Outputs (7)

FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning. (2023)
Journal Article
Kugel, R., Schaye, J., Schaller, M., Helly, J. C., Braspenning, J., Elbers, W., …Vernon, I. (2023). FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning. Monthly Notices of the Royal Astronomical Society, 526(4), 6103-6127. https://doi.org/10.1093/mnras/stad2540

To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables. In simula... Read More about FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning..

Bayesian Emulation and History Matching of JUNE (2022)
Journal Article
Vernon, I., Owen, J., Aylett-Bullock, J., Cuestra-Lazaro, C., Frawley, J., Quera-Bofarull, A., Sedgewick, A., Shi, D., Truong, H., Turner, M., Walker, J., Caulfield, T., Fong, K., & Krauss, F. (2022). Bayesian Emulation and History Matching of JUNE. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2233), Article 20220039. https://doi.org/10.1098/rsta.2022.0039

We analyse JUNE: a detailed model of Covid-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. JUNE requires substantial computational resources to evaluate, making model calibration and general unc... Read More about Bayesian Emulation and History Matching of JUNE.

JUNE: open-source individual-based epidemiology simulation (2021)
Journal Article
Aylett-Bullock, J., Cuesta-Lazaro, C., Quera-Bofarull, A., Icaza-Lizaola, M., Sedgewick, A., Truong, H., Curran, A., Elliott, E., Caulfield, T., Fong, K., Vernon, I., Williams, J., Bower, R., & Krauss, F. (2021). JUNE: open-source individual-based epidemiology simulation. Royal Society Open Science, 8(7), https://doi.org/10.1098/rsos.210506

We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic i... Read More about JUNE: open-source individual-based epidemiology simulation.

Constraints on galaxy formation models from the galaxy stellar mass function and its evolution (2016)
Journal Article
Rodrigues, L. F. S., Vernon, I., & Bower, R. G. (2017). Constraints on galaxy formation models from the galaxy stellar mass function and its evolution. Monthly Notices of the Royal Astronomical Society, 466(2), 2418-2435. https://doi.org/10.1093/mnras/stw3269

We explore the parameter space of the semi-analytic galaxy formation model GALFORM, studying the constraints imposed by measurements of the galaxy stellar mass function (GSMF) and its evolution. We use the Bayesian emulator method to quickly eliminat... Read More about Constraints on galaxy formation models from the galaxy stellar mass function and its evolution.

Galaxy Formation: Bayesian History Matching for the Observable Universe (2014)
Journal Article
Vernon, I., Goldstein, M., & Bower, R. (2014). Galaxy Formation: Bayesian History Matching for the Observable Universe. Statistical Science, 29(1), 81-90. https://doi.org/10.1214/12-sts412

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 o... Read More about Galaxy Formation: Bayesian History Matching for the Observable Universe.

Galaxy Formation: a Bayesian Uncertainty Analysis (2010)
Journal Article
Vernon, I., Goldstein, M., & Bower, R. G. (2010). Galaxy Formation: a Bayesian Uncertainty Analysis. Bayesian Analysis, 05(04), 619-670. https://doi.org/10.1214/10-ba524

In many scientific disciplines complex computer models are used to understand the behaviour of large scale physical systems. An uncertainty anal- ysis of such a computer model known as Galform is presented. Galform models the creation and evolution o... Read More about Galaxy Formation: a Bayesian Uncertainty Analysis.

The Parameter Space of Galaxy Formation (2010)
Journal Article
Bower, R., Vernon, I., Goldstein, M., Benson, A., Lacey, C., Baugh, C., …Frenk, C. (2010). The Parameter Space of Galaxy Formation. Monthly Notices of the Royal Astronomical Society, 407(4), 2017-2045. https://doi.org/10.1111/j.1365-2966.2010.16991.x

Semi-analytic models are a powerful tool for studying the formation of galaxies. However, these models inevitably involve a significant number of poorly constrained parameters that must be adjusted to provide an acceptable match to the observed Unive... Read More about The Parameter Space of Galaxy Formation.