Quaero - California Institute of Technologyveverka/files/20080211_knuteson...2008/02/11 · Quaero...

Bruce Knuteson

Quaero(I search for, I seek)

Multivariate Workshop, Caltech, Feb 11 2008

The problemThe solution

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Bruce Knuteson 2

The problem

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Bruce Knuteson 3

Vista

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Bruce Knuteson 4

Standard Model

Data

Sleuth

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Bard

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Bruce Knuteson 6

Bard

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Bard

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Bard

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Bard stories

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Bruce Knuteson

Quaero@D0RunIDØ CollaborationPhys.Rev.Lett.87:231801,2001

Quaero@H1S. Caron, B. KnutesonEur.Phys.J.C53:167-175,2008

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• Time budget is calculated

• H events are run through the detector simulation

• H, SM, data are partitioned into final states• Variables are chosen automatically • Binning is chosen automatically• A binned likelihood is calculated• Results from different final states are combined• Results from different experiments are combined• Systematic errors are integrated numerically• Result returned

Quaero algorithm overview(you wish to test a hypothesis H )

Quaero adjusts its analysis strategy to

fit within time budget

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Bruce Knuteson

TurboSim

A fast detector simulation that

tunes itself to any experiment’s

detailed detector simulation

Full simulation 100 seconds

TurboSim0.01 seconds

Bruce Knuteson

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Bruce Knuteson 16

Vista exclusive

final states

CDF Run II preliminary (927 pb-1)

Order according to decreasing s/(√b x size)

Use as many final states as time allows

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Variables are chosen separately for each final state

Dimensional Rule of Thumb: > 10d events are needed to adequately

populate a d-dimensional space Corollary: analysis should be performed in a space of

dimensionality d = log10NMC Prescription: 1. Generate a long (but finite) list of relevant variables TeV: pT, ϕ, η, Δϕij, ΔRij, mij, mijk, mijkl

LEP: E, ϕ, θ, Δϕij, ΔRij, mij, mijk, mijkl

2. Order according to decreasing discrepancy (h vs b)

3. Use the first d variables in the list, removing highly correlated variables

Goals: speed, robustness, transparency

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FewKDE

Typical kernel solution

1) place “bumps of probability” around each Monte Carlo point

2) sum these bumps into a continuous distribution

Time cost is O(N2) FewKDE

fit for parameters of a handful of Gaussians appropriately handle hard physical boundaries

Form a discriminant

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Expected # of events / unit of x

Expected evidence

OptimalBinning

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Quaero@H1S. Caron, B. KnutesonEur.Phys.J.C53:167-175,2008

clickable exclusion plot

http://mit.fnal.gov/Quaero/quaero/doc/examples/clickableExclusionPlots/all_lq_sue+.html

http://mit.fnal.gov/Quaero/quaero/doc/examples/clickableExclusionPlots/all_lq_sue+.html

Bruce Knuteson

Quaero(I search for, I seek)

Multivariate Workshop, Caltech, Feb 11 2008

The problemThe solution

http://www.mit.edu/

http://www.mit.edu/

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Quaero - California Institute of Technologyveverka/files/20080211_knuteson...2008/02/11 · Quaero...

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