Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data

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Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data

Stivaktakis, R; Tsagkatakis, G; Moraes, B; Abdalla, F; Starck, J-L; Tsakalides, P; (2020) Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data. IEEE Transactions on Big Data , 6 (3) pp. 460-476. 10.1109/TBDATA.2019.2934475. Green open access

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Abstract

In this paper, we address the problem of spectroscopic redshift estimation in Astronomy. Due to the expansion of the Universe, galaxies recede from each other on average. This movement causes the emitted electromagnetic waves to shift from the blue part of the spectrum to the red part, due to the Doppler effect. Redshift is one of the most important observables in Astronomy, allowing the measurement of galaxy distances. Several sources of noise render the estimation process far from trivial, especially in the low signal-to-noise regime of many astrophysical observations. In recent years, new approaches for a reliable and automated estimation methodology have been sought out, in order to minimize our reliance on currently popular techniques that heavily involve human intervention. The fulfilment of this task has evolved into a grave necessity, in conjunction with the insatiable generation of immense amounts of astronomical data. In our work, we introduce a novel approach based on Deep Convolutional Neural Networks. The proposed methodology is extensively evaluated on a spectroscopic dataset of full spectral energy galaxy distributions, modelled after the upcoming Euclid satellite galaxy survey. Experimental analysis on observations of idealistic and realistic conditions demonstrate the potent capabilities of the proposed scheme.

Type:	Article
Title:	Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1109/TBDATA.2019.2934475
Publisher version:	http://dx.doi.org/10.1109/TBDATA.2019.2934475
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Astrophysics, Cosmology, Deep Learning, Convolutional Neural Networks, Spectroscopic Redshift Estimation, Euclid
UCL classification:	UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy
URI:	https://discovery.ucl.ac.uk/id/eprint/10114103