Event

Massive neutrinos suppress the growth of cosmic structure on small, non-linear scales, making it crucial to go beyond a traditional power spectrum analysis to tighten constraints on their mass. By jointly analyzing data from multiple surveys and optimally extracting information through field-level inference, we can gain dramatically more insight into neutrinos and the Universe as a whole.

In this talk, I will first explore how different components of the cosmic web can be leveraged to break parameter degeneracies and extract significantly more information about neutrino mass from the non-linear matter field. I will then assess the extent to which upcoming galaxy clustering, weak lensing, and CMB surveys can capture this non-linear information in practice. 

 

In turn, I will introduce the HalfDome cosmological simulations—a suite of full-sky simulations designed for joint analyses of Stage IV surveys—and discuss how they can be used to mitigate systematics, tighten parameter constraints, and serve as a playground for machine learning applications. 

 

Additionally, I will motivate field-level inference as an optimal approach for extracting information from cosmic structure and reconstructing the initial conditions of the Universe—in particular, I will motivate different methods of field-level inference, ranging from differentiable forward modeling to neural networks, and highlight the potential for improving BAO constraints with DESI.