Discussion Lead: Andrew Jaffe (Imperial, Flatiron CCA)
Topic: Sellentin, E., Loureiro, A., Whiteway, L., Lafaurie, J. S., Balan, S. T., Olamaie, M., Jaffe, A. H., & Heavens, A. F. (2023), “Almanac: MCMC-based signal extraction of power spectra and maps on the sphere”, The Open Journal of Astrophysics, 6, 31, doi:10.21105/astro.2305.16134
Loureiro, A., Whiteway, L., Sellentin, E., Silva Lafaurie, J., Jaffe, A. H., & Heavens, A. F. (2023), “Almanac: Weak Lensing power spectra and map inference on the masked sphere”, The Open Journal of Astrophysics, 6, 6, doi:10.21105/astro.2210.13260
Links: https://arxiv.org/abs/2305.16134, https://arxiv.org/abs/2210.13260
Abstracts: We present a field-based signal extraction of weak lensing from noisy observations on the curved and masked sky. We test the analysis on a simulated Euclid-like survey, using a Euclid-like mask and noise level. To make optimal use of the information available in such a galaxy survey, we present a Bayesian method for inferring the angular power spectra of the weak lensing fields, together with an inference of the noise-cleaned tomographic weak lensing shear and convergence (projected mass) maps. … We use Hamiltonian Monte Carlo sampling, inferring simultaneously the power spectra and denoised maps with a total of ∼16.8 million free parameters.
Inference in cosmology often starts with noisy observations of random fields on the celestial sphere, such as maps of the microwave background radiation, continuous maps of cosmic structure in different wavelengths, or maps of point tracers of the cosmological fields. Almanac uses Hamiltonian Monte Carlo sampling to infer the underlying all-sky noiseless maps of cosmic structures, in multiple redshift bins, together with their auto- and cross-power spectra. It can sample many millions of parameters, handling the highly variable signal-to-noise of typical cosmological signals, and it provides science-ready posterior data products.