Measuring Column Densities in Quasar Outflows: VLT Observations of QSO 2359-1241

We present high resolution spectroscopic VLT observations of the outflow seen in QSO 2359-1241. These data contain absorption troughs from five resonance Fe II lines with a resolution of ~7 km/s and signal-to-noise ratio per resolution element of order 100. We use this unprecedented high quality data set to investigate the physical distribution of the material in front of the source, and by that determine the column densities of the absorbed troughs. We find that the apparent optical depth model gives a very poor fit to the data and greatly underestimates the column density measurements. Power-law distributions and partial covering models give much better fits with some advantage to power-law models, while both models yield similar column density estimates. The better fit of the power-law model solves a long standing problem plaguing the partial covering model when applied to large distance scale outflow: How to obtain a velocity dependent covering factor for an outflow situated at distances thousands of time greater than the size of the AGN emission source. This problem does not affect power-law models. Therefore, based on the better fit and plausibility of the physical model, we conclude that in QSO 2359-1241, the outflow covers the full extent of the emission source but in a non-homogeneous way.Comment: 27 pages, 6 figures, to appear on ApJ Jul 10. The full (online) version of figure 2 can be obtained here: http://www.phys.vt.edu/~arav/f2_online_version.p

Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data

Background: Large geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV ‘hotspots’ is scarce, and population-based surveillance data are not always available. Here, we evaluated the viability of using clinic-based HIV prevalence data to measure the spatial variability of HIV in South Africa and Tanzania. Methods: Population-based and clinic-based HIV data from a small HIV hyper-endemic rural community in South Africa as well as for the country of Tanzania were used to map smoothed HIV prevalence using kernel interpolation techniques. Spatial variables were included in clinic-based models using co-kriging methods to assess whether cofactors improve clinic-based spatial HIV prevalence predictions. Clinic- and population-based smoothed prevalence maps were compared using partial rank correlation coefficients and residual local indicators of spatial autocorrelation. Results: Routinely-collected clinic-based data captured most of the geographical heterogeneity described by population-based data but failed to detect some pockets of high prevalence. Analyses indicated that clinic-based data could accurately predict the spatial location of so-called HIV ‘hotspots’ in > 50% of the high HIV burden areas. Conclusion: Clinic-based data can be used to accurately map the broad spatial structure of HIV prevalence and to identify most of the areas where the burden of the infection is concentrated (HIV ‘hotspots’). Where population-based data are not available, HIV data collected from health facilities may provide a second-best option to generate valid spatial prevalence estimates for geographical targeting and resource allocation