Primordial Black Holes for the LIGO Events in the Axion-like Curvaton Model

We revise primordial black hole (PBH) formation in the axion-like curvaton model and investigate whether PBHs formed in this model can be the origin of the gravtitational wave (GW) signals detected by the Advanced LIGO. In this model, small-scale curvature perturbations with large amplitude are generated, which is essential for PBH formation. On the other hand, large curvature perturbations also become a source of primordial GWs by their second-order effects. Severe constraints are imposed on such GWs by pulsar timing array (PTA) experiments. We also check the consistency of the model with these constraints. In this analysis, it is important to take into account the effect of non-Gaussianity, which is generated easily in the curvaton model. We see that, if there are non-Gaussianities, the fixed amount of PBHs can be produced with a smaller amplitude of the primordial power spectrum.Comment: 25 pages, 8 figure

Axion Curvaton Model for the Gravitational Waves Observed by Pulsar Timing Arrays

The stochastic gravitational wave background (SGWB) recently detected by the PTA collaborations could be the gravitational waves (GWs) induced by curvature perturbations. However, primordial black holes (PBHs) might be overproduced if the SGWB is explained by the GWs induced by the curvature perturbations that follow the Gaussian distribution. This motivates models associated with the non-Gaussianity of the curvature perturbations that suppress the PBH production rate. In this work, we show that the axion curvaton model can produce the curvature perturbations that induce GWs for the detected SGWB while preventing the PBH overproduction with the non-Gaussianity.Comment: 18 pages, 4 figure

Computationally efficient algorithms for the two-dimensional Kolmogorov-Smirnov test

Goodness-of-fit statistics measure the compatibility of random samples against some theoretical or reference probability distribution function. The classical one-dimensional Kolmogorov-Smirnov test is a non-parametric statistic for comparing two empirical distributions which defines the largest absolute difference between the two cumulative distribution functions as a measure of disagreement. Adapting this test to more than one dimension is a challenge because there are 2^d-1 independent ways of ordering a cumulative distribution function in d dimensions. We discuss Peacock's version of the Kolmogorov-Smirnov test for two-dimensional data sets which computes the differences between cumulative distribution functions in 4n^2 quadrants. We also examine Fasano and Franceschini's variation of Peacock's test, Cooke's algorithm for Peacock's test, and ROOT's version of the two-dimensional Kolmogorov-Smirnov test. We establish a lower-bound limit on the work for computing Peacock's test of Omega(n^2.lg(n)), introducing optimal algorithms for both this and Fasano and Franceschini's test, and show that Cooke's algorithm is not a faithful implementation of Peacock's test. We also discuss and evaluate parallel algorithms for Peacock's test

Essential contribution of the CCL3-CCR5 axis to murine lung metastasis process

Division of Molecular Bioregulatio

Temperature Dependence of the FIR Reflectance of LaSrGaO4

The reflectance of single crystal LaSrGaO4 has been measured from approx 50 to 40000 cm^-1 along the "a" and "c" axis. The optical properties have been calculated from a Kramers-Kronig analysis of the reflectance for both polarizations. The reflectance curves have been fit using a product of Lorentzian oscillators.Comment: 12 pages including 5 figures and 2 tables. Latex file, Requires elsart.sty file and eps