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 - PowerPoint PPT PresentationTRANSCRIPT
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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
Daniel Abercrombie 4
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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Daniel Abercrombie 38
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Daniel Abercrombie 39
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Daniel Abercrombie 42
ΔR = 0.3
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Daniel Abercrombie 43
ΔR = 0.4
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Daniel Abercrombie 44
ΔR = 0.5
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Daniel Abercrombie 45
ΔR = 0.6
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Daniel Abercrombie 46
ΔR = 0.7
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Daniel Abercrombie 47
ΔR = 0.8
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Daniel Abercrombie 48
ΔR = 0.9
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Daniel Abercrombie 49
ΔR = 1.0
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Daniel Abercrombie 50
ΔR = 0.7
175 GeV < pT < 200 GeV
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Daniel Abercrombie 51
ΔR = 0.7
200 GeV < pT < 225 GeV
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Daniel Abercrombie 52
ΔR = 0.7
225 GeV < pT < 250 GeV
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Daniel Abercrombie 53
ΔR = 0.7
250 GeV < pT < 275 GeV
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Daniel Abercrombie 54
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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Daniel Abercrombie 64
Top Mass
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Daniel Abercrombie 65
Top Mass Width