Ultra-fast model emulation with PRISM; analyzing the Meraxes galaxy formation model

Ultra-fast model emulation with PRISM; analyzing the Meraxes galaxy formation model. van der Velden, E., Duffy, A. R., Croton, D., & Mutch, S. J. arXiv e-prints, 2011:arXiv:2011.14530, November, 2020.

Paper abstract bibtex

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 semi-analytic model, purposefully built to study the growth of galaxies during the Epoch of Reionization (EoR). 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 stellar mass densities; luminosity functions; and color-magnitude relations. We demonstrate the power of using PRISM instead of a full Bayesian analysis when dealing with highly correlated model parameters and a scarce set of observational data. Our results show that the various observational data sets constrain Meraxes differently and do not necessarily agree with each other, signifying the importance of using multiple observational data types when constraining such models. Furthermore, we show that PRISM can detect when model parameters are too correlated or cannot be constrained effectively. We conclude that a mixture of different observational data types, even when they are scarce or inaccurate, is a priority for understanding galaxy formation and that emulation frameworks like PRISM can guide the selection of such data.

@article{van_der_velden_ultra-fast_2020,
	title = {Ultra-fast model emulation with {PRISM}; analyzing the {Meraxes} galaxy formation model},
	volume = {2011},
	url = {http://adsabs.harvard.edu/abs/2020arXiv201114530V},
	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 semi-analytic model,
purposefully built to study the growth of galaxies during the Epoch of
Reionization (EoR). 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 stellar mass densities; luminosity functions; and
color-magnitude relations. We demonstrate the power of using PRISM
instead of a full Bayesian analysis when dealing with highly correlated
model parameters and a scarce set of observational data. Our results
show that the various observational data sets constrain Meraxes
differently and do not necessarily agree with each other, signifying the
importance of using multiple observational data types when constraining
such models. Furthermore, we show that PRISM can detect when model
parameters are too correlated or cannot be constrained effectively. We
conclude that a mixture of different observational data types, even when
they are scarce or inaccurate, is a priority for understanding galaxy
formation and that emulation frameworks like PRISM can guide the
selection of such data.},
	urldate = {2020-12-02},
	journal = {arXiv e-prints},
	author = {van der Velden, Ellert and Duffy, Alan R. and Croton, Darren and Mutch, Simon J.},
	month = nov,
	year = {2020},
	keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Computational Physics},
	pages = {arXiv:2011.14530},
}

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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 stellar mass densities; luminosity functions; and color-magnitude relations. We demonstrate the power of using PRISM instead of a full Bayesian analysis when dealing with highly correlated model parameters and a scarce set of observational data. Our results show that the various observational data sets constrain Meraxes differently and do not necessarily agree with each other, signifying the importance of using multiple observational data types when constraining such models. Furthermore, we show that PRISM can detect when model parameters are too correlated or cannot be constrained effectively. 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