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-In a work published on JCAP and featured on MPA research highlight, we built a novel forward-modeling pipeline for field-level Bayesian inference (FBI), based on the EFTofLSS and Bayesian frameworks. FBI aims to infer the initial conditions of the Universe, i.e. the primordial density field, from the observed LSS field, i.e. the galaxy density field, while simultaneously inferring the parameters of the cosmological models. FBI guarantees optimal inference without information loss.
+In a work published on JCAP and featured on MPA research highlight, we built a novel forward-modeling pipeline for field-level inference (FLI), based on the EFTofLSS and Bayesian frameworks. FLI aims to infer the initial conditions of the Universe, i.e. the primordial density field, from the observed LSS field, i.e. the galaxy density field, while simultaneously inferring the parameters of the cosmological models. FLI guarantees optimal inference without information loss.
-In a recent follow-up to appear on Physical Review Letters, we further demonstrated that FBI can robustly recover not only the correct initial conditions of the test simulations, but also *unbiased* cosmological parameters---with a factor of x5 improvement over standard analysis involving low-order n-point summary statistics, namely the power spectrum (2-point correlation function) and bispectrum (3-point function).
+In a follow-up published on Physical Review Letters, we further demonstrated that FLI can robustly recover not only the correct initial conditions of the test simulations, but also *unbiased* cosmological parameters---with a factor of x5 improvement over standard analysis involving low-order n-point summary statistics, namely the power spectrum (2-point correlation function) and bispectrum (3-point function).
Preprint of the PRL article can be found on arXiv.
Here is my interview with Shaun Hotchkiss on Cosmology Talks about this result, featuring Beatriz Tucci (PhD candidate, MPA and IMPRS), the stellar half of this work and interview.