Total Hadron-Hadron Cross Sections at High Energies

We calculate total hadron-hadron cross sections at high energies using the Low-Nussinov two-gluon model of the Pomeron. The gluon exchange is represented by a phenomenological potential including screened color-Coulomb, screened confining, and spin-spin interactions. We use bound-state wave functions obtained from a potential model for mesons, and a Gaussian wave function for a proton. We evaluate total cross sections for collisions involving pion, kaon, rho, phi, D, J/psi, psi', Upsilon, Upsilon', and the proton. We find that the total cross sections increase with the square of the sum of the root-mean square radii of the colliding hadrons, but there are variations arising from the spin-spin interaction. We also calculate the total cross sections of a mixed-color charmonium state on a pion and on a proton. The dependence of the total cross section on the size and the color state of the charmonium is investigated.Comment: 19 pages, in Late

Signature of Granular Structures by Single-Event Intensity Interferometry

The observation of a granular structure in high-energy heavy-ion collisions can be used as a signature for the quark-gluon plasma phase transition, if the phase transition is first order in nature. We propose methods to detect a granular structure by the single-event intensity interferometry. We find that the correlation function from a chaotic source of granular droplets exhibits large fluctuations, with maxima and minima at relative momenta which depend on the relative coordinates of the droplet centers. The presence of this type of maxima and minima of a single-event correlation function at many relative momenta is a signature for a granular structure and a first-order QCD phase transition. We further observe that the Fourier transform of the correlation function of a granular structure exhibits maxima at the relative spatial coordinates of the droplet centers, which can provide another signature of the granular structure.Comment: 22 pages, 5 figures, in LaTex, to be published in Physical Review

Single-Event Handbury-Brown-Twiss Interferometry

Large spatial density fluctuations in high-energy heavy-ion collisions can come from many sources: initial transverse density fluctuations, non-central collisions, phase transitions, surface tension, and fragmentations. The common presence of some of these sources in high-energy heavy-ion collisions suggests that large scale density fluctuations may often occur. The detection of large density fluctuations by single-event Hanbury-Brown-Twiss interferometry in heavy-ion collisions will provide useful information on density fluctuations and the dynamics of heavy-ion collisions.Comment: 8 pages, 4 figures, invited talk presented at the XI International Workshop on Correlation and Fluctuation in Multiparticle Production, Nov. 21-24, 2006, Hangzhou, Chin

Fourier sparsity, spectral norm, and the Log-rank conjecture

We study Boolean functions with sparse Fourier coefficients or small spectral norm, and show their applications to the Log-rank Conjecture for XOR functions f(x\oplus y) --- a fairly large class of functions including well studied ones such as Equality and Hamming Distance. The rank of the communication matrix M_f for such functions is exactly the Fourier sparsity of f. Let d be the F2-degree of f and D^CC(f) stand for the deterministic communication complexity for f(x\oplus y). We show that 1. D^CC(f) = O(2^{d^2/2} log^{d-2} ||\hat f||_1). In particular, the Log-rank conjecture holds for XOR functions with constant F2-degree. 2. D^CC(f) = O(d ||\hat f||_1) = O(\sqrt{rank(M_f)}\logrank(M_f)). We obtain our results through a degree-reduction protocol based on a variant of polynomial rank, and actually conjecture that its communication cost is already \log^{O(1)}rank(M_f). The above bounds also hold for the parity decision tree complexity of f, a measure that is no less than the communication complexity (up to a factor of 2). Along the way we also show several structural results about Boolean functions with small F2-degree or small spectral norm, which could be of independent interest. For functions f with constant F2-degree: 1) f can be written as the summation of quasi-polynomially many indicator functions of subspaces with \pm-signs, improving the previous doubly exponential upper bound by Green and Sanders; 2) being sparse in Fourier domain is polynomially equivalent to having a small parity decision tree complexity; 3) f depends only on polylog||\hat f||_1 linear functions of input variables. For functions f with small spectral norm: 1) there is an affine subspace with co-dimension O(||\hat f||_1) on which f is a constant; 2) there is a parity decision tree with depth O(||\hat f||_1 log ||\hat f||_0).Comment: v2: Corollary 31 of v1 removed because of a bug in the proof. (Other results not affected.

Efficient calculation of the robustness measure R for complex networks

In a recent work, Schneider et al. (2011) proposed a new measure R for network robustness, where the value of R is calculated within the entire process of malicious node attacks. In this paper, we present an approach to improve the calculation efficiency of R, in which a computationally efficient robustness measure R' is introduced when the fraction of failed nodes reaches to a critical threshold qc. Simulation results on three different types of network models and three real networks show that these networks all exhibit a computationally efficient robustness measure R'. The relationships between R' and the network size N and the network average degree are also explored. It is found that the value of R' decreases with N while increases with . Our results would be useful for improving the calculation efficiency of network robustness measure R for complex networks.Peer ReviewedPostprint (author's final draft