Microlensing path parametrization for Earth-like Exoplanet detection around solar mass stars

We propose a new parametrization of the impact parameter u0 and impact angle {\alpha} for microlensing systems composed by an Earth-like Exoplanet around a Solar mass Star at 1 AU. We present the caustic topology of such system, as well as the related light curves generated by using such a new parametrization. Based on the same density of points and accuracy of regular methods, we obtain results 5 times faster for discovering Earth-like exoplanet. In this big data revolution of photometric astronomy, our method will impact future missions like WFIRST (NASA) and Euclid (ESA) and they data pipelines, providing a rapid and deep detection of exoplanets for this specific class of microlensing event that might otherwise be lost.Comment: 8 pages, 7 figures, accepted to be published in The Astronomical Journa

Observational constraints for Lithium depletion before the RGB

Precise Li abundances are determined for 54 giant stars mostly evolving across the Hertzsprung gap. We combine these data with rotational velocity and with information related to the deepening of the convective zone of the stars to analyse their link to Li dilution in the referred spectral region. A sudden decline in Li abundance paralleling the one already established in rotation is quite clear. Following similar results for other stellar luminosity classes and spectral regions, there is no linear relation between Li abundance and rotation, in spite of the fact that most of the fast rotators present high Li content. The effects of convection in driving the Li dilution is also quite clear. Stars with high Li content are mostly those with an undeveloped convective zone, whereas stars with a developed convective zone present clear sign of Li dilution.Comment: 5 pages, 4 figures. accepted for publicatio

Modulated phases and devil's staircases in a layered mean-field version of the ANNNI model

We investigate the phase diagram of a spin-$1/2$ Ising model on a cubic lattice, with competing interactions between nearest and next-nearest neighbors along an axial direction, and fully connected spins on the sites of each perpendicular layer. The problem is formulated in terms of a set of noninteracting Ising chains in a position-dependent field. At low temperatures, as in the standard mean-feild version of the Axial-Next-Nearest-Neighbor Ising (ANNNI) model, there are many distinct spatially commensurate phases that spring from a multiphase point of infinitely degenerate ground states. As temperature increases, we confirm the existence of a branching mechanism associated with the onset of higher-order commensurate phases. We check that the ferromagnetic phase undergoes a first-order transition to the modulated phases. Depending on a parameter of competition, the wave number of the striped patterns locks in rational values, giving rise to a devil's staircase. We numerically calculate the Hausdorff dimension $D_{0}$ associated with these fractal structures, and show that $D_{0}$ increases with temperature but seems to reach a limiting value smaller than $D_{0}=1$.Comment: 17 pages, 6 figure

Hardening DGA classifiers utilizing IVAP

Domain Generation Algorithms (DGAs) are used by malware to generate a deterministic set of domains, usually by utilizing a pseudo-random seed. A malicious botmaster can establish connections between their command-and-control center (C&C) and any malware-infected machines by registering domains that will be DGA-generated given a specific seed, rendering traditional domain blacklisting ineffective. Given the nature of this threat, the real-time detection of DGA domains based on incoming DNS traffic is highly important. The use of neural network machine learning (ML) models for this task has been well-studied, but there is still substantial room for improvement. In this paper, we propose to use Inductive Venn-Abers predictors (IVAPs) to calibrate the output of existing ML models for DGA classification. The IVAP is a computationally efficient procedure which consistently improves the predictive accuracy of classifiers at the expense of not offering predictions for a small subset of inputs and consuming an additional amount of training data