Metallicity dependence of HMXB populations

High-mass X-ray binaries (HMXBs) might have contributed a non-negligible fraction of the energy feedback to the interstellar and intergalactic media at high redshift, becoming important sources for the heating and ionization history of the Universe. However, the importance of this contribution depends on the hypothesized increase in the number of HMXBs formed in low-metallicity galaxies and in their luminosities. In this work we test the aforementioned hypothesis, and quantify the metallicity dependence of HMXB population properties. We compile from the literature a large set of data on the sizes and X-ray luminosities of HMXB populations in nearby galaxies with known metallicities and star formation rates. We use Bayesian inference to fit simple Monte Carlo models that describe the metallicity dependence of the size and luminosity of the HMXB populations. We find that HMXBs are typically ten times more numerous per unit star formation rate in low-metallicity galaxies (12 + log(O/H) < 8, namely < 20% solar) than in solar-metallicity galaxies. The metallicity dependence of the luminosity of HMXBs is small compared to that of the population size. Our results support the hypothesis that HMXBs are more numerous in low-metallicity galaxies, implying the need to investigate the feedback in the form of X-rays and energetic mass outflows of these high-energy sources during cosmic dawn.Comment: 9 pages, 5 figures, accepted for publication in Astronomy & Astrophysic

Estimating flooded area and mean water level using active and passive microwaves: the example of Paraná River Delta floodplain

This paper describes a procedure to estimate both the fraction of flooded area and the mean water level in vegetated river floodplains by using a synergy of active and passive microwave signatures. In particular, C band Envisat ASAR in Wide Swath mode and AMSR-E at X, Ku and Ka band, are used. The method, which is an extension of previously developed algorithms based on passive data, exploits also model simulations of vegetation emissivity. The procedure is applied to a long flood event which occurred in the Paraná River Delta from December 2009 to April 2010. Obtained results are consistent with in situ measurements of river water level

Towards a remote sensing data based evapotranspiration estimation in Northern Australia using a simple random forest approach

In this work we have developed a random forest regressor to predict daily evapotranspiration in three eddy-covariance sites in Northern Australia from in-situ meteorological data and fluxes, and satellite leaf area index and land surface temperature data. The variable analysis for the random forest regressor suggests that leaf area index is the most important one at this temporal scale. A development sample corresponding to the period 2010–2013 was used, while the year 2014 has been separated for testing. Using this approach, we have obtained satisfactory performances in the three sites, with RMSE errors around 1 mm/day (and relative RMSEs ~0.3) in comparison to the measured values. With the final aim of testing the predictive capability of a model that uses remote sensing data as input, regressors that predict evapotranspiration exclusively from leaf area index and land surface temperature, have been trained resulting in reasonable performances. The RMSEs over the test set are ~1.1−1.2 mm/day. In all cases, the errors are comparable to those obtained in previous works that use similar locations and different methods. When compared to the measured values, the random forest predictions of evapotranspiration are more accurate than the global MODIS ET 8-day 1 km product, which was used as benchmark for the performance evaluation of our models, in the three selected locations