In the first moments after the Big Bang, the universe expanded faster than light could cross it. This expansion left a subtle ripple in the distribution of matter, like the rings on water from a thrown stone. Traces of that ripple are visible today in how galaxies cluster into groups and filaments.
Astronomers have learned to read these traces in a new way. Instead of looking only at large-scale structures, they reconstruct the entire picture — from the largest structures down to fine details. It's like reconstructing the original throw of the stone from all the rings on the water, including the faintest ones. The method uses computer simulations where virtual galaxies are born from the same initial conditions as real ones.
The accuracy has improved by a factor of 1.5 compared to previous approaches. This means that future sky surveys, such as Euclid, will be able to more reliably separate signal from random noise. And thus — get closer to answering what made the universe inflate so rapidly in the first fractions of a second.
as written by the authors arXiv:2603.20855
Local primordial non-Gaussianity, parameterised as $$f_{\rm NL}^{\rm local}$$, will be stringently constrained using state-of-the-art methods applied to next-generation galaxy redshift survey data. In this paper, in preparation for the upcoming data sets, we demonstrate for the first time the joint field-level inference of $$f_{\rm NL}^{\rm local}$$, nuisance parameters, and the initial conditions in realistic halo catalogues, ones which are generated through full dark-matter-only $$N$$-body simulations. The field-level inference algorithm optimally constrains $$f_{\rm NL}^{\rm local}$$ through a Bayesian forward-modelling approach at the field level, which outperforms traditional methods by leveraging the full statistical power of the data at the scales considered. In addition, we assess its performance under various design choices in the forward model, including tests of the structure formation model and resolution. We demonstrate the robustness of our approach by applying it to a subset of the \textit{Quijote} simulation suite, performing the inference at scales down to $$k_{\rm max} \approx 0.1 h \rm{Mpc}^{-1}$$. Compared with a power spectrum and bispectrum estimator, we find a $$\sim1.3$$ improvement in $$\sigma(f_{\rm NL}^{\rm local})$$ when applying \borg{}, while marginalising over the initial conditions and bias parameters. From the small-scale information sensitivity tests, we show that the constraints on $$f_{\rm NL}^{\rm local}$$ improve as we increase the resolution of the inference. These findings underscore the transformative potential of field-level inference to leverage the information available in ongoing surveys such as \textit{Euclid}, providing accurate insights into the physics of cosmic inflation and the number of fields driving it.
The method compares millions of virtual universes with the real one to find the initial conditions that best explain the observed picture.