Hardeep Bansil, Oldrich Kepka, Vlastimil Kus, Paul Newman, Marek Tasevsky Workshop on Diffractive Analyses with ALFA 09/10/2012 Diffractive analyses with gaps

Contents Measuring rapidity gaps in ATLAS Soft diffraction Diffractive dijets How ALFA can help 2

pp Cross Section & Inelastic Interactions Non Diffractive Events Coloured exchange, Soft PT spectrum High multiplicity final states peaking at central rapidity Largest cross section at LHC Diffractive Events Colour singlet exchange (pomeron) results in a rapidity gap devoid of soft QCD radiation Can involve Single or Double proton dissociation Size of the rapidity gap is related to the invariant mass of the dissociated system(s) Related to p z loss of intact proton in Single diffractive case 25-30% of the total inelastic cross section ( ξ X > 5×10 -6 ) is measured to be inelastic diffractive 3 GAP IP

Measuring rapidity gaps in ATLAS Use the full tracking (| η |<2.5) and calorimetric range (| η |<4.9) of detector In the calorimeters electronic noise is the primary concern The standard ATLAS energy deposits are from Topological clustering of cells Seed cell required to have an energy significance σ = E/ σ Noise > 4 Statistically, expect 6 topological clusters per event from noise fluctuations alone 187,616 cells multiplied by P( σ ≥4) ≈ 6 Just one noise cluster can kill a gap Additional noise suppression is employed but set the thresholds as low as the detector will allow Apply a statistical noise cut to the leading cell in the cluster which comes from the LAr systems (noise from the hadronic Tile calorimeter follows a double Gaussian) Set P noise within a 0.1 η slice to be 1.4x10 -4 N is the number of cells in the slice The threshold S th ( η ) varies from 5.8 σ at η = 0 to 4.8 σ at η = 4.9 4

Gap Finding Algorithm Detector split into bins of η Detector Level Bin full if it contains one or more noise suppressed calorimeter clusters above E T cut of 200 MeV - AND/OR - one or more tracks reconstructed above p T cut of 200 MeV Generator Level Bin full if it contains one or more stable (c τ > 10 mm) generator particles above p T cut of 200 MeV Δη F = Largest region of pseudo-rapidity from detector edge containing no particles within bins 5 Example Single Diffractive Δη F :3.4 | η Start |:4.9 Example Non- Diffractive Δη F :0.4 | η Start |:4.9 Forward Rapidity Gap Devoid of particles p T > 200 MeV η = -4.9 to η = 0.5 Δη F = 5.4, ξ = 1x10 -4, M X = 75 GeV Minimum Bias Trigger Scintillators (Physics Trigger)

Soft Diffractive Analysis 6 Exponential Fall Poor Trigger Efficiency Diffractive Plateau Use unfolded data up to a forward gap size of Δη F = 8 Raw Δη F plot for data and MC at the detector level, including trigger requirement on MC and data Event normalised Utilising the first stable beam physics run at √s=7 TeV ATLAS accumulated 422,776 minimum bias events 7.1 μ b -1 (at peak instantaneous luminosity 1.1x10 27 cm -2 s -1 ) Trigger requirement as loose as possible. Require one hit in the MBTS online, offline we required two hits with MC thresholds matched to the efficiency observed in data EPJC 72 (2012) 1926

7 Soft Diffractive Analysis The Raw gap size distribution is unfolded to remove detector effects Tune the ratios in the MCs from Tevatron data Data is corrected for trigger inefficiency at large gap size Single application of D’Agostini’s Bayesian unfolding method MC normalised to Default ND, DD and SD Cross section up to Δη F = 8 Integrated cross section in diffractive plateau: 5 < Δη F < 8 (Approx: -5.1 < log 10 ( ξ X ) < -3.1) = 3.05 ± 0.23 mb ~4% of σ Inelas (From TOTEM) Compare to Pythia 8 4C split into sub-components Non-Diffractive contribution dominant up to gap size of 2, negligible for gaps larger than 3 Shape OK, overestimation of cross section in diffractive plateau Large Double Diffraction contribution across entire forward gap range (large uncertainty) Corrected Δη F Distribution Δη F vs. Pythia 8 ξ= ξ=

Diffractive dijets 8 Rescatter with p? ξ Comparison of Tevatron diffractive PDF to H1 expectations in terms of momentum fraction of parton in Pomeron Gap destruction by secondary scattering Search for single diffractive events 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 Now also studied at CMS Measure the ratio of the single diffractive to inclusive dijet events 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 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 Probability of 0.1 relative to H1 predictions Predict LHC to have GSP of ~ 0.03 – 0.07 Work in progress

Analysis 2 medium Anti-k t jets with R=0.4 or R=0.6: E T Jet1,2 | η | 30 GeV, E T Jet2 > 20 GeV Cut values based on 2010 SM dijet analysis / JES systematic Ask for a forward gap: | η start | = 4.9, Δη F ≥ 3.0 Currently employing two separate strategies for analysis 2010 period A-B ( ∫ L dt ≈ 7 nb -1 ) triggering on L1_J5 or L1_FJ period A-F ( ∫ L dt ≈ 3 pb -1 ) with p T -dependent L1 jet & forward jet triggers Trigger below 100% efficiency plateau to collect more events than 2010 Standard Model inclusive dijet analysis Use POMWIG/HERWIG++ for Single Diffractive Dijets No direct DD samples, DPE samples contribute little Use PYTHIA 8/HERWIG++ samples as inclusive (ND) Dijets Special MC request in preparation, filtered on gap size Reconstruct ξ and z IP using E±p z method based on fwd gap side 9 IP ( ξ )

Hard Diffraction at Generator Level Demonstration of difference in ND and SD models for dijet events Latest gap survival probability estimate 6% included in SD model Hard dijets → bigger M X → smaller gaps Like soft diffraction, have to go to bigger gaps in order to separate SD from ND Contrary to soft diffraction, we no longer observe diffractive plateau 10 p T jet > 20 GeV

Forward Gap Size Distributions Different data ranges will allow for cross checks Both provide significant statistics for forward gap sizes > 3.0 Differential cross section as a function of forward gap size for data vs. MC models (scaled to first bin of data) Without this scaling, difficult to distinguish Single Diffractive signal from Non-diffractive Currently available ND samples statistics insufficient at larger gap sizes so new samples with gap-based filter necessary 11 Diff. csx in Δη F (no noise supp.) – Comp. to MC Diff. csx in Δη F, (no noise supp.), Period B v F

ξ and z IP Aim to measure cross section as function of both of these variables as well as forward gap size With analysis cuts applied, get good correlations between truth and reconstruction levels Relies on picking out the correct value of E+pz or E-pz in calculations based on side gap is on MC- Majority of time we identify side with gap correctly at truth and reconstruction levels (that intact proton would have been on) Data – no comparison to truth so ALFA can help by tagging proton indicating which size gap should be on 12 Truth v recon ξ using Pomwig Truth v recon z IP using Herwig++

What ALFA can do for us Rapidity gaps in ATLAS data are a sensitive probe to the dynamics of soft and hard diffractive proton dissociation The data can be used to investigate and tune the current MC models ALFA can help: Constrain relative amounts of non, single and double diffraction by removing ND background More precise measurements of cross sections Study properties of soft diffractive events (multiplicities, UE) Study properties of hard diffractive dijet events (survival probability, jet shapes, ratios of SD to inclusive dijets) 13

Inclusive dijet  : results – full 2010 Official 2010 results Our reproduction We reproduced official inclusive SM2010 dijet cross section measurement → Excellent agreement! However, this trigger strategy not efficient in collecting events on tail of gap spectrum → designing of new trigger scheme

7700 events (no triggers) vs. 40 (SM2010 triggers) => a lot of room for trigger strategy improvement ATL-COM-PHYS inclusive dijet x-sec measurment Big OR of jet triggers: J20 || J30 || J35 || J50 || J75 || FJ30 || FJ50 No triggers asked for Gap-size distribution Different trigger strategies Large gaps require collecting events with small p T → we have to move below 99% trigger efficiency plateau