Extracting the Full Cosmological Information of Galaxy Surveys with SimBIG | Tues, Feb 11, 2025

[Grigory Bronevetsky]

Modeling Talks

Extracting the Full Cosmological Information of Galaxy Surveys with SimBIG ChangHoon Hahn, University of Arizona Tues, Feb 11, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/extracting-the-full-cosmological-information-of-galaxy-surveys-with-simbig More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: Galaxy surveys of the next decade will observe hundreds of millions of galaxies over unprecedented cosmic volumes and produce detailed 3D maps of galaxies. These maps encode the growth and expansion histories of the Universe that can be used to precisely test the standard “Lambda-CDM” cosmological model and probe the nature of dark energy. While current analyses extract some of this cosmological information by summarizing the galaxy maps into 2-point clustering statistics, much more information still remain in the data. In my talk, I will present how we can use simulation-based inference and leverage high-fidelity cosmological simulations to extract the full cosmological information of galaxy surveys. Specifically, I will present SimBIG, a galaxy clustering analysis framework using simulation-based inference with normalizing flows. I will show the latest results from applying SimBIG to data from current galaxy surveys and showcase the improvements we find over the current baseline analyses. Lastly, I will discuss how SimBIG will be extended to the next-generation galaxy surveys to produce even more precise tests of the Lambda-CDM model and probe dark energy across cosmic history.  Bio: ChangHoon Hahn is an assistant professor at the University of Arizona and Steward Observatory, where he is a member of the Arizona Cosmology Lab. His research focuses on applying astrostatistics and machine learning methods to the millions of galaxies observed by galaxy surveys to answer fundamental questions in cosmology and galaxy evolution. Before Arizona, he was a research scholar at Princeton and a postdoctoral fellow at Lawrence Berkeley National Lab and UC Berkeley Center for Cosmological Physics. He received his PhD from NYU Center for Cosmology and Particle Physics.

[Grigory Bronevetsky]

Video Recording: https://youtube.com/live/ZQWcVbGDYQ0
Slides: https://drive.google.com/file/d/1xa1emLa5nFjmLgC8l2bAz1yO8E235bma/view?usp=sharing

Summary:

• Focus: Analysis of cosmological survey data

• LambdaCDM model:

  • History of the universe:

    • Big Bang

    • Cosmic Inflation

    • Galaxies form, dark matter is 85% of matter

    • 10B years: dominated by dark energy driving cosmological expansion

  • Model Parameters

    • How much matter

    • How much baryons

    • How fast universe is expanding

    • How ripples in early universe were distributed

    • How clumpy the universe is at large scales

• Challenge: different families of measurements (early vs late universe) predict different values of of the expansion rate parameters

  • Using data from galaxy observations

  • Large-scale galaxy structures depend on 

    • Expansion of universe

    • Gravitational attraction and motion of galaxy clusters

    • Sound wave propagation through early universe baryonic matter

  • Currently using coarse statistical analysis based on co-occurence of galaxy pairs/structures under different evolution/statistical assumptions

• Approach:

  • Use simulations of galaxy to predict spatial distribution of galaxies and compare to galaxy survey data. Infer best simulation parameters based on simulation’s prediction error.

  • Quijote: n-body galaxy simulation

  • Molino: galaxy catalogs from Quijote

  • Insight: many different metrics of the distribution of galaxies based on regional structure; valuable area for exploration to improve model accuracy

• Challenge: difficult to infer the fine-scale structure of the universe due to modeling and experimental challenges

• Approach: generative model of universe structure

  • Run simulation many times to generate many possible galaxy structures

  • Compare predicted statistical distribution to real cosmological survey data

  • Use KL Divergence to differentiate predicted probability distribution from the real distribution

  • Use this to infer a tighter distribution of values of Lambda-CDM parameters

  • Running simulations is too expensive, so training a neural surrogate based on normalizing flows model

  • SimBIG: Simulation-based inference of galaxies

    • https://changhoonhahn.github.io/simbig/current/

    • Generate training data of galaxy observations

    • Train normalizing flow

    • Compare model’s predictions to real data

    • Use error to update model’s posterior

  • Survey: SDSS-III (https://www.sdss3.org/)

  • Used SimBIG to tighten estimates of key parameters, especially when using higher-order galaxy structure statistics based on relative locations of multiple nearby galaxies

    • 1.9x tighter Sg parameter

    • 1.5x tighter H0 parameter

    • Equivalent to collecting 4x more observational data

• Future: additional data from galaxy surveys

  • Every decade we observe ~10x more galaxies

  • Dark Energy Spectroscopic Instrument (DESI): 4m Mayall telescope, 10b years of cosmic history

  • SuMIRe Prime Focus Spectrograph: 8.2m Subaru telescope, 12b years of cosmic history