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Roberto Trasarti-Battistoni

Publications and source records attributed to Roberto Trasarti-Battistoni.

5 recordsLinked to original sources
arXiv2005

The Topological Non-connectivity Threshold and magnetic phase transitions in classical anisotropic long-range interacting spin system

We analyze from the dynamical point of view the classical characteristics of the Topological Non-connectivity Threshold (TNT), recently introduced in F.Borgonovi, G.L.Celardo, M.Maianti, E.Pedersoli, J.Stat.Phys.,116,516(2004). This shows interesting connections among Topology, Dynamics, and Thermo-Statistics of ferro/paramagnetic phase transition in classical spin systems, due to the combined effect of anisotropy and long-range interactions.

cond-mat.stat-mech↗
arXiv2002

Euclidean and Riemannian Geometrical Approaches to Non-Extensive Thermo-Statistical Mechanics

I introduce a new geometrical approach to thermo--statistical mechanics. Here I highlight the main physical ideas, and how do they translate into geometrical language. I contrast the present approach with previous thermo--statistical--geometrical formalisms, (pseudo-)Riemannian [Weinhold 1975; Ruppeiner 1979] as well as Euclidean [Gilmore 1984; Gross& Votyakov 1999] or Euclidean--looking [Levine 1986]. I point out the relevance of this approach within the contexts of non--extensive statistical thermodynamics [Tsallis 1988, 2000]. I show how the language of Riemannian geometry can be used as a powerful investigation tool,leading to several new and profound questions, and some temptative answers, on thermo--statistical mechanics.

cond-mat.stat-mech↗
arXiv1998

A global descriptor of spatial pattern interaction in the galaxy distribution

We present the function J as a morphological descriptor for point patterns formed by the distribution of galaxies in the Universe. This function was recently introduced in the field of spatial statistics, and is based on the nearest neighbor distribution and the void probability function. The J descriptor allows to distinguish clustered (i.e. correlated) from ``regular'' (i.e. anti-correlated) point distributions. We outline the theoretical foundations of the method, perform tests with a Matern cluster process as an idealised model of galaxy clustering, and apply the descriptor to galaxies and loose groups in the Perseus-Pisces Survey. A comparison with mock-samples extracted from a mixed dark matter simulation shows that the J descriptor can be profitably used to constrain (in this case reject) viable models of cosmic structure formation.

astro-ph↗
arXiv1998

Loose Groups of Galaxies in the Perseus-Pisces Survey

We present a large catalog of loose groups of galaxies in the Southern Galactic Hemisphere, selected from the Perseus-Pisces redshift Survey (PPS). Particular care is taken in order to obtain group samples as homogeneous as possible to previously published catalogs. All our catalogs contain about 200 groups, significantly more than in most previous studies where group samples were obtained from galaxy data sets of comparable quality to (but smaller extent than) PPS. Groups are identified with the adaptive Friends-Of-Friends (FOF) algorithm of Huchra & Geller (1982), with suitable normalizations D_0=0.231 Mpc/h and V_0=350 km/s at cz_0=1000 km/s. The luminosity function (LF) normalization ϕ_*=0.02 (h/Mpc)^3} appropriate for PPS yields a number density threshold δn/n\approx 180 for the adopted D_0, instead of δn/n\approx 80 used in previous studies of other samples. We discuss how to self-consistently match FOF parameters among different galaxy samples. We then separately vary several FOF and sample parameters, and discuss their effect on group properties. Loose groups in PPS nicely trace the large scale structure (LSS) in the parent galaxy sample. Group properties vary little with different redshift corrections, redshift cut-off, and galaxy LF, but are rather sensitive to the adopted links D_0 and V_0. More precisely, the typical (i.e., median) group size (velocity dispersion) is linearly related to the adopted distance (velocity) link, while it is rather insensitive to the adopted velocity (distance) link. Physical properties of groups in PPS and in directly comparable samples show good agreement. There is a complex interplay among LSS features, sample depth, FOF grouping procedure, and group properties.(abridged)

astro-ph↗