2 citations · 2 across the 5 of their papers we have counts for
18 papers
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…
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22
Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unst…
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 $Ï…
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…
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20
Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Ray…
Overtone: Cyclic Patch Modulation for Clean, Efficient, and Flexible Physics Emulators
Payel Mukhopadhyay, Michael McCabe, Ruben Ohana +1
Transformer-based PDE surrogates achieve remarkable performance but face two key challenges: fixed patch sizes cause systematic error accumulation at harmonic frequencies, and comp…