Cosmology and Gravitation

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.

Deviations from a parity-symmetric Universe would strongly signal the presence of new physics beyond the Standard Model. The lowest-order scalar observable sensitive to parity in a homogeneous and isotropic Universe is the connected four-point function of a given cosmological field. Geometrically, this function is described by a suitable average of this field over many tetrahedral configurations. As the CMB represents a spherical projection of three-dimensional curvature fluctuations, its four-point function serves as a unique, but potentially limited, probe of this fundamental symmetry. In this talk, I will discuss progress in rigorously understanding the CMB’s sensitivity to parity-violating physics from both early- and late-time sources.

We are entering the golden age of multi-wavelength astronomical surveys. In the 2020s, a plethora of surveys (such as Euclid, eROSITA, Rubin-LSST, Simons Observatory, and CMB-S4) are underway or planned to provide unprecedented insights into cosmology and astrophysics. In this talk, I will discuss the significant scientific opportunities and challenges that arise in the era of big data, highlighting recent advances in computational modeling and the roles of artificial intelligence and machine learning.

Galaxies are the medium through which we study the structure of the universe. However, widely applied statistical models of galaxies are generally over-simplified: even recently proposed models cannot capture the dependencies on environment or formation history. To solve this problem, I will introduce Graph Neural Networks (GNNs), a general and ideal tool for physical modelling. Geometrically constrained GNNs vastly improve our models, and allow us to ask detailed questions about the importance of formation history and environment for cosmological galaxy modeling. I will also prove a surprising equivalence between these two aspects of galaxy formation.

The generation of large scales white noise is a generic property of the dynamics of physical systems described by local non-linear partial differential equations. Non-linearities prevent the small scale dynamics to be erased by smoothing. Unresolved small scale dynamics act as an uncorrelated (white or Poissonian) noise (seemingly stochastic but actually deterministic) contribution to large scale dynamics. Such is the case for cosmic inhomogeneities. In the standard model of cosmology the primordial density power spectrum is taken to be sub-Poissonian and subsequent non-linear evolutions will inevitably produce white noise which will dominate on the largest scales. Non-observation of white noise on the Hubble scale precludes a power law extrapolation of the power spectrum below one comoving parsec and places severe constraints on a wide variety of phenomena in the early universe, including phase transitions, vorticity and gravitational radiation.

On April 4th, 2024, the Dark Energy Spectroscopic Instrument (DESI) released its first set of cosmological results based on measurements of the baryon acoustic oscillations (BAO) scale in the spatial distribution of galaxies and quasars, and in the Lyman-alpha forest. Those measurements constrain the expansion history of the Universe in the redshift range 0.1 < z < 4.16, with implications for studies of dark energy, neutrino cosmology and the Hubble constant. To make the most of the cosmological information content of the galaxy & quasar distributions, we now analyze the full shape of their power spectra, constraining the evolution of the large scale structure of the Universe over the range 0.1 < z < 2.1. In this talk, following a brief overview of the DESI instrument and observations, we will present the latest public results, discuss some of their main cosmological implications, highlight efforts towards the full shape results and expectations for the next data release.

Claims of a low clustering amplitude (S_8) at low redshifts from weak galaxy lensing measurements trace back nearly a decade, however, recent work suggests these results may be driven by large baryonic feedback or mischaracterization of linear alignments. I will present a complimentary approach to measure the evolution of S_8(z) using spectroscopically calibrated DESI galaxies and the latest CMB lensing measurements from Planck and ACT. These data are insensitive to many of the systematic complications present in galaxy lensing measurements, while our fiducial Hybrid Effective Field Theory model robustly regulates the information obtainable from smaller scales, such that our cosmological constraints are reliably derived from the (predominantly) linear regime. Our tomographic analysis of DESI Luminous Red Galaxies (LRG) prefers a slightly lower (5 − 7%) value of S_8 than primary CMB measurements with a statistical significance ranging from 1.8 − 2.3σ. Intriguingly, our lowest redshift LRG bin is most discrepant with a Planck cosmology, leaving open the possibility that structure growth is slowing down for redshifts z < 0.5. To address this possibility, I will conclude my talk with preliminary results from the DESI Bright Galaxy Survey, which enable tomographic S_8(z) measurements over the redshift range 0.1 \lesssim z \lesssim 0.4.

Pulsar timing arrays (PTAs) consist of a set of regularly monitored millisecond pulsars with extremely stable rotational periods. The arrival time of pulses can be altered by the passage of gravitational waves (GWs) between them and the Earth, thus serving as a galaxy-wide GW detector. Evidence for the first detection of low-frequency (~nHz) gravitational waves has recently been reported across multiple PTA collaborations, opening a new observational window into the Universe. Although the origin of the GW signal is yet to be determined, the dominant sources are expected to be inpiralling supermassive black holes (SMBHs). I will discuss a recent work in which we compare the GW detections by PTAs with the expected signal implied by existing electromagnetic observations in a simple but robust manner. This study suggests that the currently measured GW amplitude is larger than expected by a significant amount. I will then show that additional information regarding the typical number of sources contributing to the background can already be inferred from current PTA data.