All Outputs (4)

Halo merger tree comparison: impact on galaxy formation models (2021)
Journal Article
Gómez, J. S., Padilla, N., Helly, J., Lacey, C., Baugh, C., & Lagos, C. (2022). Halo merger tree comparison: impact on galaxy formation models. Monthly Notices of the Royal Astronomical Society, 510(4), 5500-5519. https://doi.org/10.1093/mnras/stab3661

We examine the effect of using different halo finders and merger tree building algorithms on galaxy properties predicted using the GALFORM semi-analytical model run on a high resolution, large volume dark matter simulation. The halo finders/tree buil... Read More about Halo merger tree comparison: impact on galaxy formation models.

Modelling emission lines in star-forming galaxies (2021)
Journal Article
Baugh, C., Lacey, C. G., Gonzalez-Perez, V., & Manzoni, G. (2022). Modelling emission lines in star-forming galaxies. Monthly Notices of the Royal Astronomical Society, 510(2), 1880-1893. https://doi.org/10.1093/mnras/stab3506

We present a new model to compute the luminosity of emission lines in star-forming galaxies and apply this in the semi-analytical galaxy formation code GALFORM. The model combines a pre-computed grid of H II region models with an empirical determinat... Read More about Modelling emission lines in star-forming galaxies.

Statistics of galaxy mergers: bridging the gap between theory and observation (2021)
Journal Article
Huško, F., Lacey, C. G., & Baugh, C. M. (2021). Statistics of galaxy mergers: bridging the gap between theory and observation. Monthly Notices of the Royal Astronomical Society, 509(4), 5918-5937. https://doi.org/10.1093/mnras/stab3324

We present a study of galaxy mergers up to z = 10 using the Planck Millennium cosmological dark matter simulation and the GALFORM semi-analytical model of galaxy formation. Utilizing the full 800 Mpc3 volume of the simulation, we studied the statisti... Read More about Statistics of galaxy mergers: bridging the gap between theory and observation.

Efficient exploration and calibration of a semi-analytical model of galaxy formation with deep learning (2021)
Journal Article
Elliott, E. J., Baugh, C. M., & Lacey, C. G. (2021). Efficient exploration and calibration of a semi-analytical model of galaxy formation with deep learning. Monthly Notices of the Royal Astronomical Society, 506(3), 4011-4030. https://doi.org/10.1093/mnras/stab1837

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 G... Read More about Efficient exploration and calibration of a semi-analytical model of galaxy formation with deep learning.