1. Update on Diffractive Dijets Analysis Hardeep Bansil University of Birmingham Soft QCD / Diffraction WG Meeting 27/01/2014

2. Contents 2 Introduction Updates – Unfolding systematic – 2D unfolding – Reweighting Pythia8 SD/DD – Efficiencies in data – Note progress Further plans

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 use Schuler-Sjöstrand for IP flux (ε = 0), unconventional 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. (Debugging) Unfolding systematic 5 Data-driven method determined from difference between unfolded reweighted reco MC and reweighted true MC (reweighting MC based on reco MC/data comparison) Typically up to 15%, increasing to 45% difference where statistics are limited so splitting up process into individual steps – Strongly affects distributions with gap > 3.0 cut (which we are interested in) Reco data distribution Reco MC distribution Ratio of reco data / reco MC Fitted function to this distribution True MC distribution True MC distribution weighted by weighting function (True-weighted MC) True-weighted MC after being folded through a statistically independent matrix (Reco MC*) Ratio of reco data / reco MC* Unfolded reco data Unfolded reco MC* Ratio of unfolded reco data / unfolded reco MC*

6. (Debugging) Unfolding systematic 6 Data-driven method determined from difference between unfolded reweighted reco MC and reweighted true MC (reweighting MC based on reco MC/data comparison) Much worse in ξ ± than gap size at larger xi values – due to corrections applied?

7. (Debugging) Unfolding systematic 7 Reco data distribution, Reco MC distribution Should these be scaled based on integral or not? Ratio of reco data / reco MC, fitted function to ratio Tried several different polynomials as tests Ended up with 5 th order polynomial No unique solution for different variables so could be optimised Not perfect description either but is this reasonable?

8. (Debugging) Unfolding systematic 8 True MC distribution True MC distribution weighted by weighting function (True-weighted MC) Provided scaling not necessary in Step 1 then should be OK

9. (Debugging) Unfolding systematic 9 True-weighted MC after being folded through a statistically independent matrix (Reco MC*) Not sure that smearing on original response matrix being applied properly (will ask offline) Folding matrix: matrix giving probability for a value of the true physical quantity to be reconstructed at another value Response matrix: Representing reconstructed physical quantity vs. true physical quantity passing cuts

10. (Debugging) Unfolding systematic 10 Ratio of reco data / reco MC* Average of ratio used for fit around 5, explaining increased difference between reco MC* and data Unfolded reco data, Unfolded reco MC* + ratio Data and reco MC* unfolded using the same original response matrix Ratio of unfolded data and reco MC* roughly the same as LHS plot

11. 2D Unfolding 11 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 Removed 5% p T reduction previously being applied to reconstructed jets ∆η F against leading jet p T (R=0.4) Raw data Unfolded data

12. 2D Unfolding 12 ∆η F against leading jet p T (R=0.4) – Supporting plots from MC Fill (passes cuts at both truth and recon levels) – enough stats for measurement Fakes (pass at recon, fails at truth) and Miss (fails at recon, pass at truth) Fakes have very large spread across gaps and pt Recon MC Fill Truth MC Fill Recon MC Fake Truth MC Miss

13. 2D Unfolding 13 Comparing to 1D unfolding measurement Ratio of unfolded to raw data decreases in every bin but overall change not that large Raw and unfolded data agree in first bin (may also be consequence of putting fakes and misses into response) Big drop in MC and data for bin at 6-6.5 in data Phase space limit should be at gaps greater than 7? Other effect? Drop bin? 2D Unfolding1D Unfolding

14. 2D Unfolding 14 Currently have the capability to unfold (for anti-kt R=0.4 and R=0.6) ∆η F against leading jet p T ξ ± against leading jet p T Still need to add this to cross section measurement Should we do 2D unfolding for other variables e.g. leading jet eta? How will unfolding systematic change if going to 2D?

15. Reweighting Pythia8 SD/DD Analysis uses one set of Monte Carlo samples generated using PYTHIA8 PYTHIA8 diffractive samples use unconventional model (Schuler-Sjöstrand) in generation Reweight SD based on ξ distribution obtained from inclusive diffractive PYTHIA8 samples 15

16. Reweighting Pythia8 SD/DD Reweight SD based on ξ distribution obtained from inclusive diffractive PYTHIA8 samples Ratio fitted with 3 rd order polynomial for B-S model (although did not work for D-L so will revisit) DD trickier as cannot measure ξ directly Can try determining MX from largest gap between stable truth particles but not guaranteed to be perfect Any other quantities that can be used to test with? 16

17. Trigger efficiencies in data Biggest difference between Bham/Prague down to trigger approach for data Gives different numbers of events in MinBias/L1Calo streams Matching of jets to Jet RoIs same but studied for different η ranges Bham studies up to |η|<2.9, looks at EM barrel-transition (1.3<|η|<1.6) separately – Separate fits for anti-kt R=0.4/0.6 jets (good/bad thing?) Prague follows method 2010 dijet analysis – |η|<0.3,0.3<|η|<0.8,0.8<|η|<1.2, 1.2<|η|<2.1,2.1<|η|<3.2 – Better accounts for the η dependence that exists at low pT – Better as a reference to trigger group to defend this – Currently no difference in Looking at 2010 dijet analysis as well to confirm Prague results (results soon) 17 J5 efficiency MinBias Data Excl. EM Transition J5 efficiency Pythia8 ND (Zoomed in)

18. Note progress 18 Thesis converted into ATLAS format Currently being edited (Paul, myself) Need to introduce Prague studies as well – Need immediate agreement on analysis (trigger) selection, consistency checks etc. at same time otherwise will be rewriting more sections – Finalise this week? Will make it available on a CERN SVN repository accessible to everyone working on analysis – How to set this up? When is realistic time to have first combined draft ready  EB etc? – Realistically 4 months left for me (similar timescale for Vlasta?)

19. Further plans 19 Understand why unfolding systematic large in places and add to csx measurement Updated efficiency method in next few days Hopefully finalise Bham/Prague analysis definitions this week Sort out reweighting in Pythia8 SD/DD – show effect on distributions Some systematics used in 2010 dijet analysis derived from PYTHIA6 – should determine from PYTHIA8 where possible? POMWIG – Currently generated 800,000 Pomwig events at truth level (EVNT) – 100,000 for each jet pt range, intact proton direction available in job options – Need to convert again to NTUP_TRUTH as missing anti-kt R=0.6 jet collection – Anyone know what settings need to be added to Athena transform for this? Other MCs to generate for want model independent conclusions?