Study of electron/pion separation in TRD. Recent results of BDT applications.

Semen Lebedev GSI, Darmstadt, Germany and LIT JINR, Dubna, Russia

Claudia HöhneGSI, Darmstadt, Germany

Gennady OsoskovLIT JINR, Dubna, Russia

S.Lebedev@gsi.de

G.Ososkov et al, Electron Identification in TRD GSI, 2010 2/14

TR Production reminder and Outline

Problems to consider:1. Choose parameters of TR model • Nr. of foils • Foil thickness grouped into 3 sets • Gas thickness

for better fit to experimental data.

2. Compare methods for e- indentifiation in order to find one most efficiently suppressing pions

• Simple cut on the sum of energy losses• Photon cluster counting • Ordered statistics (median)• Artificial Neural Network • Boosted Decision Tree

TR Production

3. Study how that best method is robust to to such experimental factorsexperimental factors as calibration of measurements, pile up of signals etc.

G.Ososkov et al, Electron Identification in TRD GSI, 2010 3/14

1. Choice of the TR model parameters

Three sets of radiator parameters:• trdNFoils (number of polyethylene foils)• trdDFoils (thicknbess of one foil [cm])• trdDGap (thickness of gap between foils [cm])

Params 1 Params 2 Params 3

Nof of foils 130 60 70

Thickness of one foil, cm 0.0013 0.0015 0.0014

Thickness of gap between foils, cm

0.02 0.05 0.04

G.Ososkov et al, Electron Identification in TRD GSI, 2010 4/14

Comparison TR simulation and experimental results for 1.5 GeV/c

params1 params2

params3Distributions of energy lossesfor pions are the same for all parameters

simulation experiment

overestimation underestimation

realistic

simulation

experiment

experimentexperiment

simulation simulation

G.Ososkov et al, Electron Identification in TRD GSI, 2010 5/14

Checking of the simulation: n of zero TR layers

Mom, GeV/c 1 1.5 2 3 4 5 7 9 11 13

param1 6.45 5.94 5.73 5.57 5.51 5.47 5.42 5.41 5.40 5.39

param2 8.15 7.09 6.70 6.38 6.25 6.18 6.11 6.08 6.07 6.06

param3 7.58 6.74 6.42 6.16 6.06 6.01 5.94 5.92 5.91 5.90

Distributions of number of zero TR layers for 1.5/GeV electrons

param1 param2 param3

Very important value for electron identification

Best pion suppression results are expected for param1

G.Ososkov et al, Electron Identification in TRD GSI, 2010 6/14

2. Methods for e- indentification and suppression

• Simple cut on the sum of energy losses

• Photon cluster counting a cluster is a number of photoelectrons in 12 TRD layers exceeding 5 KeV threshold

The main lesson: a transformation needed to reduce Landau tails a transformation needed to reduce Landau tails

of dE/dx losses of dE/dx losses

• Ordered statistics (median)π

e-

• Artificial Neural Network the main factor is – appropriate transform of all ΔE=dE/dx from 12 TRD layers to be input to ANN.

ANN was used from the ROOT package TMVA (Toolkit for MultiVariate data Analysis)

G.Ososkov et al, Electron Identification in TRD GSI, 2010 7/14

2. Methods for e- indentification (contin-1)

• Decision TreeDecision Tree (DT) (DT)

Multiple cuts on X and Y in a big tree (only grows steps 1- 4 are shown)

data sample

Final result of DT trainingon a great sample (~ 1000)

G.Ososkov et al, Electron Identification in TRD GSI, 2010 8/14

2. Methods for e- indentification (contin-2)

• Boosted Decision Tree (BDT)(BDT) • Given a training sample, boosting increases the weights of misclassifiedweights of misclassified

eventsevents (background wich is classified as signal, or vice versa), such that they have a higher chance of being correctly classified in subsequent trees.

• Trees with more misclassified events are also weighted, having a lower weight than trees with fewer misclassified events.

• Build many trees (~1000) and do a weighted sum of event scores from all trees (score is 11 if signal leaf, -1-1 if background leaf).

• The renormalized sum of all the scores, possibly weighted, is the final score of the event. High scores mean the event is most likely signal and low scores that it is most likely background.

Many weak trees (single-cut trees) combined(only 4 trees shown)

boosting algorithmproduces 500 weak trees together

G.Ososkov et al, Electron Identification in TRD GSI, 2010 9/14

BDT algorithm (I)

Two steps of the algorithm:• energy loss transform• evaluate probability using Boosted Decision Tree (BDT)

G.Ososkov et al, Electron Identification in TRD GSI, 2010 10/14

BDT algorithm (II)

Energy loss transformation: 1) Sort energy losses 2) Prepare probability density function (PDF) for ordered energy losses

• Boosted decision tree (BDT) classifier from TMVA package was used.• Before using BDT has to be trained

Transformation is very important step, without it classifiers could not be trained properly.

)()(

)(

EPDFEPDF

EPDFL

e

3) Calculate likelihood ratio for each energy loss as input to BDT:

G.Ososkov et al, Electron Identification in TRD GSI, 2010 11/14

Results, different radiator parameters

Mom, GeV/c 1 1.5 2 3 4 5 7 9 11 13Param1 1538 2892 3058 2621 2243 2149 1659 1591 1234 1189

Param2 164 400 555 593 577 529 474 421 374 314

Param3 307 660 800 845 763 690 620 525 485 395

• Statistics: around 1M electrons and 1M pions for each momentum• Standard TRD geomerty• Standard ANN and new BDT methods were used for electron identification• For each momentum ANN and BDT were trained separately

Mom, GeV/c 1 1.5 2 3 4 5 7 9 11 13Param1 933 1700 878 1394 947 754 790 838 838 878

Param2 149 348 447 428 442 322 357 370 343 317

Param3 251 534 654 713 466 401 453 438 398 371

90% electron efficiency

ANN

BDT

G.Ososkov et al, Electron Identification in TRD GSI, 2010 12/14

Results of electron Identification in TRD (I)

90% electron efficiency

electrons and pions with parameters θ = (2.5, 25), ϕ = (0, 360)for momentum 1.5 GeV/c

Method name π suppression

Boosted Decision Tree 660Artificial Neural Network 534Photon cluster counting 150Ordered statistics (mediana) 140Simple cut on energy loss sum 5

G.Ososkov et al, Electron Identification in TRD GSI, 2010 13/14

Results, ANN and BDT comparison (II)

Statistics: 1M electrons and 1M pions with parameters θ = (2.5, 25), ϕ = (0, 360) for certain momenta (1, 1.5, 2, 3, 4, 5, 7, 9, 11, 13 GeV/c).

90% electron efficiency

Pion suppression vs. momentum

Black: BDT method Blue: ANN method

G.Ososkov et al, Electron Identification in TRD GSI, 2010 14/14

Stability of the suppression algorithmOne should consider not only a pion rejection procedure, as it is, but it is

necessary to take into account its robustness to such experimental factorsexperimental factors as1. calibration of measurements, pile up of signals 2. missing one or two hits

3. erroneous substitution e- hit by hit

90% electron efficiency

BDT method was used

Keep in mind:Keep in mind:the most probable value of

Eloss for pion is around 1.05-1.5 keV

First factors were simulated by adding error to the energy loss for each hit: Eloss=Eloss+Gauss(0, Sigma)

G.Ososkov et al, Electron Identification in TRD GSI, 2010 15/14

Summary and outlook

– Investigation of three parameter sets of TR simulation shows that param3 set is the most suitable.

– Comparative analysis of various methods for Electron/Pion separation demensrated the effectiveness of BDT mehod.

– First results of stability of the electron identification methods show its robustness to Eloss measurement errors up to 30-50% of the maximum value of Eloss

– this study of the electron identification stability to effects of hit missing and substituting is planned