Astrophysics

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.
 

The standard model of cosmology is built upon on a series of propositions on how the early, intermediate, and late epochs of the Universe behave. In particular, it predicts that dark energy and dark matter currently pervades the cosmos. Understanding the properties of the dark sector is plausibly the biggest challenge in theoretical physics. There is, however, a broad assumption in cosmology that the Universe on its earlier stages is fully understood and that discrepancies between the standard model of cosmology and current data are suggestive of distinct dark energy properties. Uncertainties on the validity of this hypothesis are not usually taken into account when forecasting survey capabilities, even though our investigations might be obfuscated if the intermediate and early Universe did behave abnormally. In this colloquium, I propose a program to investigate dark energy and earlier aspects of our Universe simultaneously, through space missions in the 2020s in combination with ground-based observatories. This program will help guide the strategy for the future LSST and WFIRST supernovae and weak lensing surveys. My investigations on how properties of the early and intermediate Universe affect inferences on dark energy (and vice-versa) will also support community understanding of how future missions can be employed to test some of the core hypotheses of the standard model of cosmology.

The new gravitational-wave signal GW190521in LIGO and Virgo marks the first observational detection of the elusive intermediate-mass black holes. The detection also confirms there exist a new class of black holes in the mass gap predicted by the pair-instability supernovae theory. In this talk, I will discuss the process that went behind inferring the astrophysical properties of this historic discovery. I would briefly address the alternative scenarios we looked into for a possible exotic origin of this signal, including any violation of General Relativity. For the upcoming ESA/NASA space mission LISA, I would highlight how this discovery opens a unique epoch of multi-band, multi-messenger astronomy.

The outskirts of accreting dark matter haloes exhibit a sudden drop in density delimiting the virialized region. After briefly describing the physics shaping this feature and how it is measured, I will discuss its applications. I will examine its connection to structure formation and how it can constrain the screening mechanisms of beyond-GR models of gravity.

Studying the smallest self-bound dark matter structure in our Universe can yield important clues about the fundamental particle nature of dark matter, and galaxy-scale strong gravitational lensing provides a unique way to detect and characterize dark matter on small scales at cosmological distances from the Milky Way. Research in this field can be broadly separated into works that aim to directly detect individual perturbers and works that aim to statistically constrain the matter distribution by looking at collective perturbations caused by an unresolved population of perturbers. We present recent advances in both of these approaches. With respect to the former, we present advances in the analysis of gravitational lenses and identification of small-scale clumps using machine learning. With respect to the latter, we introduce the convergence power spectrum as a promising statistical observable that can be extracted from strong lens images and used to distinguish between different dark matter scenarios, showing how different properties of the dark matter get imprinted on different scales. We include a discussion on the different contribution of substructure and line-of-sight structure to perturbations in strong lens images.