Searching for Conformity Across Cosmic Time with Local Group and Local Volume Star Formation Histories

Abstract

Conformity denotes the correlation of properties between pairs of galaxies as a function of separation. Correlations between properties such as star formation rate (SFR), stellar mass, and specific star formation rate (sSFR) have implications for the impact of environment upon galaxy formation and evolution. Conformity between primary galaxies and satellites within the same dark matter halo has been well documented in simulations and observations. However, the existence of conformity at greater distances - known as two-halo conformity - remains uncertain. We investigate whether galaxies in the Local Volume to a distance of 4 Mpc show conformity by examining SFR, sSFR, stellar mass, and quenched fraction as a function of physical separation. Making use of the star formation histories of these galaxies, we then extend this analysis back in time to offer the first probe of conformity inside our past light cone. At the present day, we find that the stellar mass or sSFR of a galaxy correlates with the median SFR of neighboring galaxies at a separation of 2 to 3 Mpc. At a lookback time of 1 Gyr, we find a correlation with the quenched fraction of neighboring galaxies, again at 2 to 3 Mpc separation. These signals of conformity likely arise from the differences between the recent star formation histories of Local Group dwarf galaxies and those outside the Local Group. As current and future missions including JWST, Rubin, and Roman expand the sample of Local Volume galaxies, tests of conformity using star formation histories will provide an important tool for exploring spatio-temporal correlations between galaxies.

14 pages, 10 figures, accepted to ApJ 12/07/2022

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Cited by in corpus (3)

• How the Galaxy-Halo Connection Depends on Large-Scale Environment
• Assessing the connection between galactic conformity and assembly-type bias
• Insights into the dependence of galaxy properties on the environment with explainable machine learning models