The dynamical state of galaxy groups and their luminosity content

We analyse the dependence of the luminosity function (LF) of galaxies in groups on group dynamical state. We use the Gaussianity of the velocity distribution of galaxy members as a measurement of the dynamical equilibrium of groups identified in the Sloan Digital Sky Survey Data Release 7 by Zandivarez & Martinez. We apply the Anderson-Darling goodness-of-fit test to distinguish between groups according to whether they have Gaussian or non-Gaussian velocity distributions, i.e. whether they are relaxed or not. For these two subsamples, we compute the (0.1)r-band LF as a function of group virial mass and group total luminosity. For massive groups, , we find statistically significant differences between the LF of the two subsamples: the LFs of groups that have Gaussian velocity distributions have a brighter characteristic absolute magnitude (similar to 0.3 mag) and a steeper faint-end slope (similar to 0.25). We detect a similar effect when comparing the LF of bright [M-0.1r(group) - 5log(h) < -23.5] Gaussian and non-Gaussian groups. Our results indicate that, for massive/luminous groups, the dynamical state of the system is directly related to the luminosity of its galaxy members.

The structure and resilience of financial market networks

Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience. (C) 2012 American Institute of Physics. [doi:10.1063/1.3683467]