13 papers
A Strong Cosmologically Coupled Mass Growth Solution in Linearized General Relativity
Kevin S. Croker, Joshua Cohon, Viktor T. Toth +13
DESI Data Release 2 has heightened interest in cosmologically coupled black holes (CCBHs) as an explanation for accelerating late-time expansion, but known models for CCBHs grow to…
Future of Artificial Intelligence for Science in Japan 2024 Community Report
Yoshitaka Itow, Jia Liu, Hirokazu Maesaka +16
This white paper summarizes scientific challenges and AI/ML research opportunities identified through the FAIRS Japan 2024 unconference process. The discussion focuses on three maj…
AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology II: Project Planning and Proposal Evaluation
Jia Liu, Veena Krishnaraj, Kateryna Vovk +18
We investigate how well large language models (LLMs) can assist scientific project planning and proposal evaluation. One-page project plans were independently generated for eight e…
Cluster Mass Inference from Galaxy Kinematics
Bonny Y. Wang, Leander Thiele, Matthew Ho
The masses of galaxy clusters carry cosmological and astrophysical information. We develop a simulation-based inference pipeline to infer cluster masses from full projected phase-s…
Machine Learning Techniques for Astrophysics and Cosmology: Simulation-Based Inference
Leander Thiele
Simulation-based inference (SBI) enables parameter inference by training neural networks on forward simulations. It is being applied both for intractable likelihoods as well as und…
LLMs with in-context learning for Algorithmic Theoretical Physics
Anamaria Hell, Leander Thiele
There is an increasing number of algorithmic computations in theoretical physics. These, while conceptually simple, can nevertheless be time-consuming and contain subtleties that s…
Bayesian Cosmic Void Finding with Graph Flows
Leander Thiele
Cosmic voids contain higher-order cosmological information and are of interest for astroparticle physics. Finding genuine matter underdensities in sparse galaxy surveys is, however…
Replicating weak-lensing summary-statistic covariances with normalizing flows
Joaquin Armijo, Leander Thiele, Jia Liu
We explore the ability of normalizing flow (NF) generative models to reproduce weak-lensing summary statistics when trained on a set of cosmological simulations. Our analysis focus…
Reconstructing the local density field with combined convolutional and point cloud architecture
Baptiste Barthe-Gold, Nhat-Minh Nguyen, Leander Thiele
We construct a neural network to perform regression on the local dark-matter density field given line-of-sight peculiar velocities of dark-matter halos, biased tracers of the dark…
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…
Set-based Implicit Likelihood Inference of Galaxy Cluster Mass
Bonny Y. Wang, Leander Thiele
We present a set-based machine learning framework that infers posterior distributions of galaxy cluster masses from projected galaxy dynamics. Our model combines Deep Sets and cond…
Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI
Leander Thiele, Adrian E. Bayer, Naoya Takeishi
The simulation cost for cosmological simulation-based inference can be decreased by combining simulation sets of varying fidelity. We propose an approach to such multi-fidelity inf…
De-baryonifying halos via optimal transport
Leander Thiele
Baryonic feedback uncertainty is a limiting systematic for next-generation weak gravitational lensing analyses. At the same time, high-resolution weak lensing maps are best analyze…