Lepton identification in Au+Au at 1.23 GeV/u with HADES

1. Lepton identification in Au+Au at 1.23 GeV/uwith HADES using multi-variate analysis Szymon Harabasz for the HADES Collaboration • Outline: • Electromagnetic probes in heavy-ion collisions • The HADES experiment • Physics goals • Experimental challenges • Possible solutions • Summary

2. Electromagnetic probes in heavy ion collisions • g, g*:No strong final state interactions leave reaction volume undisturbed • Reflect whole ”history” of collision: • From pre-equilibrium phase • From thermalized medium QGP and hot hadronic gas • From vector meson decays after thermal freeze-out • They probe the electromagnetic structure of dense/hot matter Invariant mass monitors directly the spectral function

3. Observables: light vector mesons Vacuum Both r and g* have JP=1− Medium pp interactions and baryonic excitations

4. Meet the HADES Beams from SIS18: pions, protons, nuclei, Ekin=1-2 GeV/u • Search for very rare probes • Di-lepton production governed by the factor a2 • Branching ratio to e+e−: 7.14⨯10-5 • Vector meson production sub-threshold • Need for fast detector • Need for large acceptance • Excellent particle identification • Need for excellent mass resolution • Track multiplicity as large as 300 per event ( combinatorial background) •  Efficient track reconstruction

5. HADES Curiculum Vitae • Explore strongly interacting matter under extreme conditions (heavy ion collisions) • Address various aspects of baryon-resonance physics (elementary collisions) • Stage I (2002 - 2008) • Light collision systems  limited granularity of time-of-flight system • 15 peer reviewed papers since 2009 • Stage II (2012 - 2015) • Heavy collision systems (Au+Au at 1.23 GEV/u April 2012) • p-induced reactions (2014) • Stage III (2018 - …) • Excitation function for low-mass lepton pairs and (multi-)strange baryons and mesons. • Medium-heavy systems upto 8 GeV/u

6. Virtual photon emission in heavy-ion collisions Ar+KCl compared to referenceafter subtraction of contributions from h • Isolation of excess by a comparison with a measured “reference” spectrum • First evidence for radiation from the “medium”! • Excess yield scales with system size like Apart1.4 ½[pp+pn]=C+C x 2.5 - 3 w e+e- Quest for heavier systems! HADES: Phys.Rev.C84:014902,2011

7. Au+Au at 1.23 GeV/uBeam time April/May 2012 AuAu 1.23 GeV/u • HADES DAQ:Versatile, FPGA board based system using dedicated add-on boards and data/trigger/slow-control transport via serial optical links (TRBnet) • 557 hours beam Au on Au target • (1.2 - 1.5) x 106 ions per second • 8 kHz trigger rate • 200 Mbyte/s data rate • 7.3 x 109 events • 140 x 1012 Byte of data dp 1.25 GeV CC 2 GeV/u pNb 3.5 GeV pp 1.25 GeVCC 1 GeV/u Ar+KCl pp 3.5 GeV

8. The HADES event reconstruction Magnet Beam

9. The HADES event reconstruction Magnet Magnet He4 t p p+ p− Beam Beam e− e+ p− p+ K+ p He3 He4 t

10. The HADES event reconstruction e− e+ p− p+ p K+ Magnet He4 He3 t Beam

11. The HADES event reconstruction field wires signal wires Magnet Beam Q1 Q2 Q3 readout cathodes ΔQ=Q2+Q3-Q1

12. The HADES event reconstruction Magnet Beam

13. Experimental challenges Δθ • High contribution of hadrons • Possibility to match them with RICH ring and misidentify them as leptons Protons and pions Electrons out of acceptance, γ conversions, misidentified particles… → Need for deep understanding of combinatorial background MDC I and II Photon detector VUV mirror (not technical, not in scale) C4F10 radiator volume

14. Why not use combined information from many observables?

15. Why not use combined information from many observables?

16. Toolkit for MultiVariate Analysis (TMVA) • Advantages: • Not need to iteratively optimize each single cut • Finer, multidimensional decision boundary • Particle hardly accepted by a number of hard cuts may be removed by MVA • Particle removed by one hard cut but well fulfilling conditions of other cuts may be accepted by MVA • ROOT: is the analysis framework used by most (HEP)-physicists • Provide common analysis (ROOT scripts) and application framework • Currently implemented classifiers • Rectangular cut optimisation • Projective and multidimensional likelihood estimator • k-Nearest Neighbor algorithm • Fisher and H-Matrix discriminants • Function discriminant • Artificial neural networks (3 multilayer perceptron implementations) • Boosted/bagged decision trees • RuleFit • Support Vector Machine

17. 1 input layer k hidden layers 1 ouput layer ... 1 1 1 2 output classes(signal and background) . . . . . . . . . Nvar discriminating input variables i j Mk . . . . . . N M1 (“Activation” function) with: Nonlinear Analysis: Artificial Neural Networks Achieve nonlinear classifier response by “activating” output nodes using nonlinear weights Feed-forward Multilayer Perceptron Weight adjustment using analytical back-propagation

18. Use of neural network to identify leptons • Discriminating input variables: • RICH ring parameters: • Number of fired pads • Avg. charge per pad • Ring geometry parameters • Velocity β • Energy loss in MDC and TOF • Difference of charge in PreShower’s chambers • Momentum Signal Background Num. of fired RICH pads Avg. charge per RICH pad β TRAINING SAMPLE background MDC dE/dx Shower ΔQ Momentum signal

19. Neural network response function RPC region, θ < 45O TOF region, θ > 45O Output: "probability", that the given particlebelongs to signal (is a lepton) e− e+ e− e+ Leptons p− p+ p− p+ p ? Identification conditions Hadrons

20. Signal-to-background estimates using RICH rotation technique Characterizing "true" (signal) and "random" (background) track-RICH ring matches 0 5 1 0 5 1 2 4 4 2 3 3 RICH MDC Rotate RICH software-wise by 60O Correlate tracks with rings Get only random matches

21. Signal-to-background estimates using RICH rotation technique Characterizing "true" (signal) and "random" (background) track-RICH ring matches • UrQMD Au+Au@1.25 GeV/u • Max. Impact parameter 9 fm • The same analysis chain as for experimental data Understanding of background with simulation No ID cuts electrons pions protons muons SUM background from RICH rotation Background Signal

22. Signal-to-background estimates using RICH rotation technique Characterizing "true" (signal) and "random" (background) track-RICH ring matches No ID cuts MLP > 0.6 More ID cuts Background reduced by 93% Signal reduced by 21% Trade-off: purity vs. efficiency

23. Comparison to hard cutsPurity and momentum distributions Multi-variate Hard cuts purity Integrated purity: 94.8 %, 84.8 % STAR min. bias ~ 92 %PHENIX 90 % [Trento 2013]

24. Summary: • Di-leptons are excellent tool to investigate hot/dense nuclear matter formed in heavy ion collisions • Their measurement is challenging and requires effective detector system and sophisticated analysis methods • Using neural network algorithm one extracts from Au+Au HADES data a very pure sample of electrons • Next steps: • Combine identified leptons into pairs • Identify sources of combinatorial background and subtract it

25. The HADES collaboration

26. Thankyou for yourattention

27. Backup slides

28. GiBUU and HSD [Janus Weil and Ulrich Mosel 2013 J. Phys.: Conf. Ser.426 012035] [E. L. Bratkovskayaet al.Phys. Rev. C 87, 064907 (2013)]

29. Dileptons from heavy ion collisions  contribution subtracted [Janus Weil and Ulrich Mosel 2013 J. Phys.: Conf. Ser.426 012035] [PRL 98 (2007) 052302, PLB 690(2010) 118] [E. L. Bratkovskayaet al.Phys. Rev. C 87, 064907 (2013)]

30. Di-leptons and stages of a heavy-ion collision

31. 900 1000 800 900 700 800 600 700 500 600 400 500 300 400 200 300 Negatively-charged particles 100 200 0 100 0 100 100 200 200 300 300 400 400 500 500 600 Positively-charged particles 600 700 700 800 800 900 900 1000

32. Purity – definition Signal (hatched grey area) from not rotated RICH after all PID cuts and RICH matching QA cut minus background) Background (solid red area) from rotated RICH after all PID cuts and RICH matching QA cut)

33. Lepton identification results RPC region, θ < 45O TOF region, θ > 45O Identificationconditions