g b, g c production

1
g b, g c production

• Tara Shears, University of Liverpool

2
Motivation

• Good testbed for quantitative QCD tests
• ? c events could be used to probe charm quark
  content of proton
• bb, cc events imperfect NLO description
• ? heavy flavour sensitive to new physics (eg.
  chargino neutralino production)

3
Production

• LO

• NLO

Dominates for gc
Diagrams equal for gb
Coupling ? charge2 ? s(cg) 4.s(bg) b quark
60 c quark content of p (PRD 012003)
4
Method / data

• Method
• Apply standard run 2 g selection
• Estimate fakes from CPR info
• Use tagging algorithm for b/c id
• Run 1 used lepton pt
• We use sec. vtx tag (SECVTX will update with
  better)
• Divide data into Et bins, tag, take b,c frac.
  from MC gt s

• Datasets
• CPH108 (25 Iso trigger)
• Good online run, clc, smx, cal, trigger, svx
• Good offline cal,cot
• ?L 66.05 3.76 pb-1
• MC Pythia g jet (LO)
• Will use madgraph for NLO

5
Photon jet sel. eff.
Et bin 25-28 28-32 32-38 38-46 46-60
Central g 118703 95499 75499 45487 28659
gt1 jet 41514 39807 37410 25873 18327
fiducial 38230 36675 34153 23789 16820
had/Em 35742 34338 32085 22448 15910
chi2 34799 33340 30883 21366 14956
isolation 32797 31559 29401 20445 14354
tracks 29242 28236 26680 18704 13287
Spt 29197 28198 26640 18684 13275
2nd CES clus 28748 27746 26192 18366 13077
Eff (g jet) 24.220.12 29.050.15 34.690.17 40.380.23 46.320.29
Require at least 1 jet gt 15 GeV, gt2 tracks, for
tagging
6
Tagging eff. composition
Et bin 25-28 28-32 32-38 38-46 46-60
E(b tag) 24.91.7 24.61.6 26.91.7 22.11.6 17.11.5
E(c tag) 4.30.3 4.80.3 5.10.3 3.70.3 2.70.3
E(uds) 0.130.02 0.130.02 0.180.02 0.180.02 0.180.03
b. frac. 0.420.02 0.410.02 0.410.02 0.460.03 0.520.04
c. frac. 0.500.03 0.520.02 0.490.02 0.420.03 0.340.03
From Pythia MC..
7
Data
Et bin 25-28 28-32 32-38 38-46 46-60
No. ggt1 jet 26415 28399 22736 13820 8680
No. tags 302 354 321 209 178
Est. no. g tags 147.850.4 168.850.4 204.052.4 216.148.0 113.935.6
s(gq) N(gq)/(eff(gjet).eff(tag).eff(trg).?L)
no.tags q fraction
Not known yet
8
Systematic errors

• Photon jet selection 1.7 from g id (test on
  Z, loose cuts)
• Jet e scale not yet done
• Fake rate est. use standard procedure
• B/c fractions take arbitrary 10
• SECVTX systematics not yet done (small for top)
• Trigger systematics not yet done
• Luminosity use quoted 5.8

9
Results 1. gb
Et bin 25-28 28-32 32-38 38-46 46-60
CPR 5.3 4.5 3.6 3.8 3.5
E(gjet) 0.1 0.1 0.1 0.1 0.1
E(tag) 1.1 1.0 0.9 1.2 1.0
Total stat. error 5.4 4.6 3.7 4.0 3.6
CPR 5.3 4.9 3.7 4.1 4.0
E(gjet) 1.1 0.9 0.7 0.7 0.4
lumi 0.9 0.9 0.8 1.0 0.7
Sys. err 5.5 5.1 3.8 4.3 4.1
s(bg) 15.6 14.7 13.6 16.9 11.3
Pythia 21.6 17.6 13.4 7.7 5.2
Nb. E(trg) 1 here!
10
Results 2. gc
Et bin 25-28 28-32 32-38 38-46 46-60
CPR 30.8 28.9 22.3 20.5 14.6
E(gjet) 0.5 0.5 0.4 0.5 0.3
E(tag) 6.3 6.0 5.0 7.5 5.2
Total stat. Err. 31.4 29.5 22.9 21.8 15.5
CPR 30.3 31.7 23.5 22.4 16.6
E(gjet) 5.2 5.6 4.2 3.9 1.7
lumi 5.2 5.5 5.0 5.3 2.7
Sys. error 31.2 32.7 24.4 23.3 16.9
s(cg) 107.4 95.3 85.6 92.0 46.8
Pythia 123.9 96.6 72.4 41.5 22.6
Nb. E(trg) 1 here!
11
Points data (stat. Error and sys?stat shown by
notches) Histogram Pythia gjet (LO)
12
Points data (stat. Error and sys?stat shown by
notches) Histogram Pythia gjet (LO)
13
Work plan

• Immediately
• Trigger efficiency (sys.)
• SECVTX tagging sys., jet selection sys.
• Other photon backgrounds (eg. e-gtg) (small)
• Add more data
• Next
• Tighten photon cuts to reduce fake, use CES
• Derive b/c fraction from data
• Use improved b/c tag
• Compare to NLO (Madgraph?)
• Longer term
• Extend to lower photon Et