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

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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 - PowerPoint PPT Presentation

Transcript of Optimizing the W resonance in dijet mass

Page 1: Optimizing the  W resonance in  dijet  mass

Optimizing the W resonance in dijet mass

Daniel AbercrombiePennsylvania State University

8 August 2013

Advisors: Phil Harris and Andreas Hinzmann

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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

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The Event

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Characterizing the W peak

Searching for stable mean and smallest fractional width

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200 GeV < pT < 225 GeV

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Comparing cone sizes

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• 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

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Comparing cone sizes

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

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Comparing cone sizes

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• ΔR = 0.4 gives narrowest width

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Comparing cone sizes

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• Reasonably constant responses from each cone size

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Comparing cone sizes

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• Again, ΔR = 0.4 gives the narrowest width

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Comparing cone sizes

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• Again, ΔR = 0.4 gives the narrowest width

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Comparing algorithms

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Comparing algorithms

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• Grooming keeps mass relatively constant compared to anti-kT

ΔR = 0.5

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Comparing algorithms

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ΔR = 0.5

• Trimming and filtering compete for best resolution

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Comparing algorithms

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• Pruning may be too aggressive at low pileup

ΔR = 0.5

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Comparing algorithms

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ΔR = 0.5

• Trimming and filtering compete for best resolution

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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

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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

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Thank you!

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Thank you!

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Backup Slides

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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.

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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 pT jets with a invariant mass of 30 GeV < m < 250 GeV is picked

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Comparing algorithms

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• 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

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Other measures of efficiency

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ΔR = 0.5

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

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Other measures of efficiency

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ΔR = 0.5

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

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Effects of PU

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ΔR = 0.4

• Pileup decreases efficiency• This is more prominent using larger cone sizes

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Effects of PU

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ΔR = 0.5

• Pileup decreases efficiency• This is more prominent using larger cone sizes

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Effects of PU

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ΔR = 0.7

• Pileup decreases efficiency• This is more prominent using larger cone sizes

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Effects of PU

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ΔR = 0.9

• Pileup decreases efficiency• This is more prominent using larger cone sizes

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PU jets simulation

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𝑑𝜎𝑑𝑝𝑇

∝𝑝𝑇❑− 5 ;𝑝𝑇>3GeV

𝑑𝜎𝑑𝑝𝑇

=𝑚𝑝𝑇+𝑏 ;0GeV<𝑝𝑇<3GeV

Weighting:

𝑤 (𝑁𝑃𝑈 ,𝑛 𝑗𝑒𝑡𝑠 )= 𝑁𝑃𝑈 !(𝑁𝑃𝑈−𝑛 𝑗𝑒𝑡𝑠 ) !𝑛 𝑗𝑒𝑡𝑠 !

(0.0125 )𝑛 𝑗𝑒𝑡𝑠 (0.9875 )𝑁𝑃𝑈 −𝑛 𝑗𝑒𝑡𝑠

𝐴 𝑗𝑒𝑡

𝐴𝐶𝑀𝑆≈0.0125

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PU jets simulation

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NPU = 10

• Everything above 20 GeV can be mistakenfor a quark jet

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PU jets simulation

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NPU = 15

• Everything above 20 GeV can be mistakenfor a quark jet

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PU jets simulation

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NPU = 20

• Everything above 20 GeV can be mistakenfor a quark jet

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PU jets simulation

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NPU = 25

• Everything above 20 GeV can be mistakenfor a quark jet

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PU jets simulation

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NPU = 30

• Everything above 20 GeV can be mistakenfor a quark jet

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PU jets simulation

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NPU = 35

• Everything above 20 GeV can be mistakenfor a quark jet

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PU jets simulation

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NPU = 40

• Everything above 20 GeV can be mistakenfor a quark jet

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ΔR = 0.3

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ΔR = 0.4

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ΔR = 0.5

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ΔR = 0.6

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ΔR = 0.7

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ΔR = 0.8

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ΔR = 0.9

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ΔR = 1.0

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ΔR = 0.7

175 GeV < pT < 200 GeV

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ΔR = 0.7

200 GeV < pT < 225 GeV

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ΔR = 0.7

225 GeV < pT < 250 GeV

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ΔR = 0.7

250 GeV < pT < 275 GeV

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ΔR = 0.7

275 GeV < pT < 300 GeV

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

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Comparing algorithms

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ΔR = 0.5

• Grooming keeps mass relatively constant compared to anti-kT

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Comparing algorithms

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ΔR = 0.5

• Anti-kT seems to have the smallest width

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Comparing algorithms

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ΔR = 0.5

• Pruning may be too aggressive at low pileup

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Comparing algorithms

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ΔR = 0.5

• Again, anti-kT has narrowest width

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Top Mass

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Top Mass Width