Cosmology

Gravitational forces from the largest structures in the Universe leave a detectable imprint on galaxies and their local environment. I will present a new approach to tracing the tidal field using these correlations: the intrinsic alignment of small groups of galaxies, or "multipelts". Multiplets mostly consist of 2-4 galaxies within 1 Mpc/h of each other, and we measure their orientations relative to the galaxy-traced tidal field. Using spectroscopic redshfits from the DESI Y1 survey, we detect intrinsic alignment out to projected separations of 100 Mpc/h and beyond redshift 1. We find a simillar signal regardless of galaxy luminosity or color, which could make multiplet alignment a useful tool for mapping the direction of the tidal field and any cosmological effects which impact it. Our detection demonstrates that galaxy clustering in the non-linear regime of structure formation preserves an interpretable memory of the large-scale tidal field. 

In this talk I will discuss the potential of future high-resolution CMB observations to probe structure on sub-galactic scales. In particular, I will discuss how a CMB-HD experiment can measure lensing over the range 0.005 h/Mpc < k < 55 h/Mpc, spanning four orders of magnitude, with a total lensing signal-to-noise ratio from the temperature, polarization, and lensing power spectra greater than 1900. These lensing measurements would allow CMB-HD to distinguish between cold dark matter (CDM) and non-CDM models that can resolve apparent small-scale tensions with CDM. In addition, CMB-HD can distinguish between baryonic feedback effects and non-CDM models due to the different way each impacts the lensing signal. The kinetic Sunyaev-Zel’dovich power spectrum measured by CMB-HD further constrains non-CDM models that deviate from CDM. In sum, future CMB experiments will not only measure traditional cosmological parameters with unprecedented precision, but will also simultaneously constrain baryonic physics and dark matter properties that impact sub-galactic scales.

The Effective Field Theory of Large Scale Structure (EFTofLSS) has found tremendous success as a perturbative framework for the evolution of large scale structure, and it is now routinely used to compare theoretical predictions against cosmological observations. The model for the total matter field includes one nuisance parameter at 1-loop order, the effective sound speed, which can be extracted by matching the EFT to full N-body simulations. In this talk we explore two different directions related to the effective sound speed. We first show that its emergence can be understood even without effective field theory ingredients, through a perturbative framework that solves the Vlasov-Poisson system of equations directly in phase space. However, we will argue that the EFT is necessary to ensure self-consistency. We then discuss how one can estimate the effective sound speed, via separate universe techniques, with analytic calculations. The estimate is in good agreement with simulation results, and we show it can be used to extract the cosmology dependence of the effective sound speed and to shed light on what cosmic structures shape its value.

Improving cosmological constraints from galaxy clustering presents several challenges, particularly in extracting information beyond the power spectrum due to the complexities involved in higher-order n-point function analysis. In this talk, I will introduce novel inference techniques that allow us to go beyond the state-of-the-art, not only by utilizing the galaxy trispectrum, a task that remains computationally infeasible with traditional methods, but also by accessing the full information encoded in the galaxy density field for the first time in cosmological analysis. I will present simulation-based inference (SBI), a powerful deep learning technique that enables cosmological inference directly from summary statistics in simulations, bypassing the need for explicit analytical likelihoods or covariance matrices. This is achieved using LEFTfield, a Lagrangian forward model based on the Effective Field Theory of Large Scale Structure (EFTofLSS) and the bias expansion, ensuring robustness on large scales. Furthermore, LEFTfield enables field-level Bayesian inference (FBI), where a field-level likelihood is used to directly analyze the full galaxy density field rather than relying on compressed statistics. I will conclude by exploring the question of how much cosmological information can be extracted at the field level through a comparison of σ8 constraints obtained from FBI, which directly uses the 3D galaxy density field, and those obtained from n-point functions via SBI.