Learning the Universe (LtU) is a Simons Collaboration aiming to rapidly forward model the observable Universe in order to perform Bayesian inference with new Simulation Based Inference techniques, and learn both cosmological parameters as well as the initial conditions. You can learn more about the collaboration here.
The structure of the LtU approach, from initial conditions and cosmological parameters to fully forward modelled synthetic data.
I was involved in the development and testing of the LtU-ILI pipeline (Ho et al., 2024), a framework for performing Implicit Likelihood Free inference (or Simulation Based Inference) in astrophysical and cosmological settings. I also led a recent LtU paper applying synthesizer to the CAMELS simulation, using the LtU-ILI framework to perform cosmological and astrophysical parameter inference (Lovell et al., 2024). I have also explored the application of normalising flows for generative modelling of galaxy populations (Lovell et al., 2023).
References
2024
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LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, and 12 more authors
OJA, Jul 2024
ADS Bibcode: 2024OJAp....7E..54H
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schema, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable, designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra and halo point clouds; characterising progenitors in gravitational wave signals; capturing physical dust parameters from galaxy colors and luminosities; and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://github.com/maho3/ltu-ili.
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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/ .
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.
