1. 1)LRC in the model of independent sources of two types. 2) Small step to the finite strings in NA61 data. E.Andronov, 13/05/14, SPbSU ALICE/NA61

2. MIS of two types [1] E.Andronov, V.Vechernin PoS(QFTHEP 2013)054 Not fusedFused

3. Basic formulae for MC simulations

4. MIS of two types Presence of covariation term is important Limit to one type case

5. MIS of two types One can not perform analytical calculations further for pT-n correlations

6. Connection between N1 and N2 Toy model Number of pomerons R

7. Connection between N1 and N2 Toy model

8. MIS of two types [1] E.Andronov, V.Vechernin PoS(QFTHEP 2013)054 Analytical result for b_{nn} and simple MC calculations with an approximation for b_{pTn} were obtained in [1] for FIXED r. Only negative pT-n correlations in this case!

9. MIS of two types Introduce in the probability of fusion “r” dependence on the number of primary strings N with following logic: Less strings-smaller probability to fuse More strings-bigger probability to fuse Candidate:

10. MIS of two types Candidate: Only MC simulations could help us calculate needed average values with this r(N) function. Except one case – when number of primary strings N does not fluctuate from event to event!

11. MIS of two types Nonfluctuating number of strings N MC simulation script for nonfluctuating number of strings N can be checked by this analytical formula

12. MIS of two types Nonfluctuating number of strings N Shift=15 AnalyticalMC

13. MIS of two types Nonfluctuating number of strings N Shift=100 AnalyticalMC

14. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC

15. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC

16. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC

17. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC

18. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC

19. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC Numerator of b_{nn}

20. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC Denominator of b_{nn}

21. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC Numerator of b_{nn}

22. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC b_{nn}

23. MIS of two types Nonfluctuating number of strings NShift=100 MC

24. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC

25. MIS of two types Fluctuating number of strings N, w[N]=2 Shift=100 MC

26. Conclusions for part1 MC generator with string fusion was developed and tested for fluctuating and nonfluctuating number of primary strings Analytical calculations and MC generator results are the same in nn case for fixed N Shark fin behavior of b_{nn} was found in the not total fusion region Only negative pT-n correlations for fixed N As positive, as negative pT-n correlations for fluctuating N P.S. Test of approximation of b_{pTn} from bachelor thesis was performed. Results are not shown here, but it turns out that approximation works quite well.

27. Part2 String length in NA61

28. String length in NA61 strings Let us consider only right slope of dN/dy distribution Let N be total number of strings in event Let: For fixed backward window on the top of the hill – p=0

29. String length in NA61 In order to calculate b_{nn} or Sigma we should know correlation between NB and NF, i.e. we should know P_N (NB,NF)

30. String length in NA61 Linearity of with eta implies linearity of q, and, consequently, omega[nF]

31. String length in NA61 Linearity of with eta implies linearity of q, and, consequently, omega[nF] 0.5 eta windows

32. String length in NA61 Linearity of with eta implies linearity of q, and, consequently, omega[nF] 0.5 eta windows Big chi-squared! Not so good

33. String length in NA61 Linearity of with eta implies linearity of q, and, consequently, omega[nF] w[N] 0.5 eta windows

34. String length in NA61 Linearity of with eta implies linearity of q, and, consequently, omega[nF] w[N] 0.5 eta windows Decent chi-squared

35. String length in NA61 Linearity of with eta implies linearity of q, and, consequently, omega[nF] Fit results: In the range of fittinf (4;5) there is configuration of B-F windows (4;4.5)-(4.5;5) For these windows delta=0.850±0.012

36. String length in NA61 More realistic: Complicated to fit data

37. Backup

38. Definitions [1] M.I. Gorenstein, M. Gazdzicki, Phys. Rev. C 84, 014904 (2011)

39. Normalization factors IPM and Independent Emitters [2] M.Gazdzicki, M.I.Gorenstein, M.Mackowiak-Pawlowska, Phys.Rev.C 88, 024907 (2013)

40. Long-range fluctuations

41. IPM and Independent Emitters Long-range fluctuations

42. Uncertainty in Delta for symmetric windows (mu_B=mu_F) ? No uncertainty for these lambdas