Transcript 20121009_SoftQcdAlfa_RapidityGaps

Diffractive analyses with gaps
Hardeep Bansil, Oldrich Kepka, Vlastimil Kus, Paul
Newman, Marek Tasevsky
Workshop on Diffractive Analyses with ALFA
09/10/2012
Contents
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Measuring rapidity gaps in ATLAS
Soft diffraction
Diffractive dijets
How ALFA can help
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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)

IP
GAP
Related to pz loss of intact proton in Single
diffractive case
 25-30% of the total inelastic cross section
(ξX > 5×10-6) is measured to be inelastic
diffractive
IP
GAP
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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/σNoise > 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 Pnoise within a 0.1 η slice to be 1.4x10-4
 N is the number of cells in the slice
 The threshold Sth(η) varies from 5.8 σ at η = 0 to 4.8 σ
at η = 4.9
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Gap Finding Algorithm
 Detector split into bins of η
 Detector Level Bin full if it
contains
Example Single
Diffractive
ΔηF:3.4 |ηStart|:4.9
 one or more noise suppressed
calorimeter clusters above ET cut
of 200 MeV
- AND/OR  one or more tracks reconstructed
above pT cut of 200 MeV
 Generator Level Bin full if it
contains
Example NonDiffractive
ΔηF:0.4 |ηStart|:4.9
Minimum Bias Trigger Scintillators
(Physics Trigger)
 one or more stable (cτ > 10 mm)
generator particles above pT cut
of 200 MeV
 ΔηF = Largest region of
pseudo-rapidity from
detector edge containing no
particles within bins
Forward Rapidity Gap
Devoid of particles pT > 200 MeV
η = -4.9 to η = 0.5
ΔηF = 5.4, ξ = 1x10-4, MX = 75 GeV
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Soft Diffractive Analysis
EPJC 72 (2012) 1926
 Utilising the first stable beam physics run at √s=7 TeV
 ATLAS accumulated 422,776 minimum bias events
 7.1 μb-1 (at peak instantaneous luminosity 1.1x1027 cm-2s-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
 Use unfolded data up to a
forward gap size of ΔηF = 8
Exponential
Fall
 Raw ΔηF plot for data and
MC at the detector level,
including trigger requirement
on MC and data
Poor Trigger
Efficiency
Diffractive Plateau
 Event normalised
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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 ΔηF = 8
 Integrated cross section in diffractive plateau:
 5 < ΔηF < 8 (Approx: -5.1 < log10(ξX) < -3.1)
= 3.05 ± 0.23 mb
 ~4% of σInelas (From TOTEM)
Corrected ΔηF Distribution

ξ=10-5.1
ξ=10-2.5
ΔηF vs. Pythia 8
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)
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Diffractive dijets
Work in progress
 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 FDjj
 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
Rescatter with p?
Comparison of Tevatron
diffractive PDF to H1
expectations in terms of
momentum fraction of
parton in Pomeron
Gap destruction by
secondary scattering
ξ
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Analysis
 2 medium Anti-kt jets with R=0.4 or R=0.6:
 ET Jet1,2 |η| < 4.4, ET Jet1 > 30 GeV, ET Jet2 > 20 GeV
 Cut values based on 2010 SM dijet analysis / JES systematic
 Ask for a forward gap: |ηstart| = 4.9, ΔηF ≥ 3.0
 Currently employing two separate strategies for analysis
 2010 period A-B (∫L dt ≈ 7 nb-1) triggering on L1_J5 or L1_FJ5
 2010 period A-F (∫L dt ≈ 3 pb-1) with pT-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 zIP using E±pz method based on fwd gap side IP (ξ)
( E i  pzi ) X
 
s
~
( E i  p zi ) jets

~
z IP 
( E i  p zi ) X
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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
pTjet > 20 GeV
 Hard dijets → bigger MX → 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
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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
Diff. csx in ΔηF, (no noise supp.), Period B v F
Diff. csx in ΔηF (no noise supp.) – Comp. to MC
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ξ and zIP
 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
Truth v recon ξ using Pomwig
Truth v recon zIP using Herwig++
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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)
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Inclusive dijet s : 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
Gap-size distribution
Different trigger strategies
ATL-COM-PHYS-2011-738
2010 inclusive dijet x-sec measurment
Big OR of jet triggers:
J20 || J30 || J35 || J50 || J75 || FJ30 || FJ50
No triggers asked for
7700 events (no triggers) vs. 40 (SM2010 triggers)
=> a lot of room for trigger strategy improvement
Large gaps require collecting events with small pT
→ we have to move below 99% trigger efficiency plateau