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
arXiv:2606. 10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data.
By Ludvig Doeser, Jens Jasche
arXiv:2603. 22006v2 Announce Type: replace-cross Abstract: Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy.
By Hubert Leterme, Andreas Tersenov, Jalal Fadili, Jean-Luc Starck
arXiv:2606.23346v2 Announce Type: replace-cross
Abstract: We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using just 60 $N$-body si...
By Alex A. Saoulis, Kiyam Lin, Niall Jeffrey, Maximilian von Wietersheim-Kramsta, Davide Piras, Alessio Spurio Mancini, Ana M. G. Ferreira, Benjamin Joachimi
arXiv:2607. 27320v1 Announce Type: cross Abstract: Field-level inference of cosmological initial conditions from galaxy surveys requires a forward model that is simultaneously accurate in the non-linear regime, computationally efficient, and fully differentiable.
By Cooper Jacobus, Beatriz Tucci, Oliver Philcox
arXiv:2512. 18367v2 Announce Type: replace-cross Abstract: Diffusion models are highly expressive image priors for Bayesian inverse problems.
By Wenhan Guo, Jinglun Yu, Yaning Wang, Jin U. Kang, Yu Sun
PosteriorBench is a new benchmark that evaluates how well generative inverse solvers recover full posterior distributions rather than just a single reconstruction. It tests four physics-based inverse problems—Darcy flow inversion, Poisson source recovery, carbon capture and storage, and light transport material inference—using high-fidelity reference posteriors generated by rejection sampling and MCMC. The benchmark employs five metrics (posterior-mean error, posterior-standard-deviation error, maximum mean discrepancy, sliced Wasserstein distance, and radially averaged power-spectrum error) to assess pointwise accuracy, uncertainty, distributional alignment, and global frequency fidelity, revealing significant distribution-matching gaps in current solvers and highlighting the importance of neural operators, guidance weights, and generation noise for posterior-variance calibration.
By Jiachen Yao, Zi-Siang Hsu, Xi Deng, Aditi Gupta, Xin Ju, Sally M Benson, Gege Wen, Anima Anandkumar
arXiv:2606. 11295v1 Announce Type: cross Abstract: Recovering cosmological information beyond the power spectrum is a central goal for upcoming cosmological surveys, since late-time non-Gaussian signal in the matter density cannot be accessed through two-point statistics alone.
By Federico Semenzato, Benjamin D. Wandelt, Michele Liguori, Alvise Raccanelli
arXiv:2605.29016v2 Announce Type: replace-cross
Abstract: We investigate conditional diffusion modeling for three-dimensional 21 cm lightcone emulation, focusing on cubes with a sky-plane size of $64...
By Bin Xia, John H. Wise
arXiv:2608.21729v1 Announce Type: new
Abstract: Simulation-Based Inference (SBI) serves as a vital framework for parameter inference in scientific fields where simulators involve intractable likeliho...
By Yichen Zang, Song Liu, Jiun-Yi Lin
arXiv:2604. 08648v2 Announce Type: replace-cross Abstract: We motivate the use of differentiable probabilistic programming techniques in order to account for the large model-space inherent to astrophysical $\gamma$-ray analyses.
By Siddharth Mishra-Sharma, Tracy R. Slatyer, Yitian Sun, Yuqing Wu
arXiv:2609.10456v1 Announce Type: new
Abstract: Nanoscale imaging of mammalian brains is critical for connectomics. X-ray laminography enables high-throughput imaging of extended, plate-like biologic...
By Wenxuan Fang, Abraham L. Levitan, Ana Diaz, Carles Bosch, Adrian Wanner, Andreas T. Schaefer, Mirko Holler, Tomas Aidukas, Nicholas W. Phillips, Yuxin Zhang, Alexandra Pacureanu, Manuel Guizar-Sicairos, Luis Barba