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We present image-based evolution of galaxy mergers from the Illustriscosmological simulation at 12 time-steps over 0.5 < z < 5. To do so, we createdapproximately one million synthetic deep Hubble Space Telescope and James WebbSpace Telescope images and measured common morphological indicators. Using themerger tree, we assess methods to observationally select mergers with stellarmass ratios as low as 10:1 completing within +/- 250 Myr of the mockobservation. We confirm that common one- or two-dimensional statistics selectmergers so defined with low purity and completeness, leading to highstatistical errors. As an alternative, we train redshift-dependent randomforests (RFs) based on 5-10 inputs. Cross-validation shows the RFs yieldsuperior, yet still imperfect, measurements of the late-stage merger fraction,and they select more mergers in bulge-dominated galaxies. When applied toCANDELS morphology catalogs, the RFs estimate a merger rate increasing to atleast z = 3, albeit two times higher than expected by theory. This suggestspossible mismatches in the feedback-determined morphologies, but affirms thebasic understanding of galaxy merger evolution. The RFs achieve completeness ofroughly 70% at 0.5 < z < 3, and purity increasing from 10% at z = 0.5 to 60% atz = 3. At earlier times, the training sets are insufficient, motivating largersimulations and smaller time sampling. By blending large surveys and largesimulations, such machine learning techniques offer a promising opportunity toteach us the strengths and weaknesses of inferences about galaxy evolution.

Automated Distant Galaxy Merger Classifications from Space Telescope Images using the Illustris Simulation