Electron identification capabilities of the CBM experiment at FAIR
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Electron identification capabilities of the CBM experiment at FAIR. Semen Lebedev GSI, Darmstadt, Germany and LIT JINR, Dubna, Russia Claudia Höhne GSI, Darmstadt, Germany Ivan Kisel GSI, Darmstadt, Germany Gennady Ososkov LIT JINR, Dubna, Russia [email protected] The CBM experiment.

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Electron identification capabilities of the CBM experiment at FAIR

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Electron identification capabilities of the cbm experiment at fair

Electron identification capabilities of the CBM experiment at FAIR

Semen Lebedev

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

Claudia Höhne

GSI, Darmstadt, Germany

Ivan Kisel

GSI, Darmstadt, Germany

Gennady Ososkov

LIT JINR, Dubna, Russia

[email protected]


The cbm experiment

The CBM experiment

Aim: Investigation of the QCD phasediagram -> measurement of hadronic and leptonic probes in large acceptance

MVD+STS:tracking, momentum determination, vertex reconstruction

TOF

ECAL

TRD

RICH

RICH & TRD:

electron identification  pion suppression  104

for low mass vector mesons and J/ψ reconstruction

STS

PSD

magnet

TOF: hadron identificaion

ECAL: photons, π0, γ

PSD: event characterization

up to 10MHz interaction rate →rare probes


The cbm rich detector

The CBM RICH detector

Electrons

Pions

RICH: electron identification by Cherenkov radiation

  • RICH characteristics:

  • radiator: CO2 length 1.5 m; Pth,π =4.65 GeV/c

  • glass mirrorof 6 mm thickness:

  • 3m radius; 11.8 m2 size

  • photodetector Hamamatsu H8500MAPMT:

  • 2.4 m2 -> 55k channels

All components are available on market.

Mean number of hits per electron ring isappr. 21

RICH hits (blue),found rings (red),track projections (green).

Radius versus momentum for reconstructedrings.


Reconstruction in the cbm rich detector

Reconstruction in the CBM RICH detector

Ring finding in RICH

ring-track matching

Extrapolation of STS tracks onto the photodetector plane

  • Main problems of ring recognition in CBM RICH:

  • high ring density (~80 rings per central Au-Au at 25 AGev, many secondary electrons);

  • many overlapping rings;

  • distortions and elliptic shape of the rings;

  • measurement errors: multiple scattering, B-field, detector granularity

  • ring-track matching (high density of projected tracks)


Hough transform ring finding

Hough Transform ring finding

Hough Transform:

large combinatorics => slow

Localized Hough Transform:

much less combinatorics => fast


Ring finding results

Ring finding results

Simulation: central Au+Au collisions at 25 AGeV beam energy (UrQMD)

5e+ and 5e- embedded as signal

2x Intel Xeon X5550 processors

at 2.67GHz (8 cores)

Nof fakes = 3.56 per event

Nof clones = 1.03 per event

  • Ring finder was systematically studied

  • for different variations:

  • high ring density (>factor 2);

  • reduced number of hits per ring (70%);

  • additional errors due to mirror surface

  • inhomogeneity;

Ring reconstruction efficiency for

embedded e+ and e-.

appr. 80 rings per one event

Accepted rings = rings with >= 5 hits


Electron identification in rich

Electron Identification in RICH

A

B

Electrons

Pions

Pions – dashed line

Electrons – solid line

A

Radius versus momentum for reconstructedrings in central Au+Au collisions at 25 AGeV beamenergy for UrQMD events (large RICH).

Pions which were matched to secondary electron RICH rings

B

Ring-track distance

Ellipse fitter


Trd detector

TRD detector

The CBM TRD is intended for tracking and improved electron identification for p > 1.5 GeV/c.

TR production by relativistic particles when crossingmaterial boundaries with different dielectricconstants ε.

TR production for γ > 1,000 ->

electron/pion separation

  • TRD characteristics:

  • Each station consists of several identical layers

  • Each layer consists of amultilayer dielectricradiator, and of a gaseous detector (85%Xe+15%CO2).

  • Pad readout with coordinate resolution 0.03-0.05 cm across and 0.27-3.3 cm along the pad (tracking!)


Event reconstruction in trd

Event Reconstruction in TRD

Energy loss: compare simulation (red) and experimental data (black)

  • TRD event reconstruction:

  • tracking -> collect hits in tracks (track propagation from STS)

  • electron identification -> using energy losses

electrons

Sum of energy loss in 12 layers of TRD.

Long tail of Landau distribution for π

π

Using only standard cuts is not enough -> advanced algorithms were implemented, which allow to reach pion suppression 200-700 at 90% electron efficiency.

e-

  • Methods:

  • Likelihood

  • Artificial Neural Network (ANN)

  • Ordered statistics (mediana)

  • Boosted Decision Tree (BDT)


Results of electron identification in trd i

Results of electron Identification in TRD (I)

electrons and pions with parametersθ = (2.5, 25), ϕ = (0, 360)

for certain 1.5 GeV/c momentum

  • Electron identification algorithms were

  • systematically studied for different variations:

  • TR parameters (different radiators);

  • TRD detector layout;

  • Error on energy loss resolution;

90% electron efficiency


Results of electron identification in trd ii

Results of electron Identification in TRD (II)

Statistics: 1M electrons and 1M pions with parametersθ = (2.5, 25), ϕ = (0, 360)

for certain momenta (1,1.5, 2, 3, 4, 5, 7, 9, 11, 13 GeV/c).

Pion suppression vs. momentum

Black: BDT method

Blue: ANN method

90% electron efficiency


Results of electron identification in cbm

Results of electron Identification in CBM

The RICH detectoralone yields a pion suppression factor of 500 at an electron identification efficiency of 83.3% whilein combination with TRD a factor 104 isreached at 69% efficiency.


Summary

Development of fast (parallel!) and efficient algorithm for ring recognition in CBM RICH (3 ms per event for parallel version).

Study of different algorithms for electron identification in TRD.

RICH and TRD provide sufficient good e-identification (efficiency, π suppression) to allow feasibility of low-mass vector meson and J/ψ measurements.

Summary


Electron identification capabilities of the cbm experiment at fair

Backup


Algorithm of electron identification in trd

Algorithm of electron Identification in TRD

Task: distinguish electrons and pions

using 12 energy loss measurements

  • Two steps of the algorithm:

  • energy loss transform

  • evaluate probability using BDT

  • Transform:

  • Prepare probability density function

  • (PDF) for ordered energy losses

  • (store them in file)

  • Sort energy losses

  • Calculate likelihood ratio for

  • each energy loss:

  • L = PDF_pi / PDF_pi + PDF_el

  • BDT:

  • Boosted decision tree (BDT) classifier from TMVA package was used.

  • Before using BDT has to be trained

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


Robustness of el id method

Robustness of el. id. method

  • One should considernot only apion rejection procedure,as it is, it is necessaryto take into account its robustnessto such experimental factors as calibration of measurements, pile up of signals etc.

  • Most probable value of Eloss for pion is around 1.05-1.5 keV

  • Add error to the energy loss for each hit:Eloss=Eloss+Gauss(0, Sigma)

  • BDT method was used

90% electron efficiency


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