Euclid Cosmic Visions Grant

Lead Research Organisation: University of Nottingham
Department Name: Sch of Physics & Astronomy

Abstract

We will provide new methods for using Euclid data in Legacy.

Planned Impact

Impact will be on new methods for dealing with Euclid data and the development of methods and ideas that will lead to outreach efforts.

Publications

10 25 50
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Bisigello L (2019) Statistical Stellar Mass Corrections for High- z Galaxies Observed with JWST Broadband Filters Due to Template Degeneracies in The Astrophysical Journal Supplement Series

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Bisigello L (2020) Euclid : the selection of quiescent and star-forming galaxies using observed colours in Monthly Notices of the Royal Astronomical Society

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Cheng T (2021) Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks in Monthly Notices of the Royal Astronomical Society

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Cheng Ting-Yun (2020) Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging in Monthly Notices of the Royal Astronomical Society

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Cheng Ting-Yun (2019) Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder in arXiv e-prints

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Collaboration E (2019) Euclid preparation: VII. Forecast validation for Euclid cosmological probes in arXiv e-prints

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Collaboration E (2019) Euclid preparation: VI. Verifying the Performance of Cosmic Shear Experiments in arXiv e-prints

 
Description Euclid consortium 
Organisation University of Nottingham
Department University of Nottingham Museum
Country United Kingdom 
Sector Academic/University 
PI Contribution We lead the legacy (non cosmological) sciences with this grant in the UK. It provides essential funding to carry out our work.
Collaborator Contribution We are carrying out analyses to determine how to measure galaxy evolution and gravitational lensing.
Impact publicans listed elsewhere
Start Year 2012