1 / 16

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 S.Lebedev@gsi.de. The CBM experiment.

scarlett-mckay
Download Presentation

Electron identification capabilities of the CBM experiment at FAIR

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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 S.Lebedev@gsi.de

  2. 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

  3. 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.

  4. 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)

  5. Hough Transform ring finding Hough Transform: large combinatorics => slow Localized Hough Transform: much less combinatorics => fast

  6. 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

  7. 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

  8. 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!)

  9. 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)

  10. 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

  11. 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

  12. 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.

  13. 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

  14. Backup

  15. 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.

  16. 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

More Related