Search Results for author:
Found 5 papers, 3 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.
Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs
Set-based learning is an essential component of modern deep learning and network science.
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that networks can still extract information when catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear IMNN summaries can be used as asymptotically optimal compressed statistics for Bayesian simulation-based inference.
Exoplanet atmosphere evolution: emulation with neural networks
We demonstrate there is a relationship between core masses and the atmospheric mass that they retain after disc dispersal, and this trend is consistent with the `boil-off' scenario, in which close-in planets undergo dramatic atmospheric escape during disc dispersal.
deep21: a Deep Learning Method for 21cm Foreground Removal
We seek to remove foreground contaminants from 21cm intensity mapping observations.
