PandoraPFA and LCFIVertex with GLD data

PandoraPFA and LCFIVertexwith GLD data

S. Uozumi (Kobe)Apr-23th ILD detector optimization WG meeting

PandoraPFA Performancewith the GLD

• At TILC08 Sendai, Mark told us …– We are more-or-less doing a right thing, there seems to be nothing apparently wrong.– Mark generates events with a pythia setting tuned for

LEP experiment, which gives 20% less neutral particles in a jet. It will give ~1.5% effect on JER.

• Then Mark gave us his z-pole data (both lcio file after detector simulation and stdhep files) and steering file.

With LDC00 Z-pole + full tracking + PandoraPFA, Mark’s result : JER = 23.5 % (pandora v2-01)My result : = 28.5 % (pandora v2-00)Where does 5% difference come from ?

　 full tracking,

trackcheater

PerfectPFA,

fulltracking

PerfectPFA,

trackcheater

Mokka data from Mark's

stdhep (Mark's calib)

27.8 +- 0.3 23.8 +- 0.2 23.5 +- 0.2 19.6 +- 0.2

Mark's data (Mark's calib)

25.7 +- 0.8 23.4 +- 0.7 24.3 +- 0.7 23.5 +- 0.7

Jupiter data by Miyamoto (LCPhys calib)

28.9 +- 0.3 26.3 +- 0.3 　

Jupiter data from Mark's

stdhep (QGSP_BERT

calib)

28.7 +- 0.3 26.9 +- 0.3 　　• There are still some difference, but JER values are OK (<30%) for ILD optimization study.• We decided to leave more detailed study to be done sometime in future, and move ahead on the optimization studies anyway.

JER with various configurations (Z-pole)

Flavour Tagging Performancewith the GLD

• Jupiter data (GLD) 10k events– Jupiter data converted to lcio– Z -> anything, but leptonic decays (~30%) are rejected– No ISR

• Mokka (LDC01_05Sc) 10k events– Told by Sonja, copied from DESY GRID– Z -> qq (q=u,d,s,c,b) events

• Flavour tagging by FullTracking + PandoraPFA + LCFIVertex with LDC-tuned neural net parameters.

Z-pole data & Process for FT study

GLD LDC01_05Sc

b-jets 2541 1 3726 1

c-jets 2247 0.88 3091 0.83

uds-jets 8389 3.30 11967 3.21

Breakdown with true jet flavours:

:

::

c-tagging performance

•Result with LDC01+fulltracking doesn’t perfectly reproduce Sonja’s result. But with cheated result, agreement with Sonja becomes better.•Result with GLD data looks consistent with Sonja’s LDC01_05Sc result anyway.

Mokka LDC01_05Scw/ conv

Jupiter GLD

b

c

uds

b

c

uds

NN output for c-tag

b-tagging performance

Results with different configuration are slightly different,but b-tag peformance is almost acceptable with the GLD data.

Mokka LDC01_05Scw/ conv

Jupiter GLD

b

c

uds

bc

uds

NN output for b-tag

NN output for b-tag

Summary• We still can not reproduce the Mark’s JER result (~3% worse), but further investigation is kept for future.• Also JER with GLD data is still worse than Mark’s result, but OK for starting analyses of benchmark processes.• FT performance we get with LDC01_05Sc is still slightly

different with Sonja. Maybe issue of full tracking ?• FT Performance with GLD data is consistent with Sonja’s

result with LDC01_05Sc.• Now we are comparing JER and FT performance among

GLD, GLDprime and J4LDC geometries.• Also starting analyses of benchmark processes which

use Pandora + LCFIVertex.

Backups

Input variables for flavour tagging with Neural-Net

• D0Significance1• D0Significance2 • DecayLength • DecayLength(SeedToIP) • DecayLengthSignificance • JointProbRPhi • JointProbZ • Momentum1 • Momentum2 • NumTracksInVertices

• NumVertices • PTCorrectedMass • RawMomentum• SecondaryVertexProbability • Z0Significance1 • Z0Significance2 • D0Significance1 (zoomed) • D0Significance2 (zoomed) • Z0Significance1 (zoomed) • Z0Significance2 (zoomed)

b-jet events (any number of vertices)Black … GLDRed … LDC01_05Sc

D0Significance1 D0Significance2 DecayLength DecayLength(SeedtoIP)

DecayLengthSignificance

JointProbRPhi JointProbZ Momentum1 Momentum2 NumTracksInVertices

NumVertices PTCorrectedMass RawMomentum SecVertexProb Z0Significance1

Z0Significance2 D0Significance1(zoom)

D0Significance2(zoom)

Z0Significance1(zoom)


c-jet events (any number of vertices)Black … GLDRed … LDC01_05Sc









uds-jet events (any num. of vertices)Black … GLDRed … LDC01_05Sc









PandoraPFA and LCFIVertex with GLD data

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