The Impact of Redshift on Galaxy Morphometric Classification: case studies for SDSS, DES, LSST and HST with Morfometryka

Abstract

We have carried a detailed analysis on the impact of cosmological redshift in the non-parametric approach to automated galaxy morphology classification. We artificially redshifted each galaxy from the EFIGI 4458 sample (re-centred at $z\sim 0$) simulating SDSS, DES, LSST and HST instruments set-ups over the range $0<z<1.5$. We then traced how the morphometry is degraded in each $z$ using MORFOMETRYKA. In the process, we re-sampled all catalogues to several resolutions and to a diverse signal-to-noise range, allowing us to understand the impact of image sampling and noise on our measurements separately. We summarize by exploring the impact of these effects on our capacity to perform automated galaxy supervised morphological classification by investigating the degradation of our classifier's metrics as a function of redshift for each instrument. The overall conclusion is that we can make reliable classification with MORFOMETRYKA for $z<0.2$ with SDSS, for $z<0.5$ with DES, for $z<0.8$ with LSST and for at least $z<1.5$ with HST.

Published in MNRAS

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