Blast.jl: Differentiable Non-Limber Power Spectra for Joint Clustering, Shear, and CMB lensing Analyses
Abstract
Upcoming large-scale structure surveys require accurate theoretical predictions for angular power spectra on the largest angular scales, where the commonly used Limber approximation breaks down and multiple relativistic and observational effects become relevant. At the same time, modern cosmological analyses increasingly rely on gradient-based inference techniques, motivating the development of fast, fully differentiable algorithms. In this work, we present a major extension of Blast.jl, a numerical toolkit for the efficient computation of non-Limber angular power spectra. Building on the Chebyshev-polynomial decomposition of the original framework, the updated algorithm incorporates redshift-space distortions, magnification bias, primordial non-Gaussianity, intrinsic alignments, CMB lensing, and the integrated Sachs-Wolfe effect, while remaining fully differentiable through custom automatic differentiation rules. Despite the increased physical complexity, the algorithm retains favorable scaling by precomputing all cosmology-independent quantities. We validate the expanded framework against brute-force integration and established cosmological codes, and provide a detailed analysis of the trade-off between computational speed and precision as a function of the algorithm hyperparameters. We demonstrate the readiness of Blast.jl for gradient-based inference through a simulated likelihood analysis with LSST Y10-like mock data, recovering unbiased cosmological and nuisance parameter constraints from a 35-parameter model using gradient-based samplers.
Related
- Dark Energy Survey year 6 results: Cosmological constraints from galaxy clustering and weak lensing
- Cosmological constraining power of the redshifts, heights, and angular clustering of weak gravitational lensing peaks
- Cosmological constraining power of the redshifts, heights, and angular clustering of weak gravitational lensing peaks
- Cosmological Constraints from Full-scale Clustering and Galaxy–Galaxy Lensing with DESI DR1
- Cosmology with weak lensing surveys
BibTeX
@misc{chiarenza2026blastjl,
title = {{Blast.jl: Differentiable Non-Limber Power Spectra for Joint Clustering, Shear, and CMB lensing Analyses}},
author = {Sofia Chiarenza and Marco Bonici and Stefano Camera and Giulio Fabbian and Carlos García-García and Alex Krolewski and Will J Percival},
howpublished = {HAL},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.01855},
eprint = {2609.01855},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05758433v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Blast.jl: Differentiable Non-Limber Power Spectra for Joint Clustering, Shear, and CMB lensing Analyses
AU - Sofia Chiarenza
AU - Marco Bonici
AU - Stefano Camera
AU - Giulio Fabbian
AU - Carlos García-García
AU - Alex Krolewski
AU - Will J Percival
PY - 2026
DA - 2026/09/22
PB - HAL
DO - 10.48550/arxiv.2609.01855
UR - https://hal.science/hal-05758433v1
AB - Upcoming large-scale structure surveys require accurate theoretical predictions for angular power spectra on the largest angular scales, where the commonly used Limber approximation breaks down and multiple relativistic and observational effects become relevant. At the same time, modern cosmological analyses increasingly rely on gradient-based inference techniques, motivating the development of fast, fully differentiable algorithms. In this work, we present a major extension of Blast.jl, a numerical toolkit for the efficient computation of non-Limber angular power spectra. Building on the Chebyshev-polynomial decomposition of the original framework, the updated algorithm incorporates redshift-space distortions, magnification bias, primordial non-Gaussianity, intrinsic alignments, CMB lensing, and the integrated Sachs-Wolfe effect, while remaining fully differentiable through custom automatic differentiation rules. Despite the increased physical complexity, the algorithm retains favorable scaling by precomputing all cosmology-independent quantities. We validate the expanded framework against brute-force integration and established cosmological codes, and provide a detailed analysis of the trade-off between computational speed and precision as a function of the algorithm hyperparameters. We demonstrate the readiness of Blast.jl for gradient-based inference through a simulated likelihood analysis with LSST Y10-like mock data, recovering unbiased cosmological and nuisance parameter constraints from a 35-parameter model using gradient-based samplers.
ER -