Tracing the magnetic field morphology of the Lupus I molecular cloud

Deep R-band CCD linear polarimetry collected for fields with lines-of-sight toward the Lupus I molecular cloud is used to investigate the properties of the magnetic field within this molecular cloud. The observed sample contains about 7000 stars, almost 2000 of them with polarization signal-to-noise ratio larger than 5. These data cover almost the entire main molecular cloud and also sample two diffuse infrared patches in the neighborhood of Lupus I. The large scale pattern of the plane-of-sky projection of the magnetic field is perpendicular to the main axis of Lupus I, but parallel to the two diffuse infrared patches. A detailed analysis of our polarization data combined with the Herschel/SPIRE 350 um dust emission map shows that the principal filament of Lupus I is constituted by three main clumps acted by magnetic fields having different large-scale structure properties. These differences may be the reason for the observed distribution of pre- and protostellar objects along the molecular cloud and its apparent evolutive stage. On the other hand, assuming that the magnetic field is composed by a large-scale and a turbulent components, we find that the latter is rather similar in all three clumps. The estimated plane-of-sky component of the large-scale magnetic field ranges from about 70 uG to 200 uG in these clumps. The intensity increases towards the Galactic plane. The mass-to-magnetic flux ratio is much smaller than unity, implying that Lupus I is magnetically supported on large scales.Comment: 10 pages, 9 figures. Accepted for publication in Ap

Crime prediction through urban metrics and statistical learning

Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multicollinearity in urban indicators, it is common to find controversial conclusions about the influence of some urban indicators on crime. Machine learning ensemble-based algorithms can handle well such problems. Here, we use a random forest regressor to predict crime and quantify the influence of urban indicators on homicides. Our approach can have up to 97% of accuracy on crime prediction, and the importance of urban indicators is ranked and clustered in groups of equal influence, which are robust under slightly changes in the data sample analyzed. Our results determine the rank of importance of urban indicators to predict crime, unveiling that unemployment and illiteracy are the most important variables for describing homicides in Brazilian cities. We further believe that our approach helps in producing more robust conclusions regarding the effects of urban indicators on crime, having potential applications for guiding public policies for crime control.Comment: Accepted for publication in Physica

Microcanonical thermostatistics analysis without histograms: cumulative distribution and Bayesian approaches

Microcanonical thermostatistics analysis has become an important tool to reveal essential aspects of phase transitions in complex systems. An efficient way to estimate the microcanonical inverse temperature $\beta(E)$ and the microcanonical entropy $S(E)$ is achieved with the statistical temperature weighted histogram analysis method (ST-WHAM). The strength of this method lies on its flexibility, as it can be used to analyse data produced by algorithms with generalised sampling weights. However, for any sampling weight, ST-WHAM requires the calculation of derivatives of energy histograms $H(E)$, which leads to non-trivial and tedious binning tasks for models with continuous energy spectrum such as those for biomolecular and colloidal systems. Here, we discuss two alternative methods that avoid the need for such energy binning to obtain continuous estimates for $H(E)$ in order to evaluate $\beta(E)$ by using ST-WHAM: (i) a series expansion to estimate probability densities from the empirical cumulative distribution function (CDF), and (ii) a Bayesian approach to model this CDF. Comparison with a simple linear regression method is also carried out. The performance of these approaches is evaluated considering coarse-grained protein models for folding and peptide aggregation.Comment: 9 pages, 11 figure