Optimizing the w resonance in dijet mass
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Optimizing the W resonance in dijet mass. Daniel Abercrombie Pennsylvania State University 8 August 2013 Advisors: Phil Harris and Andreas Hinzmann. The Goal of the Project. Compare jet cone sizes and algorithms

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Optimizing the w resonance in dijet mass

Optimizing the W resonance in dijet mass

Daniel Abercrombie

Pennsylvania State University

8 August 2013

Advisors: Phil Harris and Andreas Hinzmann


The goal of the project
The Goal of the Project

  • Compare jet cone sizes and algorithms

  • Identify the algorithm and parameters that givesa stable W mass and narrowest resonance

  • Results will be used in talks with ATLAS to determine a common set of parameters for jet reconstruction between the experiments

Daniel Abercrombie


The event
The Event

Daniel Abercrombie


Characterizing the w peak
Characterizing the W peak

Searching for stable mean and smallest fractional width

200 GeV < pT < 225 GeV

Daniel Abercrombie


Comparing cone sizes
Comparing cone sizes

  • Using the anti-kT algorithm gives the most conic shape and is resistant to soft radiation

  • Scanned through cone sizes from ΔR = 0.4 to ΔR = 0.8 with a resolution of 0.1

Daniel Abercrombie


Comparing cone sizes1
Comparing cone sizes

  • Jump in larger cones probably due pT cut for single jets

Daniel Abercrombie


Comparing cone sizes2
Comparing cone sizes

  • ΔR = 0.4 gives narrowest width

Daniel Abercrombie


Comparing cone sizes3
Comparing cone sizes

  • Reasonably constant responses from each cone size

Daniel Abercrombie


Comparing cone sizes4
Comparing cone sizes

  • Again, ΔR = 0.4 gives the narrowest width

Daniel Abercrombie


Comparing cone sizes5
Comparing cone sizes

  • Again, ΔR = 0.4 gives the narrowest width

Daniel Abercrombie


Comparing algorithms
Comparing algorithms

Daniel Abercrombie


Comparing algorithms1
Comparing algorithms

ΔR = 0.5

  • Grooming keeps mass relatively constant compared to anti-kT

Daniel Abercrombie


Comparing algorithms2
Comparing algorithms

ΔR = 0.5

  • Trimming and filtering compete for best resolution

Daniel Abercrombie


Comparing algorithms3
Comparing algorithms

ΔR = 0.5

  • Pruning may be too aggressive at low pileup

Daniel Abercrombie


Comparing algorithms4
Comparing algorithms

ΔR = 0.5

  • Trimming and filtering compete for best resolution

Daniel Abercrombie


Conclusions
Conclusions

  • Smaller cone sizes give the best mass resolution with a reasonably small response

  • Pruning looks like it might be too aggressive

  • Current plots should be improved by finding ways to increase the efficiency of picking the correct jets

Daniel Abercrombie


Future work
Future work

  • Explore additional parameter space of the algorithms

  • Look at the effects of jet reconstruction onthe top quark mass

  • Work on selection cuts and parameters to increase the efficiency of selecting the correct jet

Daniel Abercrombie


Thank you
Thank you!

Daniel Abercrombie


Thank you1
Thank you!

Daniel Abercrombie


Backup slides
Backup Slides

Daniel Abercrombie


Selection criteria jets
Selection criteria jets

  • Events must have at least two b tagged jets and one isolated muon with pT > 10 GeV and |η| < 2.4

  • Two jets with pT > 20 GeV and the highest combined secondary vertex values were selected as the b jets

  • Other jets were in the opposite hemisphere from the muon, MET, and b tagged jet closer to the muon

    i.e.

Daniel Abercrombie


Selection criteria jets cont
Selection criteria jets (cont.)

  • Single jets were picked with the following cuts:p > 200 GeV; mass > 60 GeV; MET > 30 GeV

    • MET cut helps ensure boosted tops

  • If there were no single jets, the dijet system with the highest pTjets with a invariant mass of 30 GeV < m < 250 GeV is picked

Daniel Abercrombie


Comparing algorithms5
Comparing algorithms

  • Pruningtight: nsubjets=2, zcut=0.1, dcut factor=0.5, algo = CAloose: nsubjets=2, zcut=0.1, dcut factor=0.2, algo = CA

  • Filteringtight: rfilt=0.2, nfilt=3, algo = CA loose: rfilt=0.3, nfilt=3, algo = CA

  • Trimmingtight: rtrim=0.2, pTfrac=0.05, algo = CA loose: rtrim=0.2, pTfrac=0.03, algo = CA

Daniel Abercrombie


Other measures of efficiency
Other measures of efficiency

ΔR = 0.5

  • All of the lines for each algorithm fall well withinthe uncertainties

Daniel Abercrombie


Other measures of efficiency1
Other measures of efficiency

ΔR = 0.5

  • All of the lines for each algorithm fall well withinthe uncertainties

Daniel Abercrombie


Effects of pu
Effects of PU

ΔR = 0.4

  • Pileup decreases efficiency

  • This is more prominent using larger cone sizes

Daniel Abercrombie


Effects of pu1
Effects of PU

ΔR = 0.5

  • Pileup decreases efficiency

  • This is more prominent using larger cone sizes

Daniel Abercrombie


Effects of pu2
Effects of PU

ΔR = 0.7

  • Pileup decreases efficiency

  • This is more prominent using larger cone sizes

Daniel Abercrombie


Effects of pu3
Effects of PU

ΔR = 0.9

  • Pileup decreases efficiency

  • This is more prominent using larger cone sizes

Daniel Abercrombie


Pu jets simulation
PU jets simulation

Weighting:

Daniel Abercrombie


Pu jets simulation1
PU jets simulation

NPU = 10

  • Everything above 20 GeV can be mistakenfor a quark jet

Daniel Abercrombie


Pu jets simulation2
PU jets simulation

NPU = 15

  • Everything above 20 GeV can be mistakenfor a quark jet

Daniel Abercrombie


Pu jets simulation3
PU jets simulation

NPU = 20

  • Everything above 20 GeV can be mistakenfor a quark jet

Daniel Abercrombie


Pu jets simulation4
PU jets simulation

NPU = 25

  • Everything above 20 GeV can be mistakenfor a quark jet

Daniel Abercrombie


Pu jets simulation5
PU jets simulation

NPU = 30

  • Everything above 20 GeV can be mistakenfor a quark jet

Daniel Abercrombie


Pu jets simulation6
PU jets simulation

NPU = 35

  • Everything above 20 GeV can be mistakenfor a quark jet

Daniel Abercrombie


Pu jets simulation7
PU jets simulation

NPU = 40

  • Everything above 20 GeV can be mistakenfor a quark jet

Daniel Abercrombie






ΔR = 0.3

Daniel Abercrombie


ΔR = 0.4

Daniel Abercrombie


ΔR = 0.5

Daniel Abercrombie


ΔR = 0.6

Daniel Abercrombie


ΔR = 0.7

Daniel Abercrombie


ΔR = 0.8

Daniel Abercrombie


ΔR = 0.9

Daniel Abercrombie


ΔR = 1.0

Daniel Abercrombie


ΔR = 0.7

175 GeV < pT < 200 GeV

Daniel Abercrombie


ΔR = 0.7

200 GeV < pT < 225 GeV

Daniel Abercrombie


ΔR = 0.7

225 GeV < pT < 250 GeV

Daniel Abercrombie


ΔR = 0.7

250 GeV < pT < 275 GeV

Daniel Abercrombie


ΔR = 0.7

275 GeV < pT < 300 GeV

Daniel Abercrombie






Cacciari, M., et al. JHEP04(2008)063

Daniel Abercrombie


Comparing algorithms6
Comparing algorithms

ΔR = 0.5

  • Grooming keeps mass relatively constant compared to anti-kT

Daniel Abercrombie


Comparing algorithms7
Comparing algorithms

ΔR = 0.5

  • Anti-kT seems to have the smallest width

Daniel Abercrombie


Comparing algorithms8
Comparing algorithms

ΔR = 0.5

  • Pruning may be too aggressive at low pileup

Daniel Abercrombie


Comparing algorithms9
Comparing algorithms

ΔR = 0.5

  • Again, anti-kT has narrowest width

Daniel Abercrombie


Top mass
Top Mass

Daniel Abercrombie


Top mass width
Top Mass Width

Daniel Abercrombie


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