Journal article

Ultra-fast Model Emulation with PRISM: Analyzing the Meraxes Galaxy Formation Model

Ellert van der Velden, Alan R Duffy, Darren Croton, Simon J Mutch

ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES | IOP PUBLISHING LTD | Published : 2021

Abstract

We demonstrate the potential of an emulator-based approach to analyzing galaxy formation models in the domain where constraining data is limited. We have applied the open-source Python package Prism to the galaxy formation model Meraxes. Meraxes is a semianalytic model, purposely built to study the growth of galaxies during the Epoch of Reionization. Constraining such models is however complicated by the scarcity of observational data in the EoR. Prism's ability to rapidly construct accurate approximations of complex scientific models using minimal data is therefore key to performing this analysis well. This paper provides an overview of our analysis of Meraxes using measurements of galaxy s..

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University of Melbourne Researchers

Grants

Awarded by Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)


Funding Acknowledgements

E.v.d.V. would like to thank Michael Goldstein, Manodeep Sinha, and Ian Vernon for fruitful discussions and valuable suggestions. Parts of the results in this work make use of the rainforest and freeze color maps in the CMASHER package (Van der Velden 2020). We are thankful for the open-source software packages used extensively in this work, including E13TOOLS6, H5PY (Collette 2013), HICKLE (Price et al. 2018), MATPLOTLIB (Hunter 2007), MLXTEND (Raschka 2018), MPI4PY (Dalcin et al. 2005), MPI4PYD7 and NUMPY (Van der Walt et al. 2011), SCIKIT-LEARN (Pedregosa et al. 2011), SCIPY (Virtanen et al. 2020), SORTEDCONTAINERS (Jenks 2019), and TQDM.8 Parts of this research were supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. Parts of this work were performed on the OzSTAR national facility at Swinburne University of Technology. OzSTAR is funded by Swinburne University of Technology and the National Collaborative Research Infrastructure Strategy (NCRIS).