Talks

Fundamental physics (including physics beyond the Standard Model) can be tested using table-top precision measurements. The talk will describe measurements of the size of the proton, the fine-structure constant and the electric dipole moment of the electron. Two recently completed measurements will be described. For the first measurement, the n=2 Lamb shift of atomic hydrogen is measured, allowing for a new determination of the charge radius of the proton. This determination is crucial to resolving the decade-old proton radius puzzle, in which it appeared that the proton radius took on a different value when measured with muons compared to measurements using electrons. The second measurement is of the n=2 triplet P fine structure of atomic helium, and this work is part of a program to obtain a new determination of the fine-structure constant. Both of these measurements use a new measurements technique: Frequency offset separated oscillatory fields. Finally, a new major effort (EDM^3) is starting at York University to measure the electron electric dipole moment using polar molecules embedded into inert-gas solids. This measurement will test for T violation and will probe physics up to the PeV energy scale.    

I will consider simple scalar gauge theories with one scalar field in a low-dimensional representation of a gauge group. The renormalizable action often has accidental symmetries that lead to one or more stable states, providing Dark Matter candidates. The gauge group can confine, or be spontaneously broken by the scalar field: I will discuss the spectrum and symmetries in both cases, focusing in particular on possible dualities between the Higgs and confined phases. I will then discuss the Dark Matter phenomenology in a few illustrative cases, showing that the thermal relic abundance can reproduce the cosmological value for masses around 100 TeV.

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.