Cosmology

Through their observable properties, the first and smallest dark matter halos represent a rare probe of subkiloparsec-scale variations in the density of the early Universe. These density variations could hold clues to the nature of inflation, the postinflationary cosmic history, and the identity of dark matter. However, the dynamical complexity of these microhalos hinders their usage as cosmological probes. A theoretical understanding of the microhalo-cosmology connection demands numerical simulation, but microhalos are too small and dense to simulate up to the present day in full cosmological context. My research meets this challenge by using controlled numerical simulations to develop (semi)analytic models of dark matter structure. I will discuss these models, which describe the formation of the first halos and their subsequent evolution as they accrete onto larger systems. I will also explore two applications: breaking a degeneracy between the properties of thermal-relic dark matter and the postinflationary history, and probing inflation's late stages via the small-scale primordial power spectrum.

CMB lensing tomography has the potential to map the amplitude and growth of structure over cosmic time, provide some of the most stringent tests of gravity, and break important degeneracies between cosmological parameters. I use the unWISE photometric galaxy catalog to create three samples at median redshifts z~0.6, 1.1, and 1.5, and cross-correlate them with the most recent Planck CMB lensing maps. The resulting significance of ~80 at 100 < ell < 1000 is the highest significance detection to date of CMB lensing cross-correlation.  The redshift distribution of the two-band unWISE galaxies is a major source of systematic error. I primarily use cross-correlations with BOSS galaxies and quasars and eBOSS quasars to measure the redshift distribution, supplemented with cross-matching to deep COSMOS photometric redshifts.  I demonstrate how to propagate the uncertainty in the redshift distribution to the modeling of the signal, and perform a number of null tests. Finally, I discuss the cosmological implications of this measurement and lessons learned for CMB lensing cross-correlations with future photometric surveys.

We propose a model for combining the Standard Model (SM) with gravity. It relies on a non-minimal coupling of the Higgs field to the Ricci scalar and on the Palatini formulation of gravity. Without introducing any new degrees of freedom in addition to those of the SM and the graviton, this scenario achieves two goals. First, it generates the electroweak symmetry breaking by a non-perturbative gravitational effect. In this way, it does not only address the hierarchy problem but opens up the possibility to calculate the Higgs mass. Second, the model incorporates inflation at energies below the onset of strong-coupling of the theory. Provided that corrections due to new physics above the scale of inflation are not unnaturally large, we can relate inflationary parameters to data from collider experiments.
 

Cosmologists wish to explain how our universe, in all its complexity, could ever have come about. This is the problem of initial conditions and the first step towards its solution is the assessment of the universe’s entropy today. It is widely agreed upon that the entropy of vacuum energy, given by the Bekenstein bound, makes up the bulk of the current entropy budget, dominating over that of gravity and over thermal motions of the cosmic radiation background.

Have we counted all significant contributions to the entropy inventory? There is one number which we have not considered: the number of degrees of freedom in our vibrant, complex biosphere. What is the entropy of life? Is it sizeable enough to need to be accounted for at the Big Bang, or negligible compared to vacuum entropy?

In this talk, I will outline the forward model approach to reconstruct cosmological fields in a Bayesian framework. I will focus on two examples - galaxy clustering and neutral hydrogen intensity mapping.

In galaxy clustering example, I will use the observations of galaxy surveys to reconstruct the initial Lagrangian field. Here, we develop a novel framework with neural networks to forward model halo masses and positions and demonstrate that our method outperforms standard reconstruction in both real and redshift space. This reconstructed initial field has enhanced signal for baryon acoustic oscillations and can enhance science returns for surveys like DESI.

For neutral hydrogen surveys, we lose over 50% of the modes at high redshifts due to foregrounds and it severely hampers their feasibility for cosmological analysis. With a novel bias framework for the forward model, I will show that we are able to reconstruct over 90% of these modes and this recovers cross-correlations with photo-z surveys like LSST and tracers like CMB lensing.

Lastly, I will briefly touch upon assumptions made in this reconstruction framework regarding noise models and likelihood. I will discuss preliminary ways to improve upon them using deep learning.