LCFI Package and Flavour Tag @ 3TeV
Tomáš Laštovička
Institute of Physics AS CR
CLIC WG3 Meeting 9/6/2010
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
Page  2
NN Input Flavour Discriminating Variables
 There are 14 flavour discriminating variables
R- 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.
Page  3
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).
Page  4
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).
Page  5
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
Page  6
Example 1
R 2
R 1
JP R
Page  7
b-jets (red)
c-jets (green)
Light-jets (black)
SiD FastMC
Di-jets @ 500GeV
ISR removed by Minv cut
JP Z
Z1
Z2
M1
M2
DL S
DL
Pt CM
RM
#t V
SVP
E
#V
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.
Page  8
R 1
JP R
R 2
JP Z
DL S
DL
#t V
SVP
Z1
Z2
M1
M2
Pt CM
E
RM
b-jets (red)
c-jets (green)
Light-jets (black)
SiD FastMC
Di-jets @ 3TeV
ISR removed by
Minv cut
#V
SiD FastMC
Di-jets @ 500GeV
ISR removed by Minv cut
Page  9
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
Page  10
R 1
JP R
R 2
JP Z
DL S
DL
#t V
SVP
Z1
Z2
M1
M2
Pt MC
E
RM
b-jets (red)
c-jets (green)
Light-jets (black)
Di-jets @
3TeV
ISR removed
by Minv cut
#V
ILD Full Sim/Rec
ff @ 3TeV
DST files
area normalised
Minv cut
Page  11
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.
Page  12
R 1
JP R
R 2
JP Z
DL S
DL
#t V
SVP
Z1
Z2
M1
M2
Pt MC
E
RM
b-jets (red)
(red)
b-jets
c-jets(green)
(green)
c-jets
Light-jets(black)
(black)
Light-jets
Di-jets @ 3TeV
ISR removed
by Minv cut
#V
SiD FastMC
H0A0 @ 3TeV
no Minv cut
4 jet events
area normalised
Page  13
R 1
JP R
DL S
#t V
R 2
JP Z
DL
SVP
Z1
Z2
M1
M2
Pt MC
E
RM
#V
b-jets (red)
c-jets (green)
Light-jets (black)
SiD FastMC
H0A0 @ 3TeV
no Minv cut
4 jet events
area normalised
ILD Full Sim/Rec
H0A0 @ 3TeV
DST files
224 – 231, 825-840
4 jet events
area normalized
Page  14
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.
Page  15
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.