Cosmology

Torsion is a popular ingredient in gravity, yet fraught with quantum and classical pathologies. I develop a novel torsion theory, consistent with power-counting and unitarity. The Friedmann equations emerge (with dark energy and radiation), as do pp waves and the Schwarzschild vacuum, all without an Einstein-Hilbert term. I show that cosmology sees torsion as a non-canonical scalar, revealing a rich phenomenology of conformal or waterfall inflatons, and cuscutons. I finally argue that future work will be driven less by toy-models, and more by computer surveys. I advocate Hamiltonian and effective field theory approaches to non-Riemannian geometry in general, relevant to ultraviolet completion and modified gravity alike. Such methods should be oriented towards the ultimate test of gravity: observed cosmological structure.

In the first part of the talk I will review some recent progress in large-scale structure theory and show how it can be used to measure cosmological parameters from current and future redshift surveys. Then I will discuss some ongoing challenges in the modeling of galaxy clsutering data and covariance matrices. Finally, I will present a systematic calculation of the probability distribution function for the dark matter density field and discuss its potential as a cosmological probe.

Gravitational waves (GWs) have already proved immensely powerful for constraining cosmological extensions of GR, both from data-driven and theoretical perspectives. However, GWs really come into their own when used in combination with complementary electromagnetic data. I’ll start by reviewing some of the bounds on extended gravity theories from GW detections to date. I'll introduce the formalism, the phenomenology, and the astrophysical pitfalls of these tests. Finally, we'll explore the impact of future experiments like LISA and accompanying galaxy surveys on the remaining parameter space of modified gravity theories.

In order to infer cosmological parameters from galaxy survey data, we typically use summary statistics such as the power spectrum and we need an accurate estimate of their covariance matrix. The traditional process of obtaining the covariance involves simulating thousands of mocks. I will present an analytic approach for the covariance matrix which is more than four orders of magnitude faster than mocks and show its validation with an analysis of the BOSS DR12 data. Furthermore, our analytic approach is free of sampling noise which makes it useful for upcoming surveys like DESI and Euclid. Towards the end, I will change gears and talk about some recent work on the assembly bias of neutral hydrogen.