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Measuring distances of cosmological sources such as galaxies, stars andquasars plays an increasingly critical role in modern cosmology. Obtaining theoptical spectrum and consequently calculating the redshift as a distanceindicator could instantly classify these objects. As long as spectroscopicobservations are not available for many galaxies and the process of measuringthe redshift is time-consuming and infeasible for large samples, machinelearning (ML) approaches could be applied to determine the redshifts ofgalaxies from different features including their photometric colors. In thispaper, by using the flux magnitudes from the Sloan Digital Sky Survey (SDSS)catalog, we develop two ML regression algorithms (Decision Tree and RandomForest) for estimating the redshifts taking color indices as input features. Wefind that the Random Forest algorithm produces the optimum result for theredshift prediction, and it will be further improved when the dataset islimited to a subset with z $\le$ 2 giving the normalised standard deviation$\overline{\Delta Z}_{\text {norm}}=0.005$ and the standard deviation$\sigma_{\Delta z}=0.12$. This work shows a great potential of using the MLapproach to determine the photometric redshifts of distant sources.

Estimating the Photometric Redshifts of Galaxies and QSOs Using Regression Techniques in Machine Learning