Tracks and double partons Lee Pondrom 6 February 2012

Jet 20 data Use tracks to look at two vertices Use Rick Field’s averages to characterize the track patterns.

Jet20 data two vertices

Jet20 data two vertices

Cuts to the data ET jet1>15 GeV |Zvtx1-Zvtx2|>10 cm |trkvtx-Zvtx|<1 cm Track pT>.5 GeV |track η|<1 and |jet η|<1 both jets. For Rick’s ‘transverse track’ requirement, use jet1 to define the Δφ region.

Rick Fields definitions CDF Run 1 Analysis Charged Particle Df Correlations PT > PTmin |h| < hcut Leading Calorimeter Jet or Leading Charged Particle Jet or Leading Charged Particle or Z-Boson “Transverse” region very sensitive to the “underlying event”! Look at charged particle correlations in the azimuthal angle Df relative to a leading object (i.e. CaloJet#1, ChgJet#1, PTmax, Z-boson). For CDF PTmin = 0.5 GeV/c hcut = 1. Define |Df| < 60o as “Toward”, 60o < |Df| < 120o as “Transverse”, and |Df| > 120o as “Away”. All three regions have the same area in h-f space, Dh×Df = 2hcut×120o = 2hcut×2p/3. Construct densities by dividing by the area in h-f space.

RField’s Z transverse data X X X X

Dijet energies and φ correlation

Track multiplicity and sum pT

Jet20 data compared to Pythia

Jet20 data compared to Pythia

Jet20 data trigger vertex tracks compared to second vertex tracks

Jet20 data compared to Pythia on the main vertex

Jet20 data compared to Pythia on the secondary vertex

Comparison of transverse N and ∑pT RField, Jet20 data, and Pythia Leading ‘jet’ ET=28 GeV RField Jet20 data Pythia trig vtx trig vtx 2ndvtx trig vtx 2ndvtx N 0.6 .61±.01 .48±.01 .53±.01 .44±.02 ∑pT 0.7 .72±.01 .46±.01 .69±.01 .41±.02 pTmax 1.6 1.66±.02 .97±.03 1.53±.03 .92±.03 pT in GeV, errors are statistical only. Field’s numbers are read from slides 7,8, and 9.

The ‘away’ jet Rick’s slide 10 shows away jet <N> =1.4 Jet20 data away jet <N>=1.8±.02 (stat) Jet20 Pythia away jet <N>=2.1±.04 (stat). So there seems to be more track activity in jet20 than for comparable ET opposite a Z

Conclusions from the 1vtx-2vtx study Jet20 data may be slightly more active than Pythia is. But the agreement is good on each of the two vertices. The trigger vertex with the 20 GeV ET jets has more track activity in the transverse region than does the second vertex. Each track activity is non-perturbative.

Energy dependence Run the tracking program over jet50, jet70, and jet100 data sets, together with the corresponding Pythia files.

Good event cross sections Only one good vertex Less than 500 tracks jet1ET0 (shifted to the CDF origin)>20,50,70,100. |jet1η|<1 and |jet2η|<1

Good event cross sections Data σ(good events) nb prescale corrected σ nb Jet20 0.5 nb 2000 1000 nb Jet50 0.2 nb 100 20 nb Jet70 0.77 nb 8 6 nb Jet100 1.4 nb 1 1.4 nb Jet20 cross section ~1 b; σ(inelastic) ~ 60 mb, and σ(CLC)=36 mb (CDF6314) so Jet20/σ(CLC)~2.8E-5 The CLC cross section is ‘min bias’. So about 1/36000 of minbias events looks like jet20. Our second vertex σ is smaller than 36 mb.

ET scan data jet20 black,jet50 red,jet70 green,jet100 blue

ET scan data normalized to same Ldt = 12208 nb-1

Good event cross sections only one vertex, at least two jets Trigger Ldt events prescale σ(nb) Jet20 12208/nb 6617 2000 1080 Jet50 43363/nb 9258 100 21.3 Jet70 19269/nb 14787 8 6.1 Jet100 14572/nb 20775 1 1.4 Prescales checked by comparing normalized yields

Jet20,50,70,100 triggers, and jet20-50 relative prescale = 20

Check prescales jet50=100,jet70=8,jet100=1

ET scan data

Data ET scan jet20 black,jet50 red, jet70 green, jet100 blue

Data Averages, transverse tracks <ET> GeV d<n>/dηdφ d<∑pT>/dηdφ 28. 0.61±0.01 0.72±0.01 66. 0.65±0.01 0.86±0.01 91. 0.68±0.005 0.97±0.01 128. 0.67±0.005 1.03±0.01 Statistical errors only. These numbers agree well with RField’s transverse numbers defining ET as the pT of the lepton pair in Z->ll.

Pythia transverse track ET scan

Pythia averages transverse tracks <ET> GeV d<n>/dηdφ d<∑pT>/dηdφ 27.2 0.53±0.01 0.62±0.01 64.6 0.57±0.01 0.78±0.01 88. 0.60±0.01 0.87±0.01 124. 0.64±0.01 1.01±0.01 Statistical errors only. Numbers are systematically 10% smaller than the data, but show the same trends with increasing <ET>.

Conclusions from the energy dependence For the transverse tracks <N> and <∑pT> change very little from 25 GeV to 125 GeV. This implies that the double parton component, which should fill in this part of phase space, is very rare indeed. Maybe trans track ∑pT >20 GeV can serve as a ‘trigger’ for double parton content.

Jet100 events with high transverse ∑pT Jet 100 data Total events events passing all cuts ∑pT>20 GeV 500,000 100331 3541 About 3.5% of the jet100 ‘dijets’ have high pT transverse track activity PYTHIA 1E6 87661 3338

0.5E6 jet100 data and 1E6 PYTHIA jet100 data PYTHIA At least 2 jets 100331 87661 At least 3 jets 48183 37720 At least 4 jets 13253 8557 ∑pT>20GeV 3541 3338 +3jets 3468 3242 +4jets 1747 1489 Jets 3 and 4 ET>5 GeV

Jet100 data compared to Pythia ET jets 1 and 2

Jet100 data compared to Pythia ET jets 3 and 4

Jet100 data and Pythia ET jet 3 and 4 for trans track ∑pT>20 GeV

Jet100 and Pythia Δφ distributions-all events and trans track ∑pT>20 GeV

Δφ_13

Data-Pythia Δφ_14 all events and trans track ∑pT>20 GeV

Δφ_23

Δφ_24

Δφ_34

Jet100 and jet70 Δφ34

Jet50 and jet20 Δφ34

Excess near Δφ_34 =  jet100 Δφ_34 from 2.5 radians to 3.2 radians Data trans tracks ∑pT>20 GeV = 578 background = 443 ‘double parton signal’ = 135±24, 0.13% of all events, or 1.3E-3. Pythia trans tracks ∑pT>20 GeV = 438 background = 386 ‘double parton signal’ = 52±21.

‘dp’ signal? From the cross section measurements (slide 23), if every event has a lurking minbias background, then for ~2.8E-5 of them the minbias morphs into jet20. The transverse track ∑pT>20 GeV requirement produces jets which approximate jet20 at a rate of 1.3E-3, 50X what is expected from the above argument.

Summary of energy scan data File trig tot evts good evts trans trk ∑pT>20 GeV gjt4bk jet100 0.5E6 100331 3541 gjt3bk jet70 1.0E6 130860 3636 gjt2bk jet50 1.0E6 82135 1526 gjt1bk jet20 1.5E6 70383 360 trk ∑pT>20/good evts ‘double parton signal’ Jet100 3.5% (0.135±0.024)% Jet70 2.8% (0.15±0.02)% Jet50 1.8 % (0.053±0.018)% Jet20 0.5% (-0.01±0.01)%

Results of energy scan Excess at Δφ34 ~ increases with jet ET. One would naively expect ‘double partons’ to be energy independent. Jet20 is too low to show any effect for a cut on scalar sum of transverse track pT > 20 GeV. Does lowering the cut to 15 GeV change anything?

Scalar ∑pT>15 GeV applied to jet50

‘Double parton yield for Jet50 ∑pT> Δφ34 2.5->  ‘double p’/ signal bkgnd good evnts 20 GeV 243 199 (0.053±0.018)% 15 GeV 453 353 (0.12±0.025)%

Compare ET3 and ET4 for the two ∑pT thresholds jet50

Compare the two thresholds for jet100, ET 3 & 4

∑pT>15 GeV compared to ∑pT>20 GeV for jet100-Δφ34

Small increase in ‘double parton’ yield for Jet100 Trans track scalar ∑pT >15 GeV Δφ34 excess from 2.4 radians to  = (0.163±0.022)% Trans track scalar ∑pT>20 GeV Δφ34 excess from 2.4 radians to  = (0.151±0.024)%

Cut on Δφ34>2.4 radians to enhance the effect

ET for jets 3 and 4 before and after the Δφ34>2.4 radians cut

Effect of the Δφ34>2.4 cut on Δφ12

Δφ34>2.4 radians for scalarsumpT>15 GeV

Look again at the second vertex The idea is to use the trans track ∑pT>15 GeV as a ‘trigger’ on the minbias second vertex. Transverse is defined by the main jet on the first vertex, here jet100. A small number of second vertices will satisfy ∑pT>15 GeV, and will have two low ET jets, with perhaps Δφ34~. These two effects then merge to form the ‘dp’ component of the single vertex 4 jets.

Second vertex cross section File gjt4bk jet100 triggers Exactly 1 vertex with at least two jets: σ=1.4 nb Exactly 2 vertices,1 with at least two jets: σ=0.33nb Poisson statistics Pr{1}/Pr{0}=<n>=.24 <n>=σL. =396E-9;L=2.0E32; σ=3 mb cross section for detection of vtx2 CLC σ=36 mb. So our definition of vtx2 is 10%.

1vtx and 2vtx results

Compare 2vtx with 1vtx, both ∑trackpT>15 GeV, 1vtxΔφ34>2.4

JetET3 is lower on the second vertex Requiring scalar∑trans tracks pT>15 GeV on first vertex or second vertex, jetET4 looks the same. jetET3 on second vertex looks like jet20 data rather than jet100, indicating that the problem is more complicated than one might naively think.

1vtx-2vtx comparisons Δφ

1vtx-2vtx comparisons Δφ

1vtx-2vtx comparisons Δφ

Δφ34 with and without scalar∑pT>15 GeV

Summary of this exercise Δφ 2nd vtx -> to 1st vtx with Δφ34>2.4 12 sharpens up- looks more like 2 independent sets of jets 13 looks flatter on 2nd vtx 14 looks good 23 looks flatter on 2nd vtx 24 looks good

Summary of this exercise On the whole, the cut in Δφ seems to work-it makes 1vtx look more like 2vtx, where the jet pairs are indeed independent, except of course that there is only one calorimeter. Probably doesn’t prove anything, but is suggestive. Take a look at Pythia. We have 1E6 events total

Compare Pythia to jet100 data with ∑trackpT>15 Gev and Δφ34>2 Compare Pythia to jet100 data with ∑trackpT>15 Gev and Δφ34>2.4rad

Δφ35 without and with track∑pT>15 GeV, data and Pythia

Δφ34 comparison The φ correlation between the 3rd and 4th jets is interpreted as a signature for ‘dp’ scattering. We look for Δφ34~ in the sample where scalar track∑pT>15 serves as a trigger of increased activity in the transverse region. The previous plots show that data and Pythia are statistically compatible, but the data have a larger excess in Δφ34~.

Compare Δφ distributions Data and Pythia

Δφ distributions data and Pythia

Δφ Distributions data and Pythia

Δφ34 histograms Pythia and data Comparing Δφ34 for all events and for events with scalar track ∑pT>15 GeV gives the following ‘double parton’ signals: Jet100 data Δφ34>2.4 rad ‘signal’=(1.6±0.2)E-3 Pythia Δφ34>2.4 rad ‘signal’=(5.6±2.8)E-4 About 1/3 smaller than the data, although the other angular distributions for Δφ34>2.4 rad look the same.

What is going on? I have no idea, but it looks to me that what we are looking for, namely the merger of two hard scatters into one event, does not exist. It is impressive that Pythia does as well as it does, given the manipulations on the events necessary to do this study. I could ask for a Pythia run with MSTP(82)=0, which would turn off MPI.

Stntuples for Z->+- There are 5E8 high pt muon triggers Yield estimates: 80<M<100GeV = 1.2E6 events pTZ>20 GeV = 2.5E5 events Transtrack ∑pT >15 GeV = 7500 events Should be enough to work with, but will require entire data sample. Z->e+e- Stntuples should be comparable.

First look at high pT muon data compare to jet20

5E6 triggers bhmubi stntuple file compare to jet20

High pT muon stntuple