Fitting the integrated Spectral Energy Distributions of Galaxies

Fitting the spectral energy distributions (SEDs) of galaxies is an almost universally used technique that has matured significantly in the last decade. Model predictions and fitting procedures have improved significantly over this time, attempting to keep up with the vastly increased volume and quality of available data. We review here the field of SED fitting, describing the modelling of ultraviolet to infrared galaxy SEDs, the creation of multiwavelength data sets, and the methods used to fit model SEDs to observed galaxy data sets. We touch upon the achievements and challenges in the major ingredients of SED fitting, with a special emphasis on describing the interplay between the quality of the available data, the quality of the available models, and the best fitting technique to use in order to obtain a realistic measurement as well as realistic uncertainties. We conclude that SED fitting can be used effectively to derive a range of physical properties of galaxies, such as redshift, stellar masses, star formation rates, dust masses, and metallicities, with care taken not to over-interpret the available data. Yet there still exist many issues such as estimating the age of the oldest stars in a galaxy, finer details ofdust properties and dust-star geometry, and the influences of poorly understood, luminous stellar types and phases. The challenge for the coming years will be to improve both the models and the observational data sets to resolve these uncertainties. The present review will be made available on an interactive, moderated web page (sedfitting.org), where the community can access and change the text. The intention is to expand the text and keep it up to date over the coming years.Comment: 54 pages, 26 figures, Accepted for publication in Astrophysics & Space Scienc

Modelling heterogeneity and the impact of chemotherapy and vaccination against human hookworm

There is a growing emphasis on the development of vaccines against helminths (worms), and mathematical models provide a useful tool to assess the impact of new vaccines under a range of scenarios. The present study describes a stochastic individual-based model to assess the relative impact of chemotherapy and vaccination against human hookworm infection and investigates the implications of potential correlations between risk of infection and vaccine efficacy. Vaccination is simulated as a reduction in susceptibility to infection and the model includes population heterogeneities and dynamical waning of protection. To help identify appropriate measures of vaccine impact, we present a novel framework to quantify the vaccine impact on the infection-associated morbidity and introduce a measure of symmetry to study the correspondence between reduction in intensity and reduction in morbidity. Our modelling shows that, in high-transmission settings, the greatest impact of vaccination will be attained when vaccine efficacy is the greatest among individuals harbouring the heaviest worm burdens, and that the decline of morbidity primarily depends on the level of protection attained in the most at risk 8–12% of the population. We also demonstrate that if risk of infection and vaccine protection are correlated, there is not always a direct correspondence between the reduction in worm burden and in morbidity, with the precise relationship varying according to transmission setting

Proving Properties of Constraint Logic Programs by Eliminating Existential Variables

We propose a method for proving first order properties of constraint logic programs which manipulate finite lists of real numbers. Constraints are linear equations and inequations over reals. Our method consists in converting any given first order formula into a stratified constraint logic program and then applying a suitable unfold/fold transformation strategy that preserves the perfect model. Our strategy is based on the elimination of existential variables, that is, variables which occur in the body of a clause and not in its head. Since, in general, the first order properties of the class of programs we consider are undecidable, our strategy is necessarily incomplete. However, experiments show that it is powerful enough to prove several non-trivial program properties