  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.
