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Found 4 papers, 2 papers with code
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
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.
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
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.
Robust Field-level Likelihood-free Inference with Galaxies
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.
Sengi: a small, fast, interactive viewer for spectral outputs from stellar population synthesis models
We present Sengi, https://christopherlovell. github. io/sengi , an online tool for viewing the spectral outputs of stellar population synthesis (SPS) codes.
Instrumentation and Methods for Astrophysics Astrophysics of Galaxies
