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

Estimation of latent heat flux using satellite land surface temperature and a variational data assimilation scheme over a eucalypt forest savanna in Northern Australia

© 2019 Elsevier B.V. In this study, the performance of the combined-source variational data assimilation scheme (CS-VDA) is assessed in detail using in situ heat fluxes (i.e. sensible heat (H) and latent heat (LE)) collected at a Eucalypt forest savanna of Northern Australia (Howard Springs). The CS VDA scheme estimates surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) and meteorological data into a surface energy balance model and a dynamic model. The main objectives of this paper were to extend previous studies to a semi-arid ecosystem and to evaluate the potential of using global meteorological forcing data (GMD) to drive the CS VDA model (rather than in-situ meteorological observations). In order to study the new errors associated with the use of GMD, the effects on LE of the uncertainty in air temperature and wind speed (the two key meteorological factors that controls the total estimation error) was quantitatively characterized. Using hourly in-situ measurements as inputs, the daily-averaged LE RMSE daily was 54 W/m 2 , which agrees with the errors previously reported in the literature. As expected, replacing local meteorological data with GMD reduces the performance of the LE estimation (GMA: RMSE daily = 82 W/m 2 , GLDAS: RMSE daily = 151 W/m 2 ). However, LE RMSE values at 8-day temporal scale for GMA are RMSE 8-days = 32 W/m 2 , similar to those reported in this area for other models (MODIS (MOD16A2) and Breathing Earth System Simulator (BESS)). The error propagation analysis indicate that the CS VDA model is very sensitive to uncertainties in wind speed measurements. Moreover, there are large discrepancies between in situ and GMD wind speed. These two factors combined can explain the degradation in LE estimations. In this context, our study is a first step towards the characterization of an operational daily LE estimation scheme using hourly LST observations