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Underlying Event Studies at CDF

Underlying Event Studies at CDF. Stefano Lami The Rockefeller University. The “Underlying Event” in Hard Scattering Processes. Min-Bias. Min-Bias.

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Underlying Event Studies at CDF

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  1. Underlying Event Studies at CDF Stefano Lami The Rockefeller University

  2. The “Underlying Event” inHard Scattering Processes Min-Bias Min-Bias • Tevatron Collider: most of collisions are ``Soft’’, outgoing particles roughly in the same direction as initial proton and antiproton. • Occasional ``Hard’’ interaction results in large transverse momentum outgoing partons. • The ``Underlying Event’’ is everything but the two outgoing Jets, including : initial/final gluon radiation beam-beam remnants secondary semi-hard interactions • UE = unavoidable background to be removed from the jets before comparing to NLO QCD predictions

  3. The Underlying Event Precise Jet measurements requires accurate modeling of the UE. The physics of the UE is complicated and involves both pQCD and non-pQCD. None of the QCD Monte-Carlo models correctly describes the properties of the underlying event Can we tune the QCD MonteCarlo models to fit collider data? Are Minimum-Bias events a good approximation to the Underlying Event? (The beam-beam remnants in the underlying event are color connected to the hard component) Pythia uses Multiple Parton Interactions to enhance the underlying event. (MPI more likely in a hard (central) collision!)

  4. Run I Results Charged particle tracks in Central Tracking to study low momenta First study: Cone analysis for 50-300 GeV Jets Sensitive to UE and NLO perturbative corrections MAX/MIN cones Sensitive to UE only • Sum the PT of charged particles in two cones of radius 0.7 at the same h as the leading jet but with |DF| = 90o. • Plot the cone with the maximum and minimumPTsum versus the ET of the leading (calorimeter) jet.

  5. 0.4 GeV/c The MAX cone increases with increasing ET of the leading jet The MIN cone stays flat The MIN cone constant at a level similar to that found in Min-Bias events. HERWIG agrees well with Jet data, but does not reproduce well Min-bias events (lack of semi-hard physics) PYTHIA parameters can be tuned to fit the data (PT0=2 GeV cut-offfor multiple parton scattering) 50 < ET(jet1) < 300 GeV/c

  6. Run I Results Second study: 0.5 - 50 GeV Charged particle Jets(PRD65:09202, 2002) • Study only the charged particle components of jets: charged particle jets • Min-bias and Jet20 data • Compare to Monte-Carlo models: HERWIG, ISAJET, and PYTHIA Use simple, non-standard, jet definition with R=0.7: • Assign all charged particles (PT> 0.5 GeV/c, |h|<1) to a jet • Jets contain particles from the UE as well as from outgoing partons • Even one charged particle can be jet 6 particles 5 ‘jets’

  7. Charged Particle Df Correlations PT > 0.5 GeV/c |h| < 1 • Look at charged particle correlations in the azimuthal angle Df relative to the leading charged particle jet. • Define |Df| < 60o as “Toward”, 60o < |Df| < 120o as “Transverse”, and | Df | > 120o as “Away”. • All three regions have same size in h-f space, DhxDf=2x120o=4p/3. Very sensitive to the “underlying event”

  8. Charged Multiplicity versus PT(chgjet1) Jet Data • Data on the average number of “toward” (|Df|<60o), “transverse” (60<|Df|<120o), and “away” (|Df|>120o) charged particles (PT > 0.5 GeV, |h| < 1, including jet#1) as a function of the transverse momentum of the leading charged particle jet. Each point corresponds to the <Nchg> in a 1 GeV bin. The solid (open) points are the Min-Bias (JET20) data. The errors on the (uncorrected) data include both statistical and correlated systematic uncertainties. Min-Bias Underlying Event “plateau” Factor of 2 more active than an average Min-Bias event!

  9. “Transverse” PT Distribution • Comparison of the “transverse” <Nchg> versus PT(charged jet#1) with the PT distribution of the “transverse” <Nchg>, dNchg/dPT. The integral of dNchg/dPT is the “transverse” <Nchg>. Shows how the “transverse” <Nchg> is distributed in PT. PT(charged jet#1) > 30 GeV/c “Transverse” <Nchg> = 2.3 PT(charged jet#1) > 5 GeV/c “Transverse” <Nchg> = 2.2

  10. “Max/Min Transverse” Nchg versus PT(chgjet1) More sensitive to the “hard scattering” component “TransMAX” “TransMIN” More sensitive to the “beam-beam remnants” The charged particle jets in the Min-Bias data are a smooth continuation of the high PT charged jets observed in the Jet20 data. Herwig does not have enough activity in the Transverse region Data PTsum for Max/Min transverse regions in agreement with first study, once normalized to different area.

  11. Run I “transverse”data compared to Models ISAJET 7.32 has a lot of activity in the transverse region, but with the wrong dependence on PT(chgj1) Outgoing Jets plus Initial & Final-State Radiation Beam-Beam Remnants PYTHIA 6.206 PYTHIA default parameters give very poor description of the Underlying Event

  12. Tuned PYTHIA 6.206 Average number of Charged tracks in the “Transverse” region vs PT(leading jet) compared to QCD hard scattering predictions of two tuned versions of PYTHIA 6.206 (CTEQ5L). Multiple Parton Interactions with varying impact parameter, double Gaussian matter distribution and smooth turn-off PT0 Bulk of Min-Bias events! Can describe transition between “soft” and “hard” regime! PYTHIA 6.206 CTEQ5L

  13. The “Underlying Event” in Run II Center of mass Energy from 1.8 to 1.96 TeV New Central Tracking, Plug Calorimeter, Electronics • Repeat Run I analysis on Charged particle jets • Same Run I track selection (and <2 vertices) • Same PYTHIA version tuned on Run I data • Min-Bias and Jet data (~85 pb-1 for jet triggers so far)

  14. The “Underlying Event” in Run II Published CDF Run I data on the average density of charged particles in the ``transverse region’’ dN/ dhdf vs PT(leading jet) Excellent agreement between Run I and Run II PYTHIA tuned to fit Run I data

  15. The “Underlying Event” in Run II Average ``transverse’’ charged PTsum density (PT > 0.5 GeV, |h| < 1) as a function of the transverse momentum of the leading charged particle jet.

  16. Conclusions • Combining Minimum Bias and Jet CDF data gives a quantitative study of the underlying event from very soft collisions to very hard collisions. • Studies of the underlying event at CDF have revealed inadequacies of some MonteCarlo generators and have led to improved tuning. • Tuned PYTHIA (with multiple parton interactions) does a good job in describing the underlying event in CDF data. • Run I and Run II data show an excellent agreement for charged particles. The underlying event is the same in Run II as in Run I but now we can study the evolution out to much higher energies.

  17. Tuned PYTHIA 6.206 vs HERWIG 6.4 “TransMAX/MIN” vs PT(chgjet1) <Nchg> • Plots shows data on the “transMAX/MIN” <Nchg> and “transMAX/MIN” <PTsum> vs PT(chgjet#1). The solid (open) points are the Min-Bias (JET20) data. • The data are compared with the QCD Monte-Carlo predictions of HERWIG 6.4 (CTEQ5L, PT(hard) > 3 GeV/c) and two tuned versions of PYTHIA 6.206 (PT(hard) > 0, CTEQ5L, PARP(67)=1 and PARP(67)=4). <PTsum>

  18. “Transverse” PT Distribution • Run I average number of charged particles per unit PT –dh-df, dNchg/dPTdhdf. The open squares correspond to Min-Bias collisions, the solid circles (squares) correspond to events with PT(chgjet#1)>5 GeV (> 30 GeV).

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