A Catalog of Photometric Redshift and the Distribution of Broad Galaxy Morphologies

Nicholas Paul,Nicholas Virag,Lior Shamir

doi:10.3390/galaxies6020064

Abstract

We created a catalog of photometric redshift of ∼3,000,000 SDSS galaxies annotated by their broad morphology. The photometric redshift was optimized by testing and comparing several pattern recognition algorithms and variable selection strategies, and was trained and tested on a subset of the galaxies in the catalog that had spectra. The galaxies in the catalog have i magnitude brighter than 18 and Petrosian radius greater than 5.5″. The majority of these objects are not included in previous SDSS photometric redshift catalogs such as the photoz table of SDSS DR12. Analysis of the catalog shows that the number of galaxies in the catalog that are visually spiral increases until redshift of ∼0.085, where it peaks and starts to decrease. It also shows that the number of spiral galaxies compared to elliptical galaxies drops as the redshift increases.

Highlights

Most galaxies can be broadly separated into two morphological types: spiral and elliptical [1].Galaxy Zoo [2] was the first attempt to analyze the distribution of a large number of spiral and elliptical galaxies in the local universe
A more recent catalog of galaxy morphology is the catalog of ∼3,000,000 Sloan Digital Sky Survey (SDSS) galaxies [6] classified automatically using machine learning [7,8]
The primary goals of this study are to test machine learning and variable selection algorithms for computing photometric redshift, optimize it for a specific population of galaxies, and mainly apply these algorithms to provide a large catalog of galaxy morphology and photometric redshift

Summary

Introduction

Most galaxies can be broadly separated into two morphological types: spiral and elliptical [1]. Galaxy Zoo [2] was the first attempt to analyze the distribution of a large number of spiral and elliptical galaxies in the local universe. Subsets of “clean” and “superclean” datasets were used to deduce the distribution of elliptical and spiral galaxies in the local universe [3,4,5]. A more recent catalog of galaxy morphology is the catalog of ∼3,000,000 Sloan Digital Sky Survey (SDSS) galaxies [6] classified automatically using machine learning [7,8]. While the catalog is large, it is limited in the sense that the vast majority of the galaxies in that catalog do not have spectroscopic data

Objectives

Methods

Findings

Conclusion