193 citations · 655 across the 42 of their papers we have counts for
9 papers · 1 filter
Single Frequency CMB Foreground Removal with Inter-scale Machine Learning
Helen Shao, Fiona McCarthy, Blake D. Sherwin +2
Accurate measurements of Cosmic Microwave Background (CMB) B-mode polarization, a key probe of inflationary physics, are hindered by complex Galactic dust foregrounds. Traditional…
A hierarchical Bayesian framework for cosmology using Type 1 AGN variability
Júlia Laguna-Miralles, Vasily Belokurov, Miles Cranmer
Independent luminosity-distance probes beyond the Type Ia supernova range are needed to test cosmic expansion at high redshift. Type 1 AGN are abundant at \(z>2\), but their use fo…
Five parameters are all you need (in CDM)
Paulo Montero-Camacho, Yin Li, Miles Cranmer
The standard cosmological model, with its six independent parameters, successfully describes our observable Universe. One of these parameters, the optical depth to reionization $τ_…
Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
Ji Won Park, Simon Birrer, Madison Ueland +6
We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence () from photometric measurements of galaxies along a given line of sight. The me…
The SZ flux-mass (-) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
Digvijay Wadekar, Leander Thiele, J. Colin Hill +8
Feedback from active galactic nuclei (AGN) and supernovae can affect measurements of integrated SZ flux of halos () from CMB surveys, and cause its relation with the…
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Pablo Lemos, Miles Cranmer, Muntazir Abidi +5
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys. Despite continual improvements…