3 citations · 3 across the 7 of their papers we have counts for
6 papers · 1 filter
(The) Wiggles going non-linear
Nathan Cohen, Jan Hamann
The simplest models of slow-roll inflation predict a featureless, nearly scale-invariant power spectrum of primordial curvature perturbations, consistent with current observations.…
TUNeS: Neural Emulation of Large-Scale Structure Across Redshifts
Yuqi Kang, Hu Bin, Dongxing Li +1
In this work, we introduce TUNeS (Temporal UNet emulator for Structure formation), a neural network framework for accelerating N-body simulations by predicting the nonlinear evolut…
Bayesian optimisation for Bayesian evidence (BOBE) -- a fast and efficient likelihood emulator for model selection
Nathan Cohen, Jan Hamann, Ameek Malhotra
The formalism of Bayesian model selection provides a very elegant way of ranking different physical models in terms of how compatible they are with a given set of observed data. Ho…
Non-Gaussianity in CMB lensing from full-sky simulations
Jan Hamann, Yuqi Kang
The lensing convergence field describing the weak lensing effect of the Cosmic Microwave Background (CMB) radiation is expected to be subject to mild deviations from Gaussianity. W…
ABMB: Deep Delensing Assisted Likelihood-Free Inference from CMB Polarization Maps
Kai Yi, Yanan Fan, Jan Hamann +2
The existence of a cosmic background of primordial gravitational waves (PGWB) is a robust prediction of inflationary cosmology, but it has so far evaded discovery. The most promisi…
A Minkowski Functional Analysis of the Cosmic Microwave Background Weak Lensing Convergence
Jan Hamann, Yuqi Kang
Minkowski functionals are summary statistics that capture the geometric and morphological properties of fields. They are sensitive to all higher order correlations of the fields an…