12 papers · 1 filter
jFoF: GPU Cluster Finding with Gradient Propagation
Benjamin Horowitz, Adrian E. Bayer
We present jFoF, a fully GPU-native Friends-of-Friends (FoF) halo finder designed for both high-performance simulation analysis and differentiable modeling. Implemented in JAX, jFo…
Impact of Simulation Box Size for Weak Lensing: Replication and Super-Sample Effects
Akira Tokiwa, Adrian E. Bayer, Joaquin Armijo +6
We quantify the bias caused by small simulation box size on weak lensing observables and covariances, considering both replication and super-sample effects for a range of higher-or…
CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning
Ningyuan Huang, Richard Stiskalek, Jun-Young Lee +6
Cosmological simulations provide a wealth of data in the form of point clouds and directed trees. A crucial goal is to extract insights from this data that shed light on the nature…
Flinch: A Differentiable Framework for Field-Level Inference of Cosmological parameters from curved sky data
Andrea Crespi, Marco Bonici, Arthur Loureiro +6
We present Flinch, a fully differentiable and high-performance framework for field-level inference on angular maps, developed to improve the flexibility and scalability of current…
Transfer Learning Beyond the Standard Model
Veena Krishnaraj, Adrian E. Bayer, Christian Kragh Jespersen +1
Machine learning enables powerful cosmological inference but typically requires many high-fidelity simulations covering many cosmological models. Transfer learning offers a way to…
The Power of the Cosmic Web
James Sunseri, Adrian E. Bayer, Jia Liu
We study the cosmological information contained in the cosmic web, categorized as four structure types: nodes, filaments, walls, and voids, using the Quijote simulations and a modi…