fully hadronic final state resonance search
NorCal HEP-EXchange 12/01/2018Tong OuSupervisors: Shih-Chieh Hsu, Samuel Meehan (UW), Lei Zhang (NJU)Senior undergrad, Nanjing UniversityVisiting student, University of Washington, Seattle
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Why search?● Many new exotics particles decay to tt.
○ Extra dimension, Dark matter...● No significant deviation found (yet)
2Mass
Sear
ch C
hann
el 3TeV
Analysis goal and strategy
Primary task: ttbar fully hadronic final state resonance search with ATLAS full Run 2 (2015-2018) data with an integrated luminosity up to 140fb-1.
Strategy:
● Event selection: Top tagging and b-tagging● Background estimation: Smoothly falling function fit to data● Statistical analysis: Bump Hunter -> Hypothesis test (invert)
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Targetting signal Background sources
Event selection
● Focus on the boosted topology → 2 large-R jets ~ “top quarks”● Top tagging: Jet substructure info as input to a deep neural network (DNN)● Lepton veto: 2 top quarks are required to decay hadronically
○ Branching ratio of W->qq’ is 67% ● b-tagging: 2 signal regions with 1 b-tag and 2 b-tags respectively
.
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Event selection
● Top taggers: Make use of the jet substructure information.● DNN top tagger outperforms others
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Mass of top~170GeV
DNN top tagger
Background estimation
Background:
● Sources: Dijet and SM ttbar● Modeling: Direct fitting to data with a smoothly falling
function. Fitting function form:
with and are free parameters. ATLAS Exotics workshop
Targetting signals:
● Top-color assisted-technicolor signal.● HVT signal.
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Background estimation
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● Initial tests fully on Monte Carlo simulation1 b-tag & 2 top-tags
Background fitting
2 b-tags & 2 top-tagsMonte C
arlo
Statistical analysis
Bump Hunter
Hypothesis Test (invert)
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Theory cross sectionExpected upper limit
Uncertainty band
Unfortunately, if no deviation found...
Excluded
Bump Hunter (implemented in BayesianFramework)
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1 Loop over all possible bin-window, search for the window with lowest probability , the test statistic is defined as
2 Calculate p-value using pseudo-experiments.
Mtt
Bump Hunting results
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1 b-tag & 2 top-tags 2 b-tags & 2 top-tags
● Tests on fully Monte Carlo simulation● Report Bump Hunter p-value and mass window with greatest deviation
Frequentist CLs method
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Profile likelihood
Test statistic (Different one-sided requirement from p0-value calculation!)
When setting limit, we don’t care the case where the hypothesized μ is lower than the ML estimator
Test statistic
CLbCLs+b
Exclusion limit (CLs=1-CL)
Expected limit based on MC samples
● 1 b-tag and 2 b-tags regions combined. ● With and without systematics regarding signal samples generation.
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With systematicsWithout systematics
Limit from last ttbar search
Summary
ttbar fully hadronic final state resonance search with full Run2 data
● Sensitivity improved!● Wait for the result early next year!
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Thank you!And many thanks to my supervisors, Shih-Chieh, Sam and Lei, as well as tt 0L analysis group, for their patience and support!!
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Back up
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Statistical analysis
Bump Hunter Hypothesis Test
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Model independent Model dependent
Window with greatest deviation
CERN-EP-2017-042 Discovery of Higgs boson
Hypothesis test (implemented in CommonStatTools based on
RooStat) Likelihood
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conditional ML fit
unconditional ML fitTest statistic
p0-value
Profile likelihood ratio
Hypothesis test results
Local p0-value calculation based on MC samples
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Quantitative comparison between previous and present
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Asymptotic formulae
With some approximations valid in large sample limit, we can analytically derive the (any kind of) test statistic distribution, which is a non-central chi-square distribution for one-degree of freedom. Free us from generating pseudo-experiments!
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For the case of discovery:
follows a Gaussian distribution with mean and standard deviation σ.
Testing μ=0 hypothesis
Assuming data is distributed according to a stength parameter μ’=0
For the case of setting upper limit:
follows a Gaussian distribution with mean and standard deviation σ.CLs+b
CLb
Systematics
● For the background: Since our background is estimated by fitting, the only relevant systematic uncertainty for the background estimate is spurious signal caused by fit function form and fit range.
● For the signal: the relevant systematics are listed in the table.
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List of contributions
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