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AR 15802 (Archival Research)

Sun Jul 14 10:01:11 GMT 2024

Principal Investigator: Jeyhan Kartaltepe
PI Institution: Rochester Institute of Technology
Investigators (xml)

Title: Toward a Robust Method for Identifying Merger Signatures in Deep Hubble Images
Cycle: 27

Abstract
Galaxy mergers of all flavors (including major and minor mergers as well as gas rich and gas poor mergers) are potentially important drivers in the transformation of galaxies over cosmic time. However, mergers are incredibly difficult to reliably identify at high redshift as their tell-tale features, such as tidal tails and double nuclei, are affected by surface brightness dimming, poor resolution, and band pass shifting. New machine learning approaches to identify merger signatures show significant promise, however, a better understanding of the visibility of merger features for a range of merger stages as a function of observed wavelength, image depth, and gas fraction is greatly needed in order to make progress using these new techniques. We propose to conduct a comprehensive analysis of galaxy mergers identified across the HST deep fields, including COSMOS, CANDELS, CLASH, and the Frontier Fields with ACS and WFC3 observations. We will first use mock HST images produced from the Illustris simulation to define and test a robust method for the identification of galaxy merger signatures, quantify their level of completeness and purity as a function of image depth, merger stage, gas fraction, and observed wavelength, and then apply this methodology to the HST observations. We will then use this sample of galaxy mergers to investigate the properties of mergers, such as their position on the SFR-M* plane as a function of redshift and merger stage. The development of a robust merger identification method will be essential for future large surveys to be conducted with LSST, Euclid, and WFIRST.