recent measurement of underlying...
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Recent measurement of underlying events
13 TeV results with leading charged-particles and jets
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Wei Yang Wang National University of Singapore (NUS)
On behalf of the CMS collaboration 7th MPI
23/11/2015
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Outline
1. Underlying event observables
2. Data/MC samples
3. Event and track selections
4. Data correction
5. Systematic uncertainties
6. Results
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Underlying Event Observables
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The underlying event: ◦ Additional activity on top of the hard scattering component of the collision
Hard interaction ISR/FSR
MPI
Everything
MPI, ISR/FSR, hadronisation, colour reconnections, beam remnants, soft rescattering of beam remnants etc…
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Underlying Event Observables
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Reference hard direction Towards region: Δ𝜙 < 60°
Away region: Δ𝜙 > 120°
Transverse region: 60° < Δ𝜙 < 120°
/
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Underlying Event
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Towards region: Δ𝜙 < 60°
Away region: Δ𝜙 > 120°
Transverse region: 60° < Δ𝜙 < 120°
UE observable:
𝑁𝑐ℎ / Δ𝜂Δ Δ𝜙 , Σ𝑝𝑇 / Δ𝜂Δ Δ𝜙
TransMAX(TransMIN): activity in maximum(minimum) activity side of transverse region
TransAVE: (TransMAX+TransMIN)/2 activity
TransDIF: (TransMAX-TransMIN) activity ◦ Sensitive to ISR/FSR
MPI/BBR
ISR/FSR +MPI/BBR
Reference hard direction Leading charged-particle/jet
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Data/MC samples
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Early run 2 data:
• Lumi 281 nb−1
• Pileup of 1.3
• ZeroBias trigger
Monte Carlo samples:
◦ PYTHIA8 CUETP8M1: ◦ validation and correction (with PU 1.3)
◦ PU systematic (w/o PU)
◦ HERWIG++ CUETHS1 and EPOS v1.99: model dependency systematic
◦ PYTHIA8 Monash and CUETP8S1: comparison with corrected data
Detector response simulated with GEANT4 and events processed as with data
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Event selection ZeroBias triggered event sample with exactly (exclusive) one good vertex.
All good vertex within: ◦ 10cm of beamspot-z
◦ 𝜌 <= 2cm (relative to beamspot in xy-plane)
◦ Vertex dof > 4
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Object Selections Particle level selection
UE/Leading particles: ◦ 𝑝𝑇 ≥ 0.5 GeV
◦ 𝜂 ≤ 2
leading objects from most energetic vertex
Leading jet (SisCone, 𝑅 = 0.5): ◦ Using particles with |𝜂| < 2.5
◦ pT ≥ 1 GeV
◦ |η| ≤ 2
Detector level selection
• Same as particle level selection, done on tracks
• Track quality cuts:
• highPurity
• 𝑑𝑥𝑦/𝜎𝑑𝑥𝑦< 3.0, 𝑑𝑧/𝜎𝑑𝑧
< 3.0
• 𝜎𝑝𝑇/𝑝𝑇 < 0.05
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Data Correction Unfolding
◦ RooUnfold: Iterative (“Bayesian”) method ◦ Methodology:
◦ Characterising UE activity as 2D histogram before making a profile
𝑋𝑇𝑟𝑎𝑐𝑘𝑠, 𝑝𝑇𝐿𝑒𝑎𝑑𝑖𝑛𝑔 𝑇𝑟𝑎𝑐𝑘𝐽𝑒𝑡 2𝐷
𝑢𝑛𝑓𝑜𝑙𝑑𝑋𝑃𝑎𝑟𝑡𝑖𝑐𝑙𝑒𝑠, 𝑝𝑇𝐿𝑒𝑎𝑑𝑖𝑛𝑔 𝐺𝑒𝑛𝐽𝑒𝑡 2𝐷
𝑝𝑟𝑜𝑓𝑖𝑙𝑒𝑋𝑃𝑎𝑟𝑡𝑖𝑐𝑙𝑒𝑠 , 𝑝𝑇𝐿𝑒𝑎𝑑𝑖𝑛𝑔 𝐺𝑒𝑛𝐽𝑒𝑡 𝑃𝑟𝑜𝑓𝑖𝑙𝑒
◦ “Training” the unfolding matrix (using CUETP8M1) ◦ Unfolding data iteratively with the “Bayesian” method
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Systematic Uncertainties Efficiency/Fake mismodelling
◦ Reduction of efficiency by 3.9% and increasing fakes by 50%
Pileup (PU) ◦ Effect of unfolding (CUETP8M1) with response matrix with and without PU
(CUETHS1)
Model dependency of correction ◦ Effect of correction with different MC generator models
◦ CUETP8M1 corrected with CUETHS1 or EPOS
Impact parameter variation ◦ Varying the impact parameter to 2 and 4 (from 3)
Vertex degree of freedom ◦ Varying the vertex degree of freedom requirement to 2 and 6 (from 4)
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Systematic: Summary Summary of systematics at 𝑝𝑇 = 20 GeV (plateau)
Ranges given across regions. ◦ Black: leading track
◦ Red: leading jet
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Distribution (𝑝𝑇 =20𝐺𝑒𝑉)
Pileup Impact Parameter Significance sig<2 (sig<4)
Vertex Sel. Dof>2 (Dof>6)
Efficiency/ Fake mismodelling
Model dependency
𝑁𝑐ℎ
/ Δ𝜂Δ Δ𝜙 1-2% 1-4%
0.4-0.7 (0.1)% 0.2-0.4 (*)%
<0.1 (0.1)% <0.1 (0.3)%
1-2% 1-2%
1-4% 1-4%
Σ𝑝𝑇
/ Δ𝜂Δ Δ𝜙 1-2% 1-4%
0.7-0.8 (0.1)% 0.4-0.5 (0.3-0.5)%
<0.1 (0.1)% <0.1 (0.2-0.3)%
1-2% 1-2%
1-4% 1-4%
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Results
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Particle densities:Leading track TransAVE/transDIF: Comparison with PYTHIA8 (Monash, CUETP8M1, EPOS), HERWIG++ (CUETHS1)
Best performing: Monash and CUETP8M1
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Particle densities:Leading track TransMAX/transMIN: Comparison with PYTHIA8 (Monash, CUETP8M1, EPOS), HERWIG++ (CUETHS1)
Best performing: Monash
14 EPOS describes the rising region but drops in the plateau region and seems to flatten again
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Particle densities: Leading jet
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TransAVE/transDIF: Comparison with PYTHIA8 (Monash, CUETP8M1, EPOS), HERWIG++ (CUETHS1) Best performing: Monash and CUETP8M1
UE densities plateaus with a higher activity as a function of leading jet 𝑝𝑇
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Particle densities: Leading jet
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TransMAX/transMIN: Comparison with PYTHIA8 (Monash, CUETP8M1, EPOS), HERWIG++ (CUETHS1) Best performing: Monash
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Energy densities: Leading track TransAVE/transDIF: Comparison with PYTHIA8 (Monash, CUETP8M1, EPOS), HERWIG++ (CUETHS1)
Best performing: Monash and CUETP8M1 (transDIF)
17 CUETP8M1 overestimates transAVE energy densities at high leading track 𝑝𝑇
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Energy densities: Leading track TransMAX/transMIN: Comparison with PYTHIA8 (Monash, CUETP8M1, EPOS), HERWIG++ (CUETHS1)
Best performing: Monash and CUETP8M1
18 CUETP8M1 tends to overestimate energy densities at high leading track 𝑝𝑇
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Energy densities: Leading jet TransAVE/transDIF: Comparison with PYTHIA8 (Monash, CUETP8M1, EPOS), HERWIG++ (CUETHS1)
Best performing: Monash
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Energy densities: Leading jet TransMAX/transMIN: Comparison with PYTHIA8 (Monash, CUETP8M1, EPOS), HERWIG++ (CUETHS1)
Best performing: Monash
20 Tunes generally describe UE densities as function of leading track 𝑝𝑇 better
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Energy dependence Particle/energy density center-of-mass energy dependence at 0.9, 2.76, 7, and 13 TeV for transAVE activity compared with:
◦ PYTHIA8 (Monash, CUETP8M1, CUETP8S1), HERWIG++ (CUETHS1)
Monash predicts a better centre-of-mass energy dependence
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Energy dependence Particle/energy density at 2.76 and 13 TeV for transMAX/ transMIN/ transDIF activity
All tunes describe transDIF densities better
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Energy dependence Particle/energy density at 2.76 and 13 TeV for transMAX/ transMIN/ transDIF activity
transMIN densities have a stronger energy evolution to transDIF
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Energy dependence Particle/energy density at 2.76 and 13 TeV for transMAX/ transMIN/ transDIF activity
Monash describes well for all transverse densites but generally not as well for average energy density.
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Summary UE @ 13 TeV has been measured and fully corrected for detector effects and selection efficiencies for the transAVE, transMIN, transMAX and transDIF densities
Results are compared to various PYTHIA8, HERWIG++ tunes, and EPOS
Comparison is made with UE @ 0.9, 2.76, 7 TeV ◦ For tuning of energy dependence of the MC
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END
Thank you for your attention!
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Appendix
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MC regularisation of soft MPI cross section
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HERWIG++ follows a similar prescription
PYTHIA8 energy dependence follows the following prescription:
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Monte Carlo models
PYTHIA/HERWIG differences (same PDF, CTEQ6L1): ◦ Details of interleaving between ISR/FSR/MPI ◦ 𝑝𝑇-ordered/angular-ordered evolution ◦ Lund string/cluster hadronisation ◦ Tunable parameters in all MC are optimised with different datasets
EPOS describes soft-parton dynamics by Gribov-Regge theory with the exchange of virtual quasi-states with multi-pomeron exchanges. Hard-pomeron scattering is included to simulate hard-parton processes.
String hadronisation is implemented in EPOS
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Cluster hadronisation model
String hadronisation model