activity
20182026
most citedCMB-S4 Science Case, Reference Design, and Project Plan

443 citations · 744 across the 19 of their papers we have counts for

collaborators
Showing astro-ph.COShow all

28 papers · 1 filter

astro-ph.CO2026

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

Samuel Goldstein, Kendrick M. Smith, Utkarsh Giri +1

We present a method to estimate non-Gaussian power spectrum covariance matrices by directly measuring the response of the small-scale power spectrum to long-wavelength perturbation…

astro-ph.CO2026

MadEvolve: Evolutionary Optimization of Cosmological Algorithms with Large Language Models

Tianyi Li, Shihui Zang, Moritz Münchmeyer

We develop a general framework to discover scientific algorithms and apply it to three problems in computational cosmology. Our code, MadEvolve, is similar to Google's AlphaEvolve,…

astro-ph.CO2025

The Simons Observatory: forecasted constraints on primordial gravitational waves with the expanded array of Small Aperture Telescopes

The Simons Observatory Collaboration, I. Abril-Cabezas, S. Adachi +479

We present updated forecasts for the scientific performance of the degree-scale (0.5 deg FWHM at 93 GHz), deep-field survey to be conducted by the Simons Observatory (SO). By 2027,…

astro-ph.CO2025

Squeezed Limit non-Gaussianity Estimation with Cosmic Shear

Shi-Hui Zang, Moritz Münchmeyer

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 $…

astro-ph.CO2025

QML-FAST -- A Fast Code for low- Tomographic Maximum Likelihood Power Spectrum Estimation

Yurii Kvasiuk, Anderson Lai, Moritz Münchmeyer +1

We present a novel implementation for the quadratic maximum likelihood (QML) power spectrum estimator for multiple correlated scalar fields on the sphere. Our estimator supports ar…

astro-ph.CO2025

Reconstruction of Dark Matter and Baryon Density From Galaxies: A Comparison of Linear, Halo Model and Machine Learning-Based Methods

Jordan Krywonos, Yurii Kvasiuk, Matthew C. Johnson +1

For many analyses in cosmology it is necessary to reconstruct the likely distribution of unobserved fields, such as dark matter or non-luminous baryons, from observed luminous trac…