pith. machine review for the scientific record. sign in

arxiv: 2512.07295 · v2 · submitted 2025-12-08 · 🌌 astro-ph.CO

Recognition: unknown

Squeezed Limit non-Gaussianity Estimation with Cosmic Shear

Authors on Pith no claims yet
classification 🌌 astro-ph.CO
keywords mathrmlensingmethodnon-gaussianityapproachestimationestimatorfields
0
0 comments X
read the original abstract

We present a new method to constrain local primordial non-Gaussianity using the large-scale modulation of the local lensing power spectrum. Our work extends our recently proposed $\pi$-field method for primordial non-Gaussianity estimation to spherical coordinates and applies it to galaxy lensing. Our approach is computationally efficient and only requires binned multipole power spectra $C_\ell(z_1,z_2)$ on large scales, as well as their covariance. Our method is simpler to implement than a full bispectrum estimator, but still contains the full squeezed-limit information. We validate our model using a suite of N-body simulations and demonstrate its accuracy in recovering the $f_{\mathrm{NL}}$ values. We then perform a Fisher forecast for an LSST-like weak lensing survey, finding $\sigma_{f_{\mathrm{NL}}} \simeq 44$. Our approach readily combines with other $f_{\mathrm{NL}}$-sensitive fields such as kSZ velocity reconstruction and clustering-based $\pi$-fields, for a future combined $f_{\mathrm{NL}}$ estimator using various large-scale galaxy and CMB observables.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. If at First You Don't Succeed, Trispectrum: I. Estimating the Matter Power Spectrum Covariance with Higher-Order Statistics

    astro-ph.CO 2026-04 unverdicted novelty 6.0

    Estimators from squeezed bispectrum and collapsed trispectrum recover unbiased small-scale matter power spectrum covariance at the percent level using 25 Quijote simulations.