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Inferring the photometric and size evolution of galaxies from image simulations

Abstract : Current constraints on the luminosity and size evolution of galaxies rely on catalogs extracted from multi-band surveys. However resulting catalogs are altered by selection effects difficult to model and that can lead to conflicting predictions if not taken into account properly. In this thesis we have developed a new approach to infer robust constraints on model parameters. We use an empirical model to generate a set of mock galaxies from physical parameters. These galaxies are passed through an image simulator emulating the instrumental characteristics of any survey and extracted in the same way as from observed data for direct comparison. The difference between mock and observed data is minimized via a sampling process based on adaptive Monte Carlo Markov Chain methods. Using mock data matching most of the properties of a Canada-France-Hawaii Telescope Legacy Survey Deep (CFHTLS Deep) field, we demonstrate the robustness and internal consistency of our approach by inferring the size and luminosity functions and their evolution parameters for realistic populations of galaxies. We compare our results with those obtained from the classical spectral energy distribution (SED) fitting method, and find that our pipeline infers the model parameters using only 3 filters and more accurately than SED fitting based on the same observables. We then apply our pipeline to a fraction of a real CFHTLS Deep field to constrain the same set of parameters in a way that is free from systematic biases. Finally, we highlight the potential of this technique in the context of future surveys and discuss its drawbacks.
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Submitted on : Sunday, April 22, 2018 - 1:02:52 AM
Last modification on : Thursday, December 10, 2020 - 11:05:31 AM
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  • HAL Id : tel-01773454, version 1


Sébastien Carassou. Inferring the photometric and size evolution of galaxies from image simulations. Astrophysics [astro-ph]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066382⟩. ⟨tel-01773454⟩



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