Simulation Based Inference (SBI), or Likelihood-free inference, is a state of the art approach to Bayesian inference that leverages the power of modern numerical simulations alongside modern neural density estimation methods. For a review, see Cranmer, Brehmer & Louppe 2020.
I have recently been working on SBI approaches to a range of problems in physics. I was involved in the LtU-ILI suite (Ho et al., 2024), a framework for performing SBI in cosmology and astrophysics. I applied this framework to forward modelled photometry from the CAMELS simulations (Lovell et al., 2024), to perform parameter inference. I’ve also been involved with a number of other projects utilising SBI approaches, for parameter inference and model comparison (de Santi et al., 2023; de Santi et al., 2023).
Figure showing the typical components and structure of an SBI methodology (courtesy of
TransferLab)
In 2024 we organised the first dedicated meeting on Simulation Based Inference for Galaxy Evolution. The next installment is scheduled for 27th - 30th May 2025 - come and join us in Bristol!
References
2024
-
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.
-
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
-
Robust Field-level Likelihood-free Inference with Galaxies
Natalí S. M. Santi, Helen Shao, Francisco Villaescusa-Navarro, and 12 more authors
ApJ, Jul 2023
Publisher: IOP ADS Bibcode: 2023ApJ...952...69D
We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. From galaxy catalogs that only contain 3D positions and radial velocities of ~1000 galaxies in tiny \{(25},{h}^{-1}}mathrm{Mpc})}^{3} volumes our models can infer the value of Ωm with approximately 12% precision. More importantly, by testing the models on galaxy catalogs from thousands of hydrodynamic simulations, each having a different efficiency of supernova and active galactic nucleus feedback, run with five different codes and subgrid models-IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE-we find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder. Furthermore, we test our models on 1024 simulations that cover a vast region in parameter space-variations in five cosmological and 23 astrophysical parameters-finding that the model extrapolates really well. Our results indicate that the key to building a robust model is the use of both galaxy positions and velocities, suggesting that the network has likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than ~10 h -1 kpc.
-
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
Natalí S. M. Santi, Francisco Villaescusa-Navarro, L. Raul Abramo, and 11 more authors
Oct 2023
Publication Title: arXiv e-prints ADS Bibcode: 2023arXiv231015234D
It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood-free inference without imposing cuts on scale. In particular, de Santi et al. (2023) developed models that could accurately infer the value of {}Omega_{}rm m} from catalogs that only contain the positions and radial velocities of galaxies that are robust to uncertainties in astrophysics and subgrid models. However, observations are affected by many effects, including 1) masking, 2) uncertainties in peculiar velocities and radial distances, and 3) different galaxy selections. Moreover, observations only allow us to measure redshift, intertwining galaxies’ radial positions and velocities. In this paper we train and test our models on galaxy catalogs, created from thousands of state-of-the-art hydrodynamic simulations run with different codes from the CAMELS project, that incorporate these observational effects. We find that, although the presence of these effects degrades the precision and accuracy of the models, and increases the fraction of catalogs where the model breaks down, the fraction of galaxy catalogs where the model performs well is over 90 %, demonstrating the potential of these models to constrain cosmological parameters even when applied to real data.
