Pt spectra for the First Pass and Second Pass jets
4.3 Underlying Event Tunes for the LHC Contributed by: Moraes
Hard interactions at hadron-hadron colliders consist of a hard collision of two incoming partons along with softer interactions from the remaining partons in the colliding hadrons (“the underlying event energy”). Minimum bias events are the type of events which would be observed with a very inclusive experimental trigger, and consist primarily of the softer interactions resulting from the collision of two hadrons. What is meant by a minimum bias event is somewhat murky, and the exact definition will de-pend on the trigger of each experiment. The description of the underlying event energy and of minimum bias events requires a non-perturbative phenomenological model. There are currently a number of mod-els available, primarily inside parton shower Monte Carlo programs, to predict both of these processes.
We discuss several of the popular models below. An understanding of this soft physics is interesting in its own right but is also essential for precision measurements of hard interactions where the soft physics effects need to be subtracted. This is true at the Tevatron and will be even more so at the LHC where the high luminosity running will bring a large number of additional minimum bias interactions per crossing.
Perhaps the simplest model for the underlying event is the uncorrelated soft scattering model present in HERWIG. Basically, the model is a parametrization of the minimum bias data taken by the UA5 experiment at the CERN pp Collider. The model tends to predict underlying event distributions softer than measured at the Tevatron and has a questionable extrapolation to higher center-of-mass en-ergies. A newer model for the underlying event in HERWIG is termed JIMMY and describes the underlying event in terms of multiple parton interactions at a scale lower than the hard scale and with the number of such parton scatterings depending on the impact parameter overlap of the two colliding hadrons.
JIMMY 4.1 linked to HERWIG 6.507 has been tuned to describe the underlying event as mea-sured by CDF [104, 117] and the resulting set of parameters, labeled UE, is shown in Table 4.3.8. The tuned settings were obtained for CTEQ6L. The default parameters are also included in table 4.3.8 for comparison. JMRAD(75) should also be changed to the same value used for JMRAD(73) when
antipro-Default JIMMY 4.1 - UE Comments
JMUEO=1 JMUEO=1 multiparton interaction
model
PTMIN=10.0 PTMIN=10.0 minimum pTin
hadronic jet production PTJIM=3.0 PTJIM=2.8 × √s
1.8 TeV
0.274
minimum pTof secondary scatters when JMUEO=1 or 2
JMRAD(73)=0.71 JMRAD(73)=1.8 inverse proton
radius squared
PRSOF=1.0 PRSOF=0.0 probability of a soft
underlying event
Table 4.3.8: JIMMY 4.1 default and UE parameters for the underlying event.
tons are used in the simulation (e.g. Tevatron events).
Notice that an energy dependent term has been introduced in PTJIM for the UE tuning. This leads to a value of PTJIM=2.1 for collisions at√
s = 630 GeV and PTJIM=4.9 for the LHC centre-of-mass energy in pp collisions.
The PYTHIA model for the underlying event also utilizes a multiple parton interaction framework with the total rate for parton-parton interactions assumed to be given by perturbative QCD. A cutoff, pT min, is introduced to regularize the divergence as the transverse momentum of the scattering goes to zero. The rate for multiple parton interactions depend strongly on the value of the gluon distribution at lowx. The cutoff, pT min, is the main free parameter of the model and basically corresponds to an inverse color screening distance. A tuning of the PYTHIA underlying event parameters (Tune A) was discussed earlier and basically succeeds in describing all of the global event properties in events at the Tevatron. With the new version of PYTHIA (version 6.3), a new model for the underlying event is available, similar in spirit to the old multiple parton interaction model but with more attention being a more sophisticated treatment of color, flavor and momentum correlations in the remnants. Table 4.3.9 shows the relevant PYTHIA 6.3 parameters tuned to the underlying event data [104, 117]. For the purpose of comparison, the corresponding default values in PYTHIA 6.323 [75] are also shown.
Parameter Default UE Comment
MSTP(51) 7 (5L) 10042 (6L) CTEQ PDF
MSTP(52) 2
MSTP(68) 3 1 max. virtuality scale
and ME matching for ISR
MSTP(70) 1 2 regul. scheme for ISR
MSTP(82) 3 4 complex scenario and double
Gaussian matter distribution
PARP(82) 2.0 2.6 ptminparameter
PARP(84) 0.4 0.3 hadronic core radius
(only for MSTP(82)=4) PARP(89) 1.8 1.8 energy scale (TeV) used to
calculate ptmin
PARP(90) 0.25 0.24 power of the ptmin
energy dependence
Table 4.3.9: PYTHIA 6.323 default [75] and UE parameters.
Predictions vs. underlying event data
Based on CDF measurements [104], the UE is defined as the angular region inφ which is transverse to the leading charged particle jet.
Figure 4.3.56 shows JIMMY 4.1 - UE (table 4.3.8) and PYTHIA 6.323 - UE (table 4.3.9) pre-dictions for the underlying event compared to CDF data [104] for the average charged particle (pt >
0.5 GeV and |η| < 1) multiplicity (a) and the average ptsum in the underlying event (b). Distributions generated with PYTHIA 6.2 - Tune A are also included in the plots for comparison. There is a
reason-2 4 6 8
CDF data
PYTHIA6.2 - Tune A (CTEQ5L) JIMMY4.1 - UE (CTEQ6L) PYTHIA6.323 - UE (CTEQ6L)
< Nchg> - transverse region
0 1 2
0 10 20 30 40 50
Ptleading jet (GeV)
Ratio (MC/data)
(a)
2 4 6 8
CDF data
PYTHIA6.2 - Tune A (CTEQ5L) JIMMY4.1 - UE (CTEQ6L) PYTHIA6.323 - UE (CTEQ6L)
< Ptsum> - transverse region
0 1 2
0 10 20 30 40 50
Ptleading jet (GeV)
Ratio (MC/data)
(b)
Fig. 4.3.56: PYTHIA 6.2 - Tune A, PYTHIA 6.323 - UE and JIMMY 4.1 - UE predictions for the underlying event compared to CDF data: (a) Average charged particles multiplicity and (b) average ptsum in the underlying event.
ably good agreement between the proposed tunings and the data. The distribution shapes are slightly different in the region of Ptljet .15 GeV. PYTHIA 6.323 - UE underestimates the data while JIMMY 4.1 - UE overestimates it.
Another measurement of the underlying event was made by defining two cones inη − φ space, at the same pseudorapidity η as the leading ET jet (calorimeter jet) and±90◦in the azimuthal direction,φ [117]. The total charged track momentum inside each of the two cones is then measured and the higher of the two values defines the “MAX” cone, with the remaining cone being labeled “MIN” cone. Figure 4.3.57 shows PYTHIA 6.323 - UE predictions for the UE compared to CDF data [117] for the< pt >
of charged particles in the MAX and MIN cones for pp collisions at (a)√
s = 630 GeV and (b) 1.8 TeV.
JIMMY 4.1 - UE predictions are compared to the data in fig. 4.3.58. Both tunings describe the data with good agreement, however, this only became possible by tuning the parameters of the minimum ptcut-off to include the correct energy dependence in both generators (PARP(82), (89) and (90) for PYTHIA 6.3 and PTJIM for JIMMY 4.1).
Tuning the JIMMY parameter PTJIM to include an energy dependent factor made it possible to describe the MAX-MIN< pt > distributions at different energies. Just to illustrate what would be the result of not adding the energy dependent factor in PTJIM, in fig. 4.3.59, JIMMY4.1 with PTJIM fixed by comparisons to the √
s = 1.8 TeV distributions to PTJIM=2.8, is compared to the√
s = 630 GeV MAX-MIN data. The predictions underestimate the data, indicating that PTJIM has to be reduced in order to describe the data.
The agreement between predictions and data seen in figs. 4.3.57 and 4.3.58 for the < pt > in MAX and MIN cones is not reproduced in the comparisons of< Nchg > distributions for pp collisions
0 0.5 1 1.5 2 2.5 3
20 30 40 50 60 70 80
PYTHIA6.323 - UE - Min cone PYTHIA6.323 - UE - Max cone
pp interactions - √s = 630 GeV
-ETleading jet (GeV)
< PT > (GeV/c)
(a)
0 1 2 3 4 5 6
50 100 150 200 250
PYTHIA6.323 - UE - Min cone PYTHIA6.323 - UE - Max cone pp interactions - √s = 1.8 TeV
-ETleading jet (GeV)
< PT > (GeV/c)
(b)
Fig. 4.3.57: PYTHIA 6.323 - UE predictions for the underlying event compared to the< pt> in MAX and MIN cones for (a) pp collisions at√
s = 630 GeV and (b) 1.8 TeV.
at√
s = 1.8 TeV shown in fig. 4.3.60. There is no data available for the< Nchg > distributions for pp collisions at√
s = 630 GeV. Both PYTHIA 6.323 - UE and JIMMY 4.1 - UE overestimate the data.
This indicates that neither model is describing the ratio < pt >/< Nchg > as seen in the data. This certainly needs to be improved in future tunings.
LHC predictions
Some predictions for the underlying event energy at the LHC are shown in Fig. 4.3.61. It shows PYTHIA 6.323 - UE (table 4.3.9), JIMMY 4.1 - UE (table 4.3.8) and PYTHIA 6.2 - Tune A pre-dictions for the average multiplicity in the underlying event for LHC pp collisions. The CDF data (pp collisions at√
s = 1.8 TeV.) for the average multiplicity in the UE is also included in fig. 4.3.61.
A close inspection of predictions for the underlying event given in fig. 4.3.61 shows that the average charged particle multiplicity in the underlying event for leading jets with Ptljet > 20 GeV reaches a plateau at ∼ 4.7 charged particles according to PYTHIA 6.2 - Tune A, ∼ 6 for JIMMY 4.1 - UE and∼ 7.5 according to PYTHIA 6.323 - UE. Expressed as particle densities per unit η − φ, where the underlying event phase-space is given by∆η∆φ = 4π/3 [104, 107], these multiplicities correspond to 1.12, 1.43 and 1.79 charged particles per unit η − φ (pt > 0.5 GeV), as predicted by PYTHIA 6.2 -Tune A, JIMMY 4.1 - UE, and PYTHIA 6.323 - UE, respectively. The shape of the distributions also shows significant differences between the model predictions. The shape of the multiplicity distribution generated by PYTHIA 6.323 - UE is considerably different from the other two models in the region of Ptljet .25 GeV.
It is clear that (1) all predictions lead to a substantially larger underlying event energy at the LHC than at the Tevatron and (2) there are large differences among the predictions from the various models.
Investigations are continuing trying to reduce the energy extrapolation uncertainty of these models. This measurement will be one of the first to be performed at the LHC and will be used for subsequent Monte Carlo tunings for the LHC.
0 0.5 1 1.5 2 2.5 3
20 30 40 50 60 70 80
JIMMY4.1 - UE - Min cone JIMMY4.1 - UE - Max cone
pp interactions - √s = 630 GeV
-ETleading jet (GeV)
< PT > (GeV/c)
(a)
0 1 2 3 4 5 6
50 100 150 200 250
JIMMY4.1 - UE - Min cone JIMMY4.1 - UE - Max cone pp interactions - √s = 1.8 TeV
-ETleading jet (GeV)
< PT > (GeV/c)
(b)
Fig. 4.3.58: JIMMY 4.1 - UE predictions for the underlying event compared to the< pt> in MAX and MIN cones for (a) pp collisions at√
s = 630 GeV and (b) 1.8 TeV.
0 0.5 1 1.5 2 2.5 3
20 30 40 50 60 70 80
JIMMY4.1 (PTJIM=2.8) - Min cone JIMMY4.1 (PTJIM=2.8) - Max cone
pp interactions - √s = 630 GeV
-ETleading jet (GeV)
< P T> (GeV/c)
Fig. 4.3.59: JIMMY 4.1 - PTJIM=2.8 (fixed for comparisons at√
s = 1.8 TeV), JMRAD(73,75)=1.8 - predictions for the UE compared to the< pt> in MAX and MIN cones for pp collisions at√
s = 630 GeV.
0 0.5 1 1.5 2 2.5 3 3.5
0 50 100 150 200 250
PYTHIA6.323 - UE - Min cone PYTHIA6.323 - UE - Max cone
pp interactions - √s = 1.8 TeV
-ETleading jet (GeV)
< Nchg>
(a)
0 0.5 1 1.5 2 2.5 3 3.5
0 50 100 150 200 250
JIMMY4.1 - UE - Min cone JIMMY4.1 - UE - Max cone
pp interactions - √s = 1.8 TeV
-ETleading jet (GeV)
< Nchg>
(b)
Fig. 4.3.60: PYTHIA 6.323 - UE (a) and JIMMY 4.1 - UE (b) predictions for the underlying event compared to the< Nchg>
in MAX and MIN cones for pp collisions at√
s = 1.8 TeV.
2 4 6 8 10 12
0 10 20 30 40 50
CDF data
PYTHIA6.2 - Tune A (CTEQ5L) JIMMY4.1 - UE (CTEQ6L) PYTHIA6.323 - UE (CTEQ6L)
LHC prediction
P
tleading jet(GeV)
< N
chg> - transverse region
Fig. 4.3.61: PYTHIA 6.2 - Tune A, JIMMY 4.1 - UE and PYTHIA 6.323 - UE predictions for the average charged multiplicity in the underlying event for LHC pp collisions.
5 Diffractive Physics
5.1 Large Multigap Diffraction at LHC