The Galactic Halo density distribution from photometric survey data: results of a pilot study

Our goal is to recover the Galactic Halo spatial density by means of field stars. To this aim, we apply a new technique to the Capodimonte Deep Field (OACDF, Alcala' et al. 2004), as a pilot study in view of the VLT Survey Telescope (VST) stellar projects. Considering the unique chance to collect deep and wide-field photometry with the VST, our method may represent a useful tool towards a definitive mapping of the Galactic Halo. In the framework of synthetic stellar populations, turn-off stars are used to reconstruct the spatial density. The determination of the space density is achieved by comparing the data with synthetic color-magnitude diagrams (CMDs). The only assumptions involve the IMF, age and metallicity of the synthetic halo population. Stars are randomly placed in the solid angle. The contributions of the various Monte Carlo distributions (with a step of 4 kpc) along the line of sight are simultaneously varied to reproduce the observed CMD. Our result on the space density is consistent with a power-law exponent n~3 over a range of Galactocentric distances from 8 to 40 kpc.Comment: 5 pages. Accepted for publication in Astronomy and Astrophysic

Experimental evidence of delocalized states in random dimer superlattices

We study the electronic properties of GaAs-AlGaAs superlattices with intentional correlated disorder by means of photoluminescence and vertical dc resistance. The results are compared to those obtained in ordered and uncorrelated disordered superlattices. We report the first experimental evidence that spatial correlations inhibit localization of states in disordered low-dimensional systems, as our previous theoretical calculations suggested, in contrast to the earlier belief that all eigenstates are localized.Comment: 4 pages, 5 figures. Physical Review Letters (in press

Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty