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Merging is potentially the dominate process in galaxy formation, yet there isstill debate about its history over cosmic time. To address this we classifymajor mergers and measure galaxy merger rates up to z $\sim$ 3 in all fiveCANDELS fields (UDS, EGS, GOODS-S, GOODS-N, COSMOS) using deep learningconvolutional neural networks (CNNs) trained with simulated galaxies from theIllustrisTNG cosmological simulation. The deep learning architecture used isobjectively selected by a Bayesian Optmization process over the range ofpossible hyperparameters. We show that our model can achieve 90% accuracy whenclassifying mergers from the simulation, and has the additional feature ofseparating mergers before the infall of stellar masses from post mergers. Wecompare our machine learning classifications on CANDELS galaxies and comparewith visual merger classifications from Kartaltepe et al. (2015), and show thatthey are broadly consistent. We finish by demonstrating that our model iscapable of measuring galaxy merger rates, $\mathcal{R}$, that are consistentwith results found for CANDELS galaxies using close pairs statistics, with$\mathcal{R}(z) = 0.02 \pm 0.004 \times (1 +z) ^ {2.76 \pm 0.21}$. This is thefirst general agreement between major mergers measured using pairs andstructure at z < 3.

Galaxy Merger Rates up to z $\sim$ 3 using a Bayesian Deep Learning Model $-$ A Major-Merger classifier using IllustrisTNG Simulation data