Galaxy Evolution - a multicomponent machine-learning model

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The scientific objective is to understand the formation and evolution of galaxies by applying novel statistical techniques, developed for machine-learning and artificial intelligence, to a new multi-wavelength data-base of galaxies. The statistical characterisation of the extra-galactic populations is fundamental to address the evolution of galaxies and AGN, both for empirical comparisons of populations at different epochs and for confronting theoretical models. The extraordinary wealth of data now available from deep multi-wavelength surveys, the questions being posed, and the theoretical models that address them, are rich and complex. However, the statistical measures being used (luminosity functions, template spectral energy distributions, 2-point correlation functions and ad-hoc scaling relations) have changed little since the 1970s. These outdated measures entrench our prejudices and limit our understanding. In this project you will adopt a radical, new approach. Using techniques from machine learning we will build a probabilistic generative model of the vast multi-wavelength catalogue and map data within the Herschel Extragalactic Legacy Project (HELP). This model will provide a robust probabilistic description of the observables, with limited and well defined prior assumptions. You will use this to characterise the key emission components of galaxy populations simultaneously at all wavelengths and the probabilistic relations between them. You will focus on the star forming and AGN components where understanding has been particularly limited by ad-hoc segregation and classification. The full posterior probabilities will be fully characterised, providing us with a compact "description" of the data. This then allows you to develop a tool that can be used to generate synthetic data sets, consistent with the constraining data, that can then be used to test any physically motivated model of galaxy evolution. The Herschel Extragalactic Legacy Project (HELP https://herschel.sussex.ac.uk/) is a European funded project due to complete in June 2018. HELP combines data from all the world-leading terrestrial and space observatories. These data were taken by ambitious surveys charting over 1000 square degrees of the sky at different wavelengths. HELP has added value through e.g. linking and homogenising the data, by providing high-level descriptions of the selection functions of the data, deriving new physical quantities like redshifts, stellar masses and star formation rates and new tools to access these. A key development in HELP has been the XID+ tool which uses Bayesian Inference for a hierarchical probabilistic model of the low-resolution data from e.g. Herschel using the higher resolution data from other facilities. This project will exploit the data from HELP and in particular exploit, and extend, the XID+ framework.