Below are some brief summaries of research areas I am currently working in, or have recently worked on. If you’re interested in any of these topics and would like to chat further, please get in touch!

Sub-millimetre galaxies in simulations

Sub-millimetre galaxies, or SMGs, are an enigmatic population of galaxies in the early universe that are incredibly bright in sub-millimetre wavelengths, forming huge numbers of stars. It has been very difficult to model these objects in cosmological simulations whilst still self-consistently matching other observational constraints, such as the galaxy stellar mass function, at $z = 0$, and many authors have proposed alternative modelling approaches, such as a varibale IMF, to explain them. For a concise summary of the issues faced modelling these objects, see this thread from Desika!

An example SMG showing the gas, stellar and dust distribution, as well as the resolved and integrated $S_{850}$ emission.

Recently (Lovell et al. 2020; arXiv:2006.15156), we used the SIMBA simulation combined with the radiative transfer package Powderday to model the sub-mm emission, and found unprecedented agreement with observationally inferred integrated number counts from single-dish instruments. We created a lightcone, allowing us to explore the effects of far-field blending, and found minimal impact on the shape or normalisation of the number counts.

$S_{850}$ counts from the SIMBA simulation, compared with observational constraints, as well as predictions from the EAGLE model.

We’re currently working on studying the intrinsic properties of these galaxies in the simulation, as well as their progenitors and descendants. And we are running high resolution zoom simulations of individual objects in order to study their resolved continuum emission, and make predictions for ALMA. Stay tuned!

First Light And Reionisation Epoch Simulations (FLARES)

Cosmological hydrodynamic simulations have, in recent years, become capable of matching key distribution functions in the local universe, such as those of stellar mass and star formation rate. However, high resolution, large volume simulations have rarely been tested in the high redshift ($z > 5$) regime, particularly in the most overdense environments. Creating models that fit both high redshift and low redshift observables self consistently is a significant challenge, but key to understanding the properties of galaxies in the first billion years of the universe’s history, and how this affects their latter evolution. Such models are also necessary to make detailed predictions, and plan observations, for upcoming space based instruments, such as JWST, WFIRST and Euclid.

The column density of gas, stars and dark matter in the most overdense region in the FLARES sample.

The First Light And Reionisation Epoch Simulations (FLARES) are a suite of 40 ‘zoom’ simulations using a modified version of the EAGLE code. EAGLE is a state-of-the-art cosmological hydrodynamic simulation that has been tuned to a small number of distribution functions in the local universe. We selected regions at high redshift ($z = 4.67$), with a range of overdensities, from an enormous $(3.2 \, \mathrm{Gpc})^3$ periodic dark matter-only volume, and resimulated these with full hydrodynamics at fiducial EAGLE resolution ($m_{\mathrm{gas}} \sim 10^6 \, \mathrm{M_{\odot} \, yr^{-1}}$). I led the first release paper (Lovell et al 2020; arXiv:2004.07283) in which we study the galaxy stellar mass function, star formation rate function and star-forming sequence predictions. By weighting them appropriately we combine the regions to produce composite distribution functions, significantly extending the dynamic range compared to periodic simulations at similar resolution. We can also study the environmental dependence of galaxy formation and evolution during the Epoch of Reionisation (EoR).

Figure of merit showing the distribution of a number of simulations on a plane of dark matter element resolution against simulated volume. FLARES, whilst explicitly simulating a similar volume to similar resolution simulations, has an 'effective' volume 4 orders of magnitude larger when combining the regions.

For further details please check out our dedicated website,, where you can find data products and visualisations.

Machine Learning & Astronomy

I am keenly interested in the interface between simulations and machine learning methods. Whilst numerical models obviously do not represent the true universe, they do model the complex non-linear spatial and time dependent interactions of populations of objects. This can be important for accurately predicting intrinsic properties, something that traditional spectral energy distribution (SED) fitting techniques do not take into account. Training machines to learn these relationships, then applying these to observations, can provide unique predictions that complement existing techniques.

I recently worked with Prof. Viviana Acquaviva at City University New York applying this method to the prediction of Star Formation Histories (SFH) in the SDSS catalogue. We trained a Convolutional Neural Network to learn the relationship between spectra and SFH in the EAGLE and Illustris simulations. The paper is available here, and below is a talk I gave on this research at the Royal Astronomical Society meeting, “Machine Learning and Artificial Intelligence applied to astronomy” in March 2019 (slides available here)

Galaxy Protoclusters

Galaxy clusters are the largest collapsed objects in the universe, comprising of a highly evolved galaxy population embedded in a hot, rarefied InterCluster Medium (ICM). Their pre-collapse progenitors, known as galaxy protoclusters, are host to some of the most extreme objects (in terms of mass, star formation rate and nuclear activity) at these early times. Protoclusters are of significant interest for understanding the environmental dependence of galaxy evolution at early times, as well as the build-up, enrichment and heating of the ICM.

The dark matter distribution in a Protocluster at $z \sim 5$ simulated with the EAGLE code

Protoclusters do not yet host an X-ray emitting ICM, and so are primarily identified through 3D galaxy overdensities. In a recently accepted paper (Lovell et al. 2018) I studied in detail the relationship between galaxy overdensity and the presence and descendant mass of protoclusters in the L-galaxies semi-analytic model. The motivation for this work was to explore the systematic issues that have the greatest impact on protocluster identification. Surface overdensities of galaxies seen in narrow band photometric surveys are typically compared to simulations in order to evaluate their protocluster probability and estimate their descendant mass. I developed a more rigorous method for generating these statistics that takes in to account the completeness and purity of the protocluster galaxy population, the galaxy distribution shape, redshift space distortions and redshift uncertainties, as well as the coincidence of AGN with protoclusters.