Efficient exploration and calibration of a semi-analytical model of galaxy formation with deep learning

Understand

We implement a sample-efficient method for rapid and accurate emulation of semi-analytical galaxy formation models over a wide range of model outputs.

• We use ensembled deep learning algorithms to produce a fast emulator of an updated version of the GALFORM model from a small number of training examples.
• We use the emulator to explore the model's parameter space, and apply sensitivity analysis techniques to better understand the relative importance of the model parameters.
• We uncover key tensions between observational datasets by applying a heuristic weighting scheme in a Markov chain Monte Carlo framework and exploring the effects of requiring improved fits to certain datasets relative to others.