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Measurements with b-jets and b-tagging
G. Watts (UW/Seattle)November 12, 2012
CHICAGO Workshop on LHC Physics in the Higgs Era
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G. Watts (UW/Seattle) 2
b-TaggingThe LHC program requires us to address bottom quarks as a unique object
Signals Low mass Higgs, SUSY high , etc.
Backgrounds W+jets, Z, and
This is not black-box object ID
The identification algorithms are getting quite complex
Their calibration are full blown analyses in their own right(and take quite a bit of time)
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G. Watts (UW/Seattle) 3
Anatomy of a b-quark
Decays via the Weak Force
~20% of decays are semileptonic
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G. Watts (UW/Seattle) 4
Anatomy of a b-quark
B
š ,šSemileptonic Decays
Electron
Electron embedded in jet extraordinarily difficultMuon
MIP in calorimeter, easy to identify in muon chambers, even low
Used heavily in calibrationLess so in analysis
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G. Watts (UW/Seattle) 5
Semileptonic TaggingMuon, GeV,
Lots of material in front of muon chambersā¦CMS Calib uses GeV
Calibration
Muon ID
Trust Monte Carlo
Used by CMS in their high mass Higgs search
š»āššāāāššUsed with regular b-tagging as a veto
(Binary Decision)
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G. Watts (UW/Seattle) 6
Anatomy of a b-quark
B
Hard ScatterDecay Length (Lxy)
Impact Parameter (IP)
šš ā 450šš mm at the LHC! ,
Algorithms tuned to take advantage of one or more of these features
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G. Watts (UW/Seattle) 7
Silicon
Pixel detectors have made this āeasyā at the LHC
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G. Watts (UW/Seattle) 8
Tagging AlgorithmsTracks
Count the number of tracks in a jetCuts on impact parameterHighly efficient
Vertex ReconstructionFit the tracks looking for a displaced vertexEfficiency has relatively low plateauMany variations
CombinedUse elements of bothRecovers some of the efficiencyOften use MVA techniques
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G. Watts (UW/Seattle) 9
Must Understand Tracking!
CMS Low (agreement justas good at high )ATLAS
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G. Watts (UW/Seattle) 10
Counting Tracks Long, rich, history (CSIP, JLIP, etc.)
CMS Track Counting (TC) Algorithm
Ranks tracks by IP significance
2nd highest track is the discrimination variable
Many variations on a theme
Loose, Medium Operating Points
Used in 2011 CMS
Simple taggers, easy to understand, good for early data!
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G. Watts (UW/Seattle) 11
Secondary Vertex FindingATLAS: Basic Kalman FitterCMS: Adaptive Vertex Fitter
Typical Tracks: GeVRequire inner layer hits
Reject 2 track vertices consistent with , conversions
Purity is great!
Efficiency can be a problem!
Used in early dataā¦
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G. Watts (UW/Seattle) 12
Combined AlgorithmsAttempt to combine the best of both worlds.
Combination techniques: likelihood, NN, BDT, etc. The input variables
CMS (likelihood) - CSVā¢ Vertex Typeā¢ 2D significanceā¢ IP Significance of all tracksā¢ Vertex Massā¢ in vertex and jetā¢ Ratio of energy of tracks in vertex
to tracks in jetā¢ The of the tracksā¢ 2D IP significance of first non-
charm track
ATLAS (NN) ā MV1ā¢ Uses only outputs of other tagging
algorithmsā¢ IP3D ā track based algorithmā¢ SV1 ā Secondary vertex finding
algorithmā¢ JetFitterCOMBNN
These are the algorithms used by most analyses
ATLAS has really converged on this one algorithm
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G. Watts (UW/Seattle) 13
CharmCharm mesons also decay by the weak forceTypical tag rates are 15-20% of bottom tag rates
Specific algorithms have been designed to identify jets containing only charm
CMSRank tracks in a vertex by IP significance
One track at a time add to a vertex
First track where GeV is likely due to a bottom quark
Combine likelihoods to reject charm in CSV
Can be a significant background in W+jets, etc.
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G. Watts (UW/Seattle) 14
CharmCascade charm/bottom decay reconstruction
Bottom Decay
Charm DecayFit to a single line hypothesis
Single track vertices are possible
NN to aggregate the final valuesIP3D is also added in
ATLAS
The āJetFitterCOMBā algorithmAn input to the MV1
JetFitterCOMBNNc is a variant tuned to reject charm
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G. Watts (UW/Seattle) 15
Performance
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G. Watts (UW/Seattle) 16
Performance
You canāt compare CMS and ATLAS!
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G. Watts (UW/Seattle) 17
CalibrationThere is no clean sample of jets
known to be bottom quarks!
šāš+ĀæšāĀæ
The QCD background is just too great!
Two bottom quark rich samples are used instead
QCD dijet events
Production
Hard because the b fraction is unknownHard because itāsā¦ top.
Calibration Scale Factor and Systematic Error
Errors driven by statistics and ability to determine b-fraction
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G. Watts (UW/Seattle) 18
The techniques matterFrom the 2011 ATLAS Analysis
(used dijet calibration only)
We expect dijet to be most powerful at low jet , and calibration at high jet
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G. Watts (UW/Seattle) 19
Operating PointsMost tagging algorithms produce a continuous output
Why not use as MVA input?(CDF has already done this in most recent SM WH results)
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G. Watts (UW/Seattle) 20
Performance & CalibrationPerformance is a function of
at least (binned)
Add another axis: tag discrimination variable
Statistics are low!
Calibrate in terms of Operating Points
šš>š
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G. Watts (UW/Seattle) 21
Calibration Results
š” š”dijets
Both experiments combine calibrations
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G. Watts (UW/Seattle) 22
Using b-taggingStraight Forward Search
Require at least 1 (2) jets to be tagged
Relatively high efficiency with 2 tags
Many of the early searches used this technique
Search with binning by
Split analysis: 0 tags, 1 tag, 2 tagsSpliting by
Tag requirements often different
Will we do better with continuous tagging?
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G. Watts (UW/Seattle) 23
Using b-tagging
Veto
Use a high efficiency operating point & algorithm
Used to suppress a background containing heavy flavor.
In suppresses backgroundIn used to suppress backgrounds
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G. Watts (UW/Seattle) 24
Using b-taggingCMS uses b-tagging to improve the mass resolution
Events are chosen using standard search techniques
Use a Boosted Decision Tree to improve the bottom jet energy resolutionā¢ Properties of a found secondary vertexā¢ Properties of the tracks (IP, etc.)ā¢ Jet Energy related variables
1
15% increase in mass resolution
Final Discriminate2
ā¢ CSV max and min value for b-tagged jet
ā¢ Calibration done at many points enables this
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G. Watts (UW/Seattle) 25
Conclusions: Future Better Combinations
Both experiments have some techniques that might benefit each other Calibration Improvements
High luminosity and statistics should shrink the b-tagging error Direct calibration of charm and tau backgrounds instead of Monte Carlo ratios
Continuous Tagging Using the output of a combined b-tagging algorithm directly as input Final variable fit for analysis ordered in significance
Better Treatment of systematic errors Errors are being driven further into the analysis Common errors like Jet Energy Scale need to be varied in common Technically challenging
High Tagging Efficiency turnover occurs around 200 GeV Calibration is very difficult due to statistics
Other types of taggers? ATLAS has a double-bottom quark tagger ()
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G. Watts (UW/Seattle) 26
Conclusions The 2012 calibration should be stunning
And its effect should be obvious in the HCP results b-tagging continues to evolve
Many possible improvements We are still a good way from the point of diminishing returns
Though we tryā¦
Challenge: are there ways to use b-tagging in analysis with more than a highly tuned MVA?
How much will we improve on this as opposed to getting read for new data ()
I didnāt mention high bottom quark searches Their use of b-tagging is similar to the SM analyses See Keithās talk tomorrow