10 papers
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
The paper evaluates the ability of current large language models to generate one‑page research project plans in physics, astrophysics, and cosmology, and compares how human reviewe…
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…