Dijet Azimuthal Distributions at D

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Dijet Azimuthal Distributions at DØ

• Amnon Harelaharel_at_fnal.gov
• University of Rochester
• for the DØ Collaboration.

Joint Meeting of Pacific Region Particle Physics
CommunitiesQCD SessionHonolulu, October 29th
November 3rd 2006
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The Observable
Jets ordered by pT

• Lowest order in pQCD back-to-back dijets
• Jets with equal pT and ?Fp

• Additional soft radiation can cause small
  azimuthal decorrelations
• Different pTs ?F slightly less than p

• Additional hard radiation can lead to an
  additional observable jet and to more
  decorrelation
• ?Fltp

Tests O(as)4 calculations NLO LO
It is not necessary to reconstruct the additional
jets in order to probe higher orders!
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The Jets

• High energy collisions result in collimated
  particles, which are clustered into jets with
  various jet algorithms.
• Used the Run II Midpoint Cone Algorithm with
  ?R0.7 at all levels.
• seed based
• midpoint seeds give infrared safety.
• iterative
• The calorimeter jet energies are corrected to
  the particle level by scaling and unsmearing.

CH
hadrons
Calorimeter jet
FH
?
EM
particle jet
Time
parton jet
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The Data
Azimuthal correlations stronger at high pT

• Integrated Luminosity 150pb-1 (first 14 months
  of RunIIa, )
• The analysis is not statistics dominated.
• Four leading jet pT bins were used, each
  corresponds to a trigger that is at least 99
  efficient there.
• The 2nd leading jet pTgt40GeV.
• Both leading jets are central ylt0.5
• Quality cuts
• require a central, high quality primary vertex
• veto high MET (cosmics)
• good running conditions
• jet ID to reduce electronics noise, etc.

PRL 94, 221801 (2005)
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Selected Experimental Issues

• Measuring a ratio reduces uncertainties (also
  theoretical ones).
• Correcting the result to the particle level
• Unfolded using MC (Pythia) with highly
  parametrized smearing
• Angular resolution (better than 20mrad)
• pT resolution effects are indirect but bigger,
  especially at ?Flt2.8 where they can change which
  jet is the 2nd leading one.
• Similarly, evaluated the uncertainties due to
  residual dependencies (in ? and F) in the jet
  energy scale that may lead to choosing a
  different 2nd leading jet.
• Additional pp interactions per bunch crossing are
  negligible.
• calculated from cross sections and verified with
  P.V.s
• Fake jets are most apparent at ?F½p, verified
  that they were all removed.

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Non-Pertubative Effects
? Non pertubative effects are less than 5
This simplifies comparisons with pQCD
calculations,and makes this measurement
extremely useful for tuning pQCD in Monte Carlo
simulators.
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Data vs. Calculations
as3 Diverges due to missing soft processes
as3 Only three partons
as4 Resummation Needed
as4 Is only LO here ? Larger S.F. uncertainties
as4 Is only LO here
PRL 94, 221801 (2005)
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Data vs. Pythia and Herwig

• 2?2 Hard Processes, 3rd and 4th jets are produced
  by parton showers.
• HERWIG describes the data well.
• PYTHIA
• Default not enough jets with low ?F.
• Pythia provides many tuning handles, which ones
  should we use?
• Insensitive to non-pertubative effects.
• More ISR can help

PRL 94, 221801 (2005)
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Tuning Pythia
Parameters that increase Final State Radiation
Parameters that increaseInitial State Radiation
Agreement still not perfect at high ?F
Increased virtuality PARP(67)1?2.5
Zoomed in to right quarter
Zoomed in to right half
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Matched MC
Good at generating hard, large-angle processes
(calculates interference)
LO calculations for 2?N hard processes
Matrix element generator Alpgen / Sherpa
Partons are matched to parton-shower jets to
avoid double counting of equivalent phase space
configurations.
Weak on the texture of the QCD radiation
Good at generating the details within a jet
Only 2-gt2 hard processes
Parton shower generator Pythia / Herwig
Multijet events dont describe data well (and
are hard to generate)
Resummed soft, collinear radiation.
Detector simulation

• Dijet ?F is a great testing ground for MC
  matching
• Only jets
• Explore many-jet final states while
  reconstructing only two of them.

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MC Matching
Alpgen uses the MLM matching scheme. Can use
either Pythia or Herwig for the parton showers.
Sherpa uses CKKW matching and its own parton
shower mechanism. Phase space is partitioned by
number of partons. The partitions are arbitrary,
not physical! They need to be added up (with the
right weights) to recover any physical
prediction. Reference Chapter 4.1 of TeV4LHC
QCD Group Report, hep-ph/0610012, by Begel,
Wobisch Zielinski
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Data vs. Matched MC
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More Work on MC Tunes

• Rick Field produced a new global Pythia tune, the
  DW tune, that incorporates the ISR tuning
  suggested by this measurement.
• See Chapter 4.2 of TeV4LHC QCD Group Report,
  hep-ph/0610012, by R. Field.
• It was shown that the measurement is insensitive
  to the multiparton interaction model
• In Pythia the model was changed (MSTP(82)4).
• Added multiparton interactions to HERWIG using
  JIMMY.
• See Preparing for measurements of dijet
  azimuthal decorrelations at ATLAS,
  ATL-PHYS-PUB-2006-013, by A. Moraes et. al.

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Future DØ Plans

• Redoing the analyses with
• Increased data set 7 times the luminosity
• Vastly improved jet energy scale
• systematic uncertainties reduced by a factor of
  5!
• better coverage of forward regions
• Better understanding of the detector may improve
  other systematics
• May add the forward regions sensitive to BFKL
  predictions.
• Though this measurement requires little
  statistics and is fairly insensitive to
  uncertainties on the jet energy scale, dramatic
  improvements are possible for both. As they were
  the two leading experimental uncertainties, we
  expect to achieve a significantly more precise
  measurement.

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?F at the LHC

• The tuned MC should prove useful at the LHC
• This is a good week 2 measurement.
• Can make a useful measurement
• even with a partially calibrated detector (e.g.
  at cell level)
• even with a very rough jet energy scale that is
  only for central jets
• even with little statistics
• Use of leading jets prevents problems with
  multiple interactions.
• ATLAS is preparing to do it.

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• Back up slides

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Unsmearing Corrections
The correction is the ratio of the number of MC
events (per bin) before smearing to the number
after smearing
pT smearing dominates below 2.8
?F smearing dominates near p
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The DØ Detector
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MC vs. Calculation