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

Efficient exploration and calibration of a semi-analytical model of galaxy formation with deep learning. Elliott, E. J., Baugh, C. M., & Lacey, C. G. arXiv e-prints, 2103:arXiv:2103.01072, March, 2021.

Paper abstract bibtex

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. 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 multi-dimensional parameter spaces, and can be applied more widely beyond semi-analytical galaxy formation models.

@article{elliott_efficient_2021,
	title = {Efficient exploration and calibration of a semi-analytical model of galaxy formation with deep learning},
	volume = {2103},
	url = {http://adsabs.harvard.edu/abs/2021arXiv210301072E},
	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 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. 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 multi-dimensional parameter spaces, and can
be applied more widely beyond semi-analytical galaxy formation models.},
	urldate = {2021-05-12},
	journal = {arXiv e-prints},
	author = {Elliott, Edward J. and Baugh, Carlton M. and Lacey, Cedric G.},
	month = mar,
	year = {2021},
	keywords = {Astrophysics - Astrophysics of Galaxies},
	pages = {arXiv:2103.01072},
}

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