Workshop: 2nd ICML Workshop on Machine Learning for Astrophysics

SimBIG: Galaxy Clustering beyond the Power Spectrum

ChangHoon Hahn · Pablo Lemos · Bruno Régaldo-Saint Blancard · Liam Parker · Michael Eickenberg · Shirley Ho · Jiamin Hou · Elena Massara · Chirag Modi · Azadeh Moradinezhad Dizgah · David Spergel


The study of the Universe revolves around understanding the fundamental parameters that describe the model of our Universe. These fundamental parameters are usually constrained by analyzing what we can observe from the sky such as galaxy distributions, the cosmic microwave background, etc. The paper uses the SIMBIG framework, which leverages machine learning techniques and simulation-based inference to improve the constraints on these fundamental parameters by an- analyzing galaxy clustering. When we apply SimBIG to a fraction of the BOSS galaxy survey, we achieve significantly (1.2 and 2.7×) tighter constraints on cosmological parameters such as Ωm and σ8 compared to standard power spectrum analyses. Using only 10% of the BOSS volume, we obtain constraints on H0 and S8 that are competitive with those from other probes. Future work will extend SimBIG to upcoming galaxy surveys for even stronger constraints.

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