Towards a consistent framework of comparing galaxy mergers in observations and simulations
L. Wang, W. J. Pearson, V. Rodriguez-Gomez

TL;DR
This study compares galaxy major merger fractions in observations and simulations across a wide stellar mass range using deep learning, finding general agreement but noting discrepancies at higher masses likely due to feedback models and measurement differences.
Contribution
It introduces a consistent framework combining forward modelling and deep learning to compare galaxy merger fractions in observations and simulations across a broad mass range.
Findings
Good agreement in merger fraction dependence for 10^9.5-10^10.5 M_sun.
Discrepancies at >10^10.5 M_sun possibly due to black hole feedback.
Minor differences attributed to simulation volume and mass measurement methods.
Abstract
Aims. We aim to perform consistent comparisons between observations and simulations on the mass dependence of the galaxy major merger fraction at low redshift over an unprecedentedly wide range of stellar masses (10^9 to 10^12 solar masses). Methods. We first carry out forward modelling of ideal synthetic images of major mergers and non-mergers selected from the Next Generation Illustris Simulations (IllustrisTNG) to include major observational effects. We then train deep convolutional neural networks (CNNs) using realistic mock observations of galaxy samples from the simulations. Subsequently, we apply the trained CNNs to real the Kilo-Degree Survey (KiDS) images of galaxies selected from the Galaxy And Mass Assembly (GAMA) survey. Based on the major merger samples, which are detected in a consistent manner in the observations and simulations, we determine the dependence of major…
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