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

TL;DR

This study investigates how cosmological redshift affects automated galaxy morphology classification across different telescopes, showing that classification remains reliable up to certain redshift limits depending on the instrument used.

Contribution

It provides a detailed analysis of redshift effects on galaxy morphometry and classification, using simulated data for SDSS, DES, LSST, and HST with MORFOMETRYKA.

Findings

Reliable classification up to z<0.2 with SDSS

Reliable classification up to z<0.5 with DES

Reliable classification up to z<0.8 with LSST

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.