Differentiable Forward Modeling for Efficient and Accurate Shear Inference
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909014277750784 |
|---|---|
| author | Mendoza, Ismael Guinot, Axel Becker, Matthew R. Avestruz, Camille Campagne, Jean-Eric Porqueres, Natalia Schneider, Michael Tsaprazi, Eleni Collaboration, the LSST Dark Energy Science |
| author_facet | Mendoza, Ismael Guinot, Axel Becker, Matthew R. Avestruz, Camille Campagne, Jean-Eric Porqueres, Natalia Schneider, Michael Tsaprazi, Eleni Collaboration, the LSST Dark Energy Science |
| contents | Forthcoming Stage-IV dark energy optical surveys, such as LSST, have the ambitious goal of measuring cosmological parameters at sub-percent precision. Realizing their full scientific potential requires very precise measurement of the cosmic shear signal and control of corresponding systematics. In this work, we present a modern implementation of the Bayesian shear inference framework in Schneider et al. (2014), in the case that the PSF and sky background are known. This framework automatically propagates the pixel-noise measurement error from each galaxy into the final shear estimate, and thus requires no external calibration to handle noise bias. As a first application of this new implementation, we infer the cosmic shear posterior from simulated images consisting of isolated exponential galaxies with LSST-like levels of shape and pixel noise. In this simplified scenario, we estimate the absolute multiplicative bias $|m|$ of our approach to be below $0.9 \times 10^{-3} \, [3σ]$ when the intrinsic distribution of galaxy properties is known, and below $1.3 \times 10^{-3}\, [3σ]$ when these distributions are inferred alongside shear. Both results are within the LSST requirement of $|m| < 2 \times10^{-3}$. Additionally, we make progress towards the algorithm's computational feasibility in the context of modern wide-field surveys, where billions of galaxies must be processed, by leveraging differentiable forward models of galaxies, gradient-based samplers, and GPUs. Our final galaxy-fitting MCMC produces $300$ effective samples of galaxy properties in $0.45$ seconds per galaxy using a single A100 GPU. In the future, we seek to generalize our algorithm to handle selection, detection, and model shear biases so it can be applied to real survey data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22048 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Differentiable Forward Modeling for Efficient and Accurate Shear Inference Mendoza, Ismael Guinot, Axel Becker, Matthew R. Avestruz, Camille Campagne, Jean-Eric Porqueres, Natalia Schneider, Michael Tsaprazi, Eleni Collaboration, the LSST Dark Energy Science Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Forthcoming Stage-IV dark energy optical surveys, such as LSST, have the ambitious goal of measuring cosmological parameters at sub-percent precision. Realizing their full scientific potential requires very precise measurement of the cosmic shear signal and control of corresponding systematics. In this work, we present a modern implementation of the Bayesian shear inference framework in Schneider et al. (2014), in the case that the PSF and sky background are known. This framework automatically propagates the pixel-noise measurement error from each galaxy into the final shear estimate, and thus requires no external calibration to handle noise bias. As a first application of this new implementation, we infer the cosmic shear posterior from simulated images consisting of isolated exponential galaxies with LSST-like levels of shape and pixel noise. In this simplified scenario, we estimate the absolute multiplicative bias $|m|$ of our approach to be below $0.9 \times 10^{-3} \, [3σ]$ when the intrinsic distribution of galaxy properties is known, and below $1.3 \times 10^{-3}\, [3σ]$ when these distributions are inferred alongside shear. Both results are within the LSST requirement of $|m| < 2 \times10^{-3}$. Additionally, we make progress towards the algorithm's computational feasibility in the context of modern wide-field surveys, where billions of galaxies must be processed, by leveraging differentiable forward models of galaxies, gradient-based samplers, and GPUs. Our final galaxy-fitting MCMC produces $300$ effective samples of galaxy properties in $0.45$ seconds per galaxy using a single A100 GPU. In the future, we seek to generalize our algorithm to handle selection, detection, and model shear biases so it can be applied to real survey data. |
| title | Differentiable Forward Modeling for Efficient and Accurate Shear Inference |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2604.22048 |