik (Prague) – Jet E T Jet2 limit and η cuts based on jet energy scale systematic – Currently no requirements to ask about jet quality cuts Ask for a forward gap: |η start | = 4.9, Δη F ≥ 2.0 9

10. Asymmetric Jet Cuts Parton level studies of single diffractive dijets Using NLOJET++ and Frixione with cuts on right  Look at NLO negative/positive interference contributions to cross section If p T of sub-leading jet is 20 GeV, then safe cut for leading jet is at 26 GeV (start of exponential drop in csx) 10 NLO Cross section plots courtesy of Radek Zlebcik Logarithmic y-axis Linear y-axis

11. Monte Carlo for Analysis Currently using Pomwig LO generator as main comparison – Modifies Herwig ep photoproduction so e  e+γ becomes p  p+IP – No rapidity gap 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) Using MC samples generated by myself (4000 events of each Pomwig sample) Will need to get official Monte Carlo production done soon Have Pythia 6 and Pythia 8 Dijet samples to use as background (8-17, 17-35, 35-70, 70-140 GeV) – Pythia 8 J0 sample (8-17 GeV) not available without pile up so not used here (needs official MC production too) Rapgap – Still trying to get this working in Rivet Try Herwig++ samples soon 11 Will asking for official MC prod now mean I have to migrate to Athena v17?

12. Reason to exclude run 153030 12 AntiKt4 AntiKt6 In an otherwise empty event, the problem modules create a large energy deposit in TileCal In AntiKt4 this can make 2 high E T jets passing the cuts, but with a larger cone size it will only create 1 jet Note the large amount of missing E T required to balance the event Affects most lumi blocks in run, DQ group informed as no DQ flags for this

13. Uncorrected Gap Size Distribution Ratio of ΣMonte Carlo to Data suggests a Gap Survival Factor of 20-30 in majority of bins (prev. ≈3) By Δη F of 6, ξ = 10 -4.5  M X = 39.4 GeV – cut out phase space for producing pair of 20 GeV jets so get drop in events after this point Dijet samples contribute less at higher gap sizes with new gap algorithm Not seeing flattening out of data or Pomwig SD  not really indicating that a diffractive plateau is present Need to move back to lower p T jets (7 GeV) in order improve chances of observing signal 13 MinBias Data Pomwig SD Pythia 8 Jets Pythia 6 Jets Biggest ND contribution at small Δη F Drop in number of events with Δη F ≥ 6, cuts into phase space Pomwig SD, Pythia 6 & 8 Jets weighted relative to luminosity of data runs used and then plotted (stacked) against MinBias Data

14. Uncorrected Jet η Distribution Pomwig SD, Pythia 6 & 8 Jets weighted relative to luminosity of data runs used and then plotted (stacked) against MinBias Data Ratio of ΣMonte Carlo to Data suggests a Gap Survival Factor of 20-40 Before gap cuts, get a small asymmetry in η (similar results for Jet 1 & 2) – Slightly more events with negative η for leading jet After gap cuts, get more events with positive η in data (apart from edge bins) and all MC samples have an unusual shape – May have something to do with jet ET cuts but would appreciate any suggestions as to why this occurs 14 MinBias Data Pomwig SD Pythia 8 Jets Pythia 6 Jets MinBias Data Pomwig SD Pythia 8 Jets Pythia 6 Jets Before forward gap cutsAfter forward gap cuts

15. SD weighted relative to luminosity of data runs used and then scaled to data integral (from plot) to make comparison of distribution shape Shape Comparison 15 E T Jet 1 η Jet 1 z IP Gap size Work more on this variable Edge bins need to be studied MinBias Data Pomwig SD Pythia 8 Jets Pythia 6 Jets MinBias Data Pomwig SD Pythia 8 Jets Pythia 6 Jets MinBias Data Pomwig SD Pythia 8 Jets Pythia 6 Jets MinBias Data Pomwig SD Pythia 8 Jets Pythia 6 Jets

16. Differential Cross Sections Pomwig SD weighted to lumi of data runs - Differential cross section as a function of leading jet E T Still need to add in Pythia dijet samples as background 16 MC/Data ratio suggests GSF of approx. 20 MinBias Data Pomwig SD SD Combined Acceptance Still need to determine why some bins have high acceptances (and which SD samples cause this to occur)

17. Differential Cross Sections Pomwig SD weighted to lumi of data runs - Differential cross section as a function of leading jet E T Still need to add in Pythia dijet samples as background No observed diffractive plateau – move to lower p T jets 17 MC/Data ratio suggests GSF of approx. 30 SD Combined Acceptance Still need to determine why some bins have high acceptances (and which SD samples cause this to occur) MinBias Data Pomwig SD

18. Next steps Move to lower p T jets for analysis to see stronger case for diffractive dijet production? – Will it be possible? Will need to evaluate JES systematic for this Try later data (e.g. Period C, D) but now watch out for pile-up and MBTS prescales Apply jet quality cuts Test for calorimeter noise with RNDM stream Get official production of Pomwig SD samples Get official production of Pythia 8 J0 sample Get cross sections from Rapgap / Herwig++ and NLO theory to compare with Pomwig 18

19. BACKUP SLIDES Diffractive Dijets 19

20. 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 20