Differentiable Forward Modeling for Efficient and Accurate Shear Inference
Ismael Mendoza, Axel Guinot, Matthew R. Becker, Camille Avestruz, Jean-Eric Campagne, Natalia Porqueres, Michael Schneider, Eleni Tsaprazi, the LSST Dark Energy Science Collaboration
astro-ph.IM, astro-ph.CO
Submitted: 2026-08-21
Updated: 2026-08-25
Code: https://github.com/GalSim-developers/JAX-GalSim
Terminology
Sources
- Chromatic Effects on the PSF and Shear Measurement for the Roman Space Telescope High-Latitude Wide Area Survey
- A Conceptual Introduction to Hamiltonian Monte Carlo
- MADNESS Deblender: Maximum A posteriori with Deep NEural networks for Source Separation
- Markov Chain Monte Carlo for Bayesian Parametric Galaxy Modeling in LSST
- BlackJAX: Composable Bayesian inference in JAX
- Self-calibration of weak lensing cosmic shear biases
- Hyper Suprime-Cam Year 3 Results: Cosmology from Cosmic Shear Power Spectra
- Euclid Definition Study Report
- Bayesian Workflow
- Metacalibration: Direct Self-Calibration of Biases in Shear Measurement
- Shear measurement bias due to spatially varying spectral energy distributions in galaxies
- Hyper Suprime-Cam Year 3 Results: Cosmology from Cosmic Shear Two-point Correlation Functions
- Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
- Weak Gravitational Lensing
- Towards solving model bias in cosmic shear forward modeling
- Steps towards nonlinear cluster inversion through gravitational distortions: III. Including a redshift distribution of the sources
- Benchmarking field-level cosmological inference from galaxy redshift surveys
- Running Markov Chain Monte Carlo on Modern Hardware and Software
- Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
- Pixellated Posterior Sampling of Point Spread Functions in Astronomical Images
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