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

Elliott, Edward J and Baugh, Carlton M and Lacey, Cedric G (2021) 'Efficient exploration and calibration of a semi-analytical model of galaxy formation with deep learning.', Monthly Notices of the Royal Astronomical Society, 506 (3). pp. 4011-4030.

Abstract

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 data sets by applying a heuristic weighting scheme in a Markov chain Monte Carlo framework and exploring the effects of requiring improved fits to certain data sets relative to others. Furthermore, we demonstrate that this method can be used to successfully calibrate the model parameters to a comprehensive list of observational constraints. In doing so, we re-discover previous GALFORM fits in an automatic and transparent way, and discover an improved fit by applying a heavier weighting to the fit to the metallicities of early-type galaxies. The deep learning emulator requires a fraction of the model evaluations needed in similar emulation approaches, achieving an out-of-sample mean absolute error at the knee of the K-band luminosity function of 0.06 dex with less than 1000 model evaluations. We demonstrate that this is an extremely efficient, inexpensive, and transparent way to explore multidimensional parameter spaces, and can be applied more widely beyond semi-analytical galaxy formation models.

Item Type:Article
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1093/mnras/stab1837
Publisher statement:This article has been accepted for publication in Monthly notices of the Royal Astronomical Society. ©: 2020 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Date accepted:19 October 2020
Date deposited:18 August 2021
Date of first online publication:27 January 2021
Date first made open access:18 August 2021

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