GMGalaxies is a research programme investigating the relationship between the history of cosmic structures – such as galaxies and voids – and the properties of these structures which can be measured from images in telescopes. For example, what happened in the history of some galaxies to transform them into passive ellipticals while others, seemingly of the same mass and in the same environment, are star-forming spirals? Even such a basic question about the link between morphology and star formation has not yet been answered, revealing our theories of galaxy formation are inadequate. This is a major concern in an era where understanding the shapes of galaxies and how they relate to the underlying dark matter is essential for progress in precision cosmology.
Current research in this area rightly gives significant attention to the crucial problem of how feedback — energy input from supernovae, active galactic nuclei, and more — affect observable properties. But as well as investigating this avenue, the GMGalaxies team have pioneered and now continue to develop and apply a new technique (“genetic modification”) to investigate systematically the role of a galaxy’s merging and accretion history at high resolution, and to enhance our understanding of large scale structure in the Universe.
The genetic modification technique involves generating multiple, slightly different sets of early-Universe conditions from which a given galaxy, halo, or void will emerge. As each version of the Universe is evolved in its own computer simulation, the initial differences lead to contrasting evolutions – for instance, the galaxy might be formed earlier or later in the Universe's history, or undergo a different number of mergers with other galaxies. All this takes place in a fully cosmological setting, replicating the accretion of gas and dark matter along filaments.
This paper investigates the suppression of star formation, or “quenching,” in a suite of genetically modified simulations. Beginning with a Milky Way-mass galaxy, we modify the mass of one dwarf satellite, performing three additional simulations. Each of these subsequent galaxies have fixed large scale structure and final main halo masses; however, the modification of the LMC satellite results in differences to the halo accretion history, including changes in the sequence and timing of other dwarf satellite mergers with the main galaxy. Furthermore, these differences result in widely varying galactic evolution. Of the four galaxies, two remain star forming disks similar to our Milky Way, while two of them quench fully. The conclusion of the paper is that these subtle changes in the accretion history — as probed by the genetic modification — can drive major differences in the evolution of Milky Way mass galaxies.
Pontzen et al. (2020) EDGE: A new approach to suppressing numerical diffusion in adaptive mesh simulations of galaxy formation, submitted to MNRAS, arXiv:2009.03313 (full text)
This paper outlines a completely new use for genetic modification: improving the numerical accuracy of our galaxy formation simulations. “Adaptive mesh refinement” is a particular approach to simulations which results in very accurate treatment of features such as shocks and instabilities, and which we therefore use for studying dwarf galaxies. However, when the entire galaxy moves at high speeds across the simulation, the accuracy degrades. This is especially a problem when the speed of the galaxy is large compared to its own internal velocity dispersions (which is the case for small galaxies). Genetic modification gives us a neat solution: we can demand that the speed of the galaxy relative to the simulation rest frame is minimal. The paper shows that such an approach leads to even more accurate gas dynamics, and so better insight into small galaxies at high redshift.
Stopyra, Peiris, and Pontzen (2020) How to Build a Catalogue of Linearly-Evolving Cosmic Voids, submitted to ApJS, arXiv:2007.14395 (full text)
This paper studies voids: large and mostly empty regions that make up most of the volume of the Universe. Our goal is to understand how the formation and evolution of these voids can be understood using simple ‘linear’ models. Normally, understanding structure formation requires computationally intensive, non-linear numerical calculations. Linear models, by contrast, are simple to understand and quick to evaluate. The Zel’dovich approximation is one such linear model, and in this paper, we quantified how well it describes the density of matter as a function of distance from the centre of the void. We found that it worked well for voids with a radius larger than 5 Mpc (approximately 16 million light years). We also showed that these voids can be well-described as ‘anti-halos’ (Pontzen et al. 2016) – the precise opposite of the dense halos that host galaxies and galaxy clusters. In other words, by swapping over-dense and under-dense regions in the early Universe, voids can be transformed into halos, and vice-versa. This allows us to directly link void formation to the state of the early universe, which will help us to better understand how the universe came to be the way it is today.
Davies, Crain, and Pontzen (2020) Quenching and morphological evolution due to circumgalactic gas expulsion in a simulated galaxy with a controlled assembly history, submitted to MNRAS, arXiv:2006.13221 (full text)
This paper investigates the influence of a dark matter halo’s assembly history on the properties of its central galaxy and circumgalactic medium (CGM) in the EAGLE galaxy formation model. By employing the genetic modification technique, we can take a present-day halo hosting a star-forming, Milky Way-like disc galaxy and systematically accelerate or delay its assembly. We find that shifting the halo assembly to earlier times yields a spheroidal, quenched galaxy, while shifting to later times yields a more actively star-forming disc. This occurs because the halo assembly history modulates the ejection of gas from the CGM by AGN feedback, and thus modifies how readily the CGM can cool and fuel star formation in the central galaxy. Genetic modification allows us to forge a causal connection between these processes, as we only adjust the assembly history. These results demonstrate that the ejection of gas from the CGM is a crucial, previously under-appreciated step in galaxy quenching.
The primary code output from the GMGalaxies project is our initial conditions generator, genetIC, which is designed to create initial conditions for N-body and hydrodynamical zoom simulations which can be tweaked or ‘genetically modified’. The purpose is to make fine custom adjustments to the history and environment of a galaxy, and so enable its dependence on these factors to be investigated systematically.
GenetIC accomplishes this by generating random initial conditions and then allowing the user to specify the required variations. Changes can be made in a large variety of linear variables including, for example, the average dark matter overdensity in specified regions or sub-regions of a galaxy's Lagrangian patch. By choosing the modifications carefully, a selection of galaxies with different accretion histories can be efficiently ‘scanned’ using a relatively small number of simulations.
Assuming that you are interested in applying this technique, note that the final changes to a halo accretion history cannot be perfectly predicted from a given modification to the initial conditions. There is an art to guessing the best set of modifications to achieve a particular effect. It is important to verify that the modifications imposed have had the desired effect (probably using a cheap dark-matter-only simulation, before spending CPU time on hydrodynamics!). To see what is possible in practice and how to achieve it, take a look at our publications.
Download the latest release of the code and manual from the github releases page. The manual also includes instructions for running genetIC as a docker container.
Pynbody is an analysis package for astrophysical N-body and hydrodynamical simulations, supporting Python 3.5 and later. It enables users to analyse their simulations without worrying about file formats. The code has been used in a variety of astrophysical domains, ranging from cosmology to star and planet formation, for the last decade.
Tangos builds on the foundation of pynbody (or equivalent analysis packages such as yt) to create rich, interactive databases summarising the results of a cosmological simulation. It is particularly crucial for the GMGalaxies team because it allows us to link information about the development of galaxies over cosmic time and across simulations with different ‘genotypes’.
For more information about using these codes, please visit their github pages.