Seminar by Lina Castiblanco: “Forecasting cosmological constraints beyond two-point statistics with One-Point Statistics “
Title: Forecasting cosmological constraints beyond two-point statistics with One-Point Statistics
Speaker: Lina Castiblanco
Bio: I am a cosmologist and postdoctoral researcher at Bielefeld University. My research focuses on extracting information about the large-scale structure of the Universe beyond traditional two-point statistics, particularly through one-point probability distribution functions of weak lensing and galaxy clustering. During my PhD at Pontificia Universidad Católica de Valparaíso, I worked on modelling the galaxy bispectrum and topological statistics to constrain primordial non-Gaussianity.
Venue&Time: Red Room | 12:00
Abstract: Current and next-generation galaxy surveys will provide vast datasets to improve our understanding of the Universe, once we extract their maximum information content. However, the late-time matter distribution is non-Gaussian, limiting the information captured by two-point statistics. To fully exploit the cosmological signal, it is essential to incorporate beyond-two-point statistics, such as one-point probability distribution functions (PDFs), which capture key non-Gaussian information from both galaxy clustering and weak lensing. In particular, the weak lensing convergence PDF is sensitive to the physics of the dark Universe, including dark matter and dark energy. This information is extracted more efficiently by performing a tomographic analysis. Additionally, a combined analysis of galaxy clustering and weak lensing in a joint PDF offers a powerful way to jointly constrain cosmology and galaxy bias. We also need to consider systematic effects, such as shape noise, intrinsic alignments, photometric redshift errors, and mass-mapping, which can lead to biased cosmological parameter estimates if they are neglected or not properly modelled. In the talk, I will present our theoretical modelling of the tomographic convergence PDF and the joint weak lensing–galaxy clustering PDF, including these systematic effects, and show our analyses combining the PDF with standard 2-point correlation functions, which show that the PDF is a powerful statistic for extracting non-Gaussian information.