CAMELS is “a project that aims at building bridges between cosmology and astrophysics through numerical simulations and machine learning”. You can read more about CAMELS here.
Schema showing the structure of the CAMELS suite of simulations.
I have produced synthetic photometric catalogues for CAMELS (Lovell et al., 2024) using our Synthesizer code; this catalogue of over 200 million individual sources is one of the largest sets of synthetic photometry produced from a hydrodynamic simulation to date. I have also explored the application of normalising flows for generative modelling of galaxy populations (Lovell et al., 2023).
I also ran the Swift-EAGLE model as part of the suite of simulations (Lovell et al. in prep.). Below is a video of the evolution of one of the Swift-EAGLE runs, with gas density in blue and gas temperature in red.
I have also been involved in a number of other CAMELS studies, including measurement of the impact of baryons on matter clustering (Gebhardt et al., 2024), symbolic regression combined with graph neural networks (Shao et al., 2023) and field level likelihood free inference (missing reference).
References
2024
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Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours
Christopher C. Lovell, Tjitske Starkenburg, Matthew Ho, and 9 more authors
Nov 2024
arXiv:2411.13960
We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference approach. Using the Synthesizer code we simulate the dust attenuated ultraviolet–near infrared stellar emission from galaxies in thousands of cosmological hydrodynamic simulations from the CAMELS suite, including the Swift-EAGLE, Illustris-TNG, Simba & Astrid galaxy formation models. For each galaxy we calculate the rest-frame luminosity in a number of photometric bands, including the SDSS {}textit{ugriz} and GALEX FUV & NUV filters; this dataset represents the largest catalogue of synthetic photometry based on hydrodynamic galaxy formation simulations produced to date, totalling \textgreater200 million sources. From these we compile luminosity functions and colour distributions, and find clear dependencies on both cosmology and feedback. We then perform simulation based (likelihood-free) inference using these distributions, and obtain constraints on both cosmological and astrophysical parameters. Both colour distributions and luminosity functions provide complementary information on certain parameters when performing inference. Most interestingly we achieve constraints on {}sigma_8 describing the clustering of matter. This is attributable to the fact that the photometry encodes the star formation–metal enrichment history of each galaxy; galaxies in a universe with a higher {}sigma_8 tend to form earlier and have higher metallicities, which leads to redder colours. We find that a model trained on one galaxy formation simulation generalises poorly when applied to another, and attribute this to differences in the subgrid prescriptions, and lack of flexibility in our emission modelling. The photometric catalogues are publicly available at: https://camels.readthedocs.io/ .
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Cosmological baryon spread and impact on matter clustering in CAMELS
Matthew Gebhardt, Daniel Anglés-Alcázar, Josh Borrow, and 9 more authors
MNRAS, Apr 2024
Publisher: OUP ADS Bibcode: 2024MNRAS.529.4896G
We quantify the cosmological spread of baryons relative to their initial neighbouring dark matter distribution using thousands of state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. We show that dark matter particles spread relative to their initial neighbouring distribution owing to chaotic gravitational dynamics on spatial scales comparable to their host dark matter halo. In contrast, gas in hydrodynamic simulations spreads much further from the initial neighbouring dark matter owing to feedback from supernovae (SNe) and active galactic nuclei (AGN). We show that large-scale baryon spread is very sensitive to model implementation details, with the fiducial SIMBA model spreading ~40 per cent of baryons \textgreater1 Mpc away compared to ~10 per cent for the IllustrisTNG and ASTRID models. Increasing the efficiency of AGN-driven outflows greatly increases baryon spread while increasing the strength of SNe-driven winds can decrease spreading due to non-linear coupling of stellar and AGN feedback. We compare total matter power spectra between hydrodynamic and paired N-body simulations and demonstrate that the baryonic spread metric broadly captures the global impact of feedback on matter clustering over variations of cosmological and astrophysical parameters, initial conditions, and (to a lesser extent) galaxy formation models. Using symbolic regression, we find a function that reproduces the suppression of power by feedback as a function of wave number (k) and baryonic spread up to \k }sim 10}, h Mpc-1 in SIMBA while highlighting the challenge of developing models robust to variations in galaxy formation physics implementation.
2023
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A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship
Christopher C. Lovell, Sultan Hassan, Daniel Anglés-Alcázar, and 8 more authors
ICML, Jul 2023
Publication Title: arXiv e-prints ADS Bibcode: 2023arXiv230706967L
Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a normalizing flow (NF) to map the probability of various galaxy and halo properties conditioned on astrophysical and cosmological parameters. By leveraging the learnt conditional relationships we can explore a wide range of interesting questions, whilst enabling simple marginalisation over nuisance parameters. We demonstrate how the model can be used as a generative model for arbitrary values of our conditional parameters; we generate halo masses and matched galaxy properties, and produce realisations of the halo mass function as well as a number of galaxy scaling relations and distribution functions. The model represents a unique and flexible approach to modelling the galaxy-halo relationship.
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A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Helen Shao, Natalí S. M. Santi, Francisco Villaescusa-Navarro, and 14 more authors
ApJ, Oct 2023
Publisher: IOP ADS Bibcode: 2023ApJ...956..149S
We discover analytic equations that can infer the value of Ωm from the positions and velocity moduli of halo and galaxy catalogs. The equations are derived by combining a tailored graph neural network (GNN) architecture with symbolic regression. We first train the GNN on dark matter halos from Gadget N-body simulations to perform field-level likelihood-free inference, and show that our model can infer Ωm with ~6% accuracy from halo catalogs of thousands of N-body simulations run with six different codes: Abacus, CUBEP3M, Gadget, Enzo, PKDGrav3, and Ramses. By applying symbolic regression to the different parts comprising the GNN, we derive equations that can predict Ωm from halo catalogs of simulations run with all of the above codes with accuracies similar to those of the GNN. We show that, by tuning a single free parameter, our equations can also infer the value of Ωm from galaxy catalogs of thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, each with a different astrophysics model, run with five distinct codes that employ different subgrid physics: IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE. Furthermore, the equations also perform well when tested on galaxy catalogs from simulations covering a vast region in parameter space that samples variations in 5 cosmological and 23 astrophysical parameters. We speculate that the equations may reflect the existence of a fundamental physics relation between the phase-space distribution of generic tracers and Ωm, one that is not affected by galaxy formation physics down to scales as small as 10 h -1 kpc.
