K 0 sk 0 s correlations in 7 TeV pp collisions from the ALICE experiment at the LHC

Transcription

1 K 0 sk 0 s correlations in 7 TeV pp collisions from the ALICE experiment at the LHC Tom Humanic Ohio State University for the ALICE Collaboration 1 WPCF 2011 Tokyo, Japan

2 Motivation for studying K 0 sk 0 s correlations in ALICE K 0 s is a different kind of boson -> complements ππ and charged KK studies and extends k T to higher values K 0 s is uncharged so no final-state Coulomb effects previous K 0 sk 0 s studies suffered from a lack of statistics -- in ALICE, high energy and long running periods allow better statistics K 0 sk 0 s state is interesting: K 0 sk 0 s> = 1/2 ( K 0 K 0 > + K 0 K 0 > + K 0 K 0 > + K 0 K 0 >) { Symmetric due to BE { Dominated by a 0 /f 0 (980) resonance 2 thus, both quantum statistics and strong interactions contribute to the femtoscopic enhancement.

3 Results from other experimental K 0 sk 0 s Bose-Einstein studies * e + e - and e ± p collisions (figure from Phys.Lett.B652:1-12,2007 ) (HERA) Phys.Lett.B652:1-12,2007 * Au+Au collisions at sqrt(s NN ) = 200 GeV (RHIC) STAR K 0 s K0 s R inv = 4.09 ± 0.46 ± 0.31 fm, λ = 0.92 ± 0.23 ± 0.13 Phys. Rev. C74: (2006) The present K 0 s K0 s study is the first to show source parameters for 3 pp collisions and for different kt and multiplicity classes

4 K 0 s identification (from V 0 h + h - ) Fit a Quadratic + Gaussian to the K 0 s mass peak and take the Quadratic to represent the background Signal/(Signal+Background) = G/(G+Q), calculated in 0.49 < mass < GeV/c 2 K 0 s Cuts used: η < < p T < 3.5 GeV/c S/(S+B) 4

5 K 0 sk 0 s Real and Background pairs distributions and C(Q inv ) All multiplicity and k T 5 Non-flat baseline as in ππ for 7 TeV p+p

6 Identity of the mystery peak in K 0 sk 0 s real distribution From PDG: f 2 (1525) mass =1525 ± 5 MeV/c 2 Γ = 73 ± 6 MeV/c 2 Main decay mode _ f 2 (1525) KK (89%) 6

7 Use PYTHIA to model the baseline for K 0 s K0 s C(Q inv ) as was done for ππ Make quadratic and Gaussian fits to PYTHIA to extract baseline parameters for C(Q inv ) C(Q inv ) = N{1 + aq inv + bq inv2 } F(Q inv ) C(Q inv ) = N{1 + a exp[(bq inv ) 2 ]} F(Q inv ) k T May 1, 2011 where F(Q inv ) is the femtoscopic part containing, in general, quantum statistics and strong interaction effects which depend on R inv and the λ parameter. 7 M

8 ALICE data 7 TeV p+p k T Gaussian fits to data C(Q inv ) with Quadratic PYTHIA baseline C(Q inv ) = N{1 + aq inv + bq inv2 } F(Q inv ) 8 M

9 Lednicky fit to data to take into account the a 0 /f 0 resonance in K 0 K 0 channel 9 * A strong final-state interaction has an important effect on neutral kaon correlations due to the f 0 (980) and a 0 (980) resonances which contribute to the K 0 K 0 -bar channel. * Use the Lednicky & Lyuboshitz analytical model and code to take into account this strong FSI assuming s-wave scattering: (R. Lednicky and V.L. Lyuboshitz, Sov.J.Nucl.Phys. 35,770 (1982)) * The code assumes a Gaussian distribution of the K 0 source points, and so one fits the model to the experimental correlation function to extract the Gaussian R and λ parameters from both quantum statistics and strong FSI. * STAR used this method to fit their K 0 s K0 s correlation function from RHIC Au+Au collisions (Phys.Rev.C74:054902,2006).

10 Example of Lednicky fit to data with quadratic baseline model divided out Big effect! ~30% reduction for R and ~50% for λ STAR saw a ~20% reduction in R and λ for Au+Au collisions. 10

11 λ parameters vs. k T and multiplicity class from Lednicky code fits and PYTHIA baseline statistical + systematic error bars (including ±10% shift in baseline parameters) λ shows a mostly flat k T dependence and is at an overall level of ~ similar to ALICE ππ results λ < 1 could be due to long-lived resonances which decay into K 0 s such as φ and K 0 * 11

12 12 R vs. k T, m T and multiplicity class from Lednicky code fits with PYTHIA baseline compared with ALICE ππ correlation data statistical + systematic error bars (including ±10% shift in baseline parameters)

13 To better evaluate the systematic error due to modeling the baseline, also use PHOJET to model the baseline -- comparing with PYTHIA results in mostly smaller systematic errors on R. Use these results to calculate the weighted average over k T of R <R>, for each of the ππ and K 0 s K0 s multiplicity classes shown: A similar increase in <R> for increasing multiplicity is seen for both ππ and K 0 sk 0 s 13

14 Fit a linear function, R = m 1 [1 + m 2 (k T - 0.7)], to each multiplicity class for ππ and K 0 s K0 s to the data (PYTHIA/PHOJET baseline), extract m 1 and m 2 : m 1 gives R at approximate ππ -- K 0 s K0 s overlap point at k T = 0.7 GeV/c m 2 gives slope of R vs. k T 14 Shows ππ and K 0 s K0 s R s agree within errors at data overlap area m 2 <0 shows K 0 s K0 s R decreases with increasing k T for the M1-11 and M12-22 multiplicity classes

15 Summary for K 0 sk 0 s 7 TeV pp analysis * The present K 0 s K0 s study is the first to show source parameters for pp collisions and for different k T and multiplicity classes. * The K 0 s K0 s results for the λ parameter show a mostly flat k T dependence which is at an overall level of ~ , similar to that seen in the ALICE ππ results for 7 TeV pp. * The K 0 s K0 s results for R show a tendency for R to decrease with increasing k T and to increase for increasing event multiplicity class as also seen in the ALICE ππ results for 7 TeV pp and in heavy-ion collisions. * Comparing with ππ, the K 0 s K0 s results for R extend the covered range of k T to ~2 GeV/c (3 larger than ππ). No discontinuity for the k T dependence of R is seen between ππ and K 0 s K0 s. 15

16 Backup slides

17 Details of the analysis: 7 TeV p+p K 0 sk 0 s K 0 s Cuts: * η < 0.8, 0.4 < p T < 3.5 GeV/c * Identification: K 0 s π+ π - (V 0 h + h - ) * < Reconstructed V 0 mass < GeV/c 2 * DCA of V 0 daughters < 0.1 cm Correlation function: * C(Q inv ) = R(Q inv )/B(Q inv ) R real pairs per event, B pairs from 10 mixed events * Form in 3 event multiplicity x 4 k T bins Fits used to extract R and λ from C(Q inv ): * C(Q inv ) = N{1 + aq inv + bq inv2 } {1 + λ exp[-(q inv R) 2 ]} * C(Q inv ) = N{1 + a exp[(bq inv ) 2 ]} {1 + λ exp[-(q inv R) 2 ]} * C(Q inv ) = N{ baseline } { Lednicky code with a 0 /f 0 resonance FSI } where the baseline parameters a and b are fixed by fits to PYTHIA with the Perugia-0 tune and PHOJET

18 k T Gaussian fits to PYTHIA to extract baseline parameters for C(Q inv ) C(Q inv ) = N{1 + a exp[(bq inv ) 2 ]} F(Q inv ) M

19 ALICE data 7 TeV p+p k T Gaussian fits to data C(Q inv ) with Gaussian PYTHIA baseline C(Q inv ) = N{1 + a exp[(bq inv ) 2 ]} {1 + λ exp[-(q inv R) 2 ]} M

20 R and λ from Lednicky fits to data for the 12 multiplicity-k T bins and for Quadratic vs. Gaussian fits to PYTHIA for the baseline statistical error bars

21 R and λ from data using the various fitting functions and for several multiplicity bins