58,810 research outputs found
Modelling multi-scale microstructures with combined Boolean random sets: A practical contribution
Boolean random sets are versatile tools to match morphological and topological properties of real structures of materials and particulate systems. Moreover, they can be combined in any number of ways to produce an even wider range of structures that cover a range of scales of microstructures through intersection and union. Based on well-established theory of Boolean random sets, this work provides scientists and engineers with simple and readily applicable results for matching combinations of Boolean random sets to observed microstructures. Once calibrated, such models yield straightforward three-dimensional simulation of materials, a powerful aid for investigating microstructure property relationships. Application of the proposed results to a real case situation yield convincing realisations of the observed microstructure in two and three dimensions
Inferring the photometric and size evolution of galaxies from image simulations
Current constraints on models of galaxy evolution rely on morphometric
catalogs extracted from multi-band photometric surveys. However, these catalogs
are altered by selection effects that are difficult to model, that correlate in
non trivial ways, and that can lead to contradictory predictions if not taken
into account carefully. To address this issue, we have developed a new approach
combining parametric Bayesian indirect likelihood (pBIL) techniques and
empirical modeling with realistic image simulations that reproduce a large
fraction of these selection effects. This allows us to perform a direct
comparison between observed and simulated images and to infer robust
constraints on model parameters. We use a semi-empirical forward model to
generate a distribution of mock galaxies from a set of physical parameters.
These galaxies are passed through an image simulator reproducing the
instrumental characteristics of any survey and are then extracted in the same
way as the observed data. The discrepancy between the simulated and observed
data is quantified, and minimized with a custom sampling process based on
adaptive Monte Carlo Markov Chain methods. Using synthetic data matching most
of the properties of a CFHTLS Deep field, we demonstrate the robustness and
internal consistency of our approach by inferring the parameters governing the
size and luminosity functions and their evolutions for different realistic
populations of galaxies. We also compare the results of our approach with those
obtained from the classical spectral energy distribution fitting and
photometric redshift approach.Our pipeline infers efficiently the luminosity
and size distribution and evolution parameters with a very limited number of
observables (3 photometric bands). When compared to SED fitting based on the
same set of observables, our method yields results that are more accurate and
free from systematic biases.Comment: 24 pages, 12 figures, accepted for publication in A&
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