A network-based quark-cluster algorithm. Athanasios Petridis Drew Fustin Drake University James Vary Iowa State University. April 8, 2003. Outline. The quark cluster model Applications Clusters as networks The algorithm Case studies Conclusions. The quark cluster model.
A network-based quark-cluster algorithm
Iowa State University
April 8, 2003
Isospin relations apply
Structure function ratio using up to 12q clusters in Ca (f1 = 0.72) and 6q clusters in d (f1 = 0.96).
The triangles are DGLAP-Q2-evolved points.
The data are from Amaudriuz et al. (NMC), Z. Phys. C51, 387 (1991)
J/Ψ suppression ratio including 6q clusters and final-state absorption
The two lines show the uncertainty of the model.
The data are from
Alde et al. (E772),
Phys. Rev. Lett. 66, 133 (1991).
2nd order cluster
3rd order cluster
6th order cluster
Links connect particles whose distance is smaller than Rc. EACH PARTICLE BELONGS TO ONE AND ONLY ONE CLUSTER
-- if the count is at least 1 then merge the two
columns by replacing A(i,j) with its OR
with A(k,j) and then setting A(k,j) to 0.
-- count the 1’s in each column (cluster order) and, then, count the clusters.
Average over many arrays of particles for each Rc.
A = 2
A = 4
A = 56
A = 107
A = 197
A = 4: f1 = 0.96, f2 = 0.02
A = 56: f1 = 0.90, f2 = 0.08
A = 107: f1 = 0.88, f2 = 0.09
A = 197: f1 = 0.85, f2 = 0.11
Computed values approximately agree with the ones used to describe the DIS, D-Y, and J/Ψ data
but for large nuclei they are lower.
drastically differ from those needed to describe the data.
More realistic nuclear density functions
including correlations should be introduced.