An Improved Technique for Increasing the Accuracy of Photometrically Determined Redshifts for ___Blended___ Galaxies

The redshift of a galaxy can be determined by one of two methods; photometric or spectroscopic. Photometric is a term for any redshift determination made using the magnitudes of light in different filters. Spectroscopic redshifts are determined by measuring the absorption spectra of the object then determining the difference in wavelength between the 'standard' absorption lines and the measured ones, making it the most accurate of the two methods. The data for this research was collected from SDSS DR8 and then separated into blended and non-blended galaxy sets; the definition of 'blended' is discussed in the Introduction section. The current SDSS photometric redshift determination method does not discriminate between blended and non-blended data when it determines the photometric redshift of a given galaxy. The focus of this research was to utilize machine learning techniques to determine if a considerably more accurate photometric redshift determination method could be found, for the case of the blended and non-blended data being treated separately. The results show a reduction of 0.00496 in the RMS error of photometric redshift determinations for blended galaxies and a more significant reduction of 0.00827 for non-blended galaxies, illustrated in Table 2