Using Machine Learning to Explore the Evolution of Active Galaxies with Euclid

Lead Research Organisation: University of Bristol
Department Name: Physics

Abstract

Euclid is a European Space Agency (ESA) M-class mission, aiming to uncover the nature of the Dark Universe. This space telescope will map the majority of the extra-Galactic sky (15,000 sq. deg.) in the optical and near infrared bands with excellent spatial resolution. The combined data of Euclid and ground observations e.g. with the Large Synoptic Survey Telescope (LSST), will form possibly the largest astronomical dataset of the next decade with 10 billion detected sources. This PhD project pertains to the preparation and exploitation of Euclid data. Specifically, the candidate will be part of the Photometric Redshift Organizational Unit (OU-PHZ) and the Galaxy and AGN Evolution Science Working Group (GAE-SWG). In anticipation of the Euclid launch (~2022), we will work with currently existing public large datasets (ESO VISTA Public Surveys, KiDS, DECaLs, PANSTARRS, etc). The focus points of this project include - but are not limited to - source classification with machine-learning methods, and AGN/galaxy coevolution studies.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023992/1 01/04/2019 30/09/2027
2431509 Studentship EP/S023992/1 01/10/2020 30/09/2024 Matthew Selwood