LCFI Package and Flavour Tag @ 3TeV

Tomáš Laštovička

Institute of Physics AS CR

CLIC WG3 Meeting 9/6/2010

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LCFI Package

Used for jet flavour tagging and secondary vertex reconstruction. Topological vertex finder ZVRES. Standard LCIO input/output

– Marlin environment (used for both ILD/SiD)

Flavour tagging based on Neural Nets.– Combine several variables…

Probability Tubes

Vertex Function

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NN Input Flavour Discriminating Variables

There are 14 flavour discriminating variablesR- and Rz- significance for 2 tracks with the highest impact parameter significance in R

(“leading tracks”)

Relative momenta of the leading tracks (relative to jet energy)

Joint Probability in R and Rz

Decay length and decay length significance (relative to jet energy)

Pt-corrected vertex mass

Secondary vertex probability

Relative total momentum of non-primary vertex tracks and their number

These inputs are re-normalised and transformed by tanh() - except joint and secondary vertex probabilities.

Tracks/vertices have to pass some minimal selection cuts.

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NN Input Flavour Discriminating Variables

Inputs are sent to 3 neural networks (8 inputs each) according to the number of secondary vertices found in a given jet– 0 vertices:

R-, Rz- significance and momenta for 2 leading tracks

Joint Probability (R, Rz)

– 1 vertex and >1 vertices:Decay length, decay length significance, pt-corrected vertex mass,

Total momentum of non-primary vertex tracks and their number,

Joint Probability (R, Rz), Secondary vertex probability

This is not a dogma, inputs can be added/removed– Requires some coding.– Studies better done outside the package (I fancy FANN package for this purpose).

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Input Variables – Additional Topics

Joint Probability Calculation– Estimated using fits to impact parameter distributions.– Might depend on detector geometry and sim/rec effects.

Ks, and conversion tagger– Part of the package, depends on detector geometry.

Cuts on tracks/vertices for NN Inputs– There is a number of parameters to tune up the package (see next slide).

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LCFI Package Optimisation

Optimisation is not only a matter of Neural Net retraining. The package has plenty of parameters:– Track selection params– ZVRES params– Flavour Tag params– Vertex Charge params

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Example 1SiD FastMCDi-jets @ 500GeVISR removed by Minv cut

b-jets (red)c-jets (green)

Light-jets (black)

R 1 R 2 Z 1

JP R JP Z M 1 M 2

DL S DL Pt CM RM

#t V #VSVP E

Z 2

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Further Examples

I compared various samples (sorry for too many plots). Let’s start with the same setup but for 3 TeV

– It’s pretty much similar as far as input variables are concerned.

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SiD FastMCDi-jets @ 3TeVISR removed by Minv cut

SiD FastMCDi-jets @ 500GeVISR removed by Minv cut

b-jets (red)c-jets (green)

Light-jets (black)

R 1 R 2 Z 1 Z 2

JP R JP Z M 1 M 2

DL S DL Pt CM RM

#t V #VSVP E

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Further Examples

I compared various samples (sorry for too many plots). Let’s start with the same setup but for 3 TeV

– It’s pretty much similar as far as input variables are concerned.

ff 2-jet events @ 3 TeV

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Di-jets @ 3TeVISR removed by Minv cut

ILD Full Sim/Recff @ 3TeVDST files area normalisedMinv cut

R 1 R 2 Z 1 Z 2

JP R JP Z M 1 M 2

DL S DL Pt MC RM

#t V #VSVP E

b-jets (red)c-jets (green)

Light-jets (black)

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Further Examples

I compared various samples (sorry for too many plots). Let’s start with the same setup but for 3 TeV

– It’s pretty much similar as far as input variables are concerned.

ff 2-jet events @ 3 TeV

H0A0 4-jet events– First reconstructed with the SiD FastMC,– then with the full simulation and reconstruction.– Please, ignore c-jets.

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Di-jets @ 3TeVISR removed by Minv cut

SiD FastMCH0A0 @ 3TeVno Minv cut4 jet eventsarea normalised

b-jets (red)c-jets (green)

Light-jets (black)

b-jets (red)c-jets (green)

Light-jets (black)

R 1 R 2 Z 1 Z 2

JP R JP Z M 1 M 2

DL S DL Pt MC RM

#t V #VSVP E

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ILD Full Sim/RecH0A0 @ 3TeVDST files 224 – 231, 825-8404 jet eventsarea normalized

R 1 R 2 Z 1 Z 2

JP R JP Z M 1 M 2

DL S DL Pt MC RM

#t V #VSVP E

b-jets (red)c-jets (green)

Light-jets (black)

SiD FastMCH0A0 @ 3TeVno Minv cut4 jet eventsarea normalised

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Discussion

SiD FastMC consistent for 500GeV and 3TeV.– And consistent to full SiD reconstruction @ 500GeV.

Then things get bit more complicated to compare– Different events, detectors, reconstruction, low statistics.– ff events comparable for b- and c-tag. Light jets probably polluted (?).– H0A0 events: b-events more or less OK, however:

• Differences between FastMC and full simulation reconstruction (e.g. Pt corrected mass secondary vertex reconstruction?).

Different input distribution compared to the reference one worse performance with default nets.

Summary

LCFI package has a number of flavour tag sensitive variables, these can be revised/modified.

We’ve looked at a couple of samples using SiD FastMC as well as DST files from Marco (full simulation and reconstruction).

Future Plans:

b-tag will be studied more closely.

c- and uds- mistag efficiencies.

Optimisation of the LCFI package.