1. Elliptic flow of D mesons Francesco Prino for the D2H physics analysis group PWG3, April 12 th 2010

2. 2 Goal of the analysis Measure the elliptic flow (v 2 ) of D mesons from their fully reconstructed hadronic decay channels Elliptic flow coefficient

3. 3 Methods : Event plane Event plane method(s):  Estimate the reaction plane from track azimuthal anisotropy Need to subtract from Q vector the contribution of D-meson daughters Alternatively: use azimuthal distribution of VZERO signals  Build the invariant mass of D 0 ->K , D + ->K  and D*->D 0  ->K  candidates in bins of  =  D -  2.  Extract signal vs.  and then fit to get v 2. Three approaches implemented (see next)  Correct for event plane resolution x y 22 D+D+ φ φφ

4. 4 EP method 1: N bins of  φ Extract D meson yield in N bins of  φ  How many bins in  φ can we afford, depends a lot on statistical significance we will be able to achieve N=8 would be ideal, but requires big statistics Fit the number of D mesons vs.  φ with K[1 + 2v 2 cos(2  φ) ]  Possibly sum D 0 and D + yields Toy MC simulation: 1000 D-mesons generated Input v 2 = 0.1

5. 5 EP method 2: 2 bins of  φ Non-zero v 2  difference between numbers of D ± in-plane and out-of-plane Extract number of D-mesons in 90º “cones”:  in-plane (-45<  φ<45 U 135<  φ<225)  out-of-plane (45<  φ<135 U 225<  φ<315) Quantify anisotropy as:  In case of only v 2 and perfect  2 resolution:

6. 6 EP method 3: cos(2  φ) vs. mass Build cos(2  φ) vs. mass distribution in the signal mass region and in the side-bands and then subtract contribution of side bands from peak region Toy MC simulation

7. 7 Methods: Scalar Product Scalar Product method:  2-particle correlation method Basically a refinement of the Event Plane method  Event divided in 2 sub-events Q n a and Q n b. Sub-events defined in  ranges: a=[-0.9,-0.5], b=[0.5,0.9]  Elliptic flow calculated as: where u n,i =e -in  i is the unit momentum vector of the analyzed track/candidate auto-correlation removed by subtracting contribution of particle i to Q n. Elliptic flow of signal extracted from the v 2 of candidates measured in three invariant mass region (signal +2 side bands)

8. 8 Status of the code All this methods implemented in dedicated analysis tasks  Task to fill histos of  for D-meson candidates passing the cuts has been developed and tested already with p-p events using random event plane  Code to compute the event plane from VZERO developed and implemented in our task  Event plane from tracks: Q-vector from full and sub-events by calling PWG2 flow libraries in our task Contribution of D-meson daughters subtracted from Q-vector inside our task Being compared with the output of the recently developed event plane task (by Johanna Gramling)  Code for Scalar Product method from PWG2-flow libraries Allow for track selection, daughter removal from Q-vector Multi-band approach tested and validated for  and K 0 s -> can be easily extended to 3-prong decays

9. 9 Results

10. 10 Event plane from VZERO Centrality: 30-80% AOD set: 040  Run-by-run correction for event plane flattening from the sector occupancy Tried also re-centering after efficiency equalization, bit does not improve Expected Better performance in pass2 Run Number  2 event plane (rad) without correction with correction G. Ortona

11. 11 Event plane from Tracks Centrality: 30-80% AOD set 040  pt, phi weights not used Should cure the small deviation from flatness that is observed R. Grajcarek

12. 12 D 0 mass histos in 2 bins of  φ Centrality: 30-80% AOD set: 040 N. of analyzed events:  6.97M,  3.02M in the selected centrality class C. Bianchin 5<pt<8 GeV/c pt>8 GeV/c In plane Out of plane

13. 13 D + mass histos in 2 bins of  φ AOD set: 040 Centrality: 30-80% N. of analyzed events: 10.56M  5.24M in the considered centrality class G. Ortona In plane Out of plane 3<pt<8 GeV/cpt>8 GeV/c

14. 14 D* mass histos in 2 bins of  φ Centrality: 30-80% AOD set: N. of analyzed events: 4.04M in the considered centrality interval R. Grajcarek

15. 15 From counts to anisotropy The statistics is marginal to allow a physically significant measurement of anisotropy/v 2 Back-of-the-envelope:  Statistical significance (in pt>8 GeV, 2  φ bins) ≈ 3 with 10M events  Assuming to gain x3 statistics In each  bin -> significance ≈ 5  relative stat. error ≈ 20% Propagating to anisotropy: to be compared with expected v 2 signals of the order of 0.1-0.15 Also:  Expect better significance from pass2 due to improved reconstruction + resolutions + PID  Maybe still room to improve the cuts

16. 16 Mass vs. cos(2  ) Centrality: 30-80% AOD set: 040 N. of analyzed events:  6.97M,  3.02M in the selected centrality class C. Bianchin

17. 17 SP method (1) Centrality: 30-80% AOD set: 040  Global tracks used as reference particles C. Perez C. Ivan

18. 18 SP method (2) Centrality: 30-80% AOD set: 040 Number of analyzed events: 2.67M  1.2M in the selected centrality class C. Perez C. Ivan

19. 19 Conclusions and perspectives The tools for the analysis have been developed and tested and are ready to be used Different methods implemented  From their comparison, insight into systematics Next:  The analysis will continue on pass2, where more statistics, better resolution and PID performance should be available.  Maybe, also room for improvement from cut optimization. Available statistics is very low  e.g. for the EP method no more than 2  bins possible.  Does not allow to do any claim on charm thermalization or anisotropy introduced by path-length dependence of energy loss Our (D2H) feeling: not enough for a talk at QM  Performance plots describing the analysis steps can go in the D-meson posters

20. 20 Backup slides

21. 21 D* mass histos Centrality: 30-80% AOD set: N. of analyzed events: 4.04M in the considered centrality interval R. Grajcarek

22. 22 Track selection for SP and QC Selection cuts for RP tracks:  cuts->SetParamType(kGlobal);  cuts->SetPtRange(0.2,5.);  cuts->SetEtaRange(-0.8,0.8);  cuts->SetMinNClustersTPC(70);  cuts->SetMinChi2PerClusterTPC(0.1);  cuts->SetMaxChi2PerClusterTPC(4.0);  cuts->SetMinNClustersITS(2);  cuts->SetRequireITSRefit(kTRUE);  cuts->SetRequireTPCRefit(kTRUE);  cuts->SetMaxDCAToVertexXY(0.3);  cuts->SetMaxDCAToVertexZ(0.3);  cuts->SetAcceptKinkDaughters(kFALSE);  cuts->SetMinimalTPCdedx(10.);