arXiv AI By Alex A. Saoulis, Kiyam Lin, Niall Jeffrey, Maximilian von Wietersheim-Kramsta, Davide Piras, Alessio Spurio Mancini, Ana M. G. Ferreira, Benjamin Joachimi

Field-level weak lensing cosmology with $60$ simulations using multifidelity simulation-based inference

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arXiv AI
Oct 28

ASTROLABE: A 3D Particle-Level Surrogate for Cosmology Simulation with Physical Flow Matching

ASTROLABE is a flow‑matching generative emulator that operates directly on the 6N phase‑space coordinates of individual particles, avoiding voxel grids and allowing any observable to be measured from inference. It uses a field‑particle hybrid transformer so that compute and memory scale linearly with the number of particles, and incorporates physical flow matching by basing the prior on COLA‑derived coordinates with momentum and mass conservation enforced throughout the flow. In Lambda‑CDM tests, ASTROLABE reproduces realistic fields, accurate power spectra, halo mass functions, velocity profiles, and halo density profiles, and it generalizes to out‑of‑distribution dark‑acoustic oscillation initial conditions while also being fine‑tuned to emulate large‑scale simulations such as TNG50‑3‑dark at a fraction of the cost.

By Ibrahim Elsharkawy, David Curtin, Yonatan Kahn, Neal Weiner
arXiv Computer Vision
Sep 7

Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models

The paper presents a fully automated method for reconstructing the surface mass density of galaxy clusters using photometry and gravitational lensing data. It introduces DarkClusters-15k, a benchmark dataset of 15,000 simulated clusters with paired mass and photometry maps across multiple redshifts and simulation frameworks. By training a diffusion prior on this dataset, the authors generate posterior samples constrained by weak- and strong-lensing observables, achieving accurate, physics‑guided reconstructions with well‑calibrated uncertainties in minutes.

By Diego Royo, Brandon Zhao, Adolfo Mu\~noz, Diego Gutierrez, Katherine L. Bouman