# Galaxy Formation: Bayesian History Matching for the Observable Universe

arXiv: https://arxiv.org/abs/1405.4976 · HTML: https://mlsanity.com/paper/1405.4976/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2014.

## Built on by (most cited first, 4 of 4)

- [Learning to Accelerate Partial Differential Equations via Latent Global Evolution](https://mlsanity.com/paper/2206.07681/) (2022)
- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [Bayesian Emulation for Computer Models with Multiple Partial Discontinuities](https://mlsanity.com/paper/2210.10468/) (2022)
- [An Efficient Learning Method to Connect Observables](https://mlsanity.com/paper/2503.01684/) (2025)

## Abstract (arXiv)

Cosmologists at the Institute of Computational Cosmology, Durham University, have developed a state of the art model of galaxy formation known as Galform, intended to contribute to our understanding of the formation, growth and subsequent evolution of galaxies in the presence of dark matter. Galform requires the specification of many input parameters and takes a significant time to complete one simulation, making comparison between the model's output and real observations of the Universe extremely challenging. This paper concerns the analysis of this problem using Bayesian emulation within an iterative history matching strategy, and represents the most detailed uncertainty analysis of a galaxy formation simulation yet performed.