Talks

A dark energy-like component in the early universe, known as early dark energy (EDE), is a proposed solution to the Hubble tension. In this talk, I will describe how a frequentist profile likelihood yields important complementary information compared to a Bayesian MCMC analysis. While in an MCMC analysis, the EDE model is clearly disfavoured by Cosmic Microwave Background and large-scale structure data, a profile likelihood analysis prefers consistently larger amounts of EDE and with that a Hubble constant consistent with the SH0ES measurement for the same data sets. The difference between MCMC and profile likelihood can be explained by prior volume effects in the MCMC analysis. I will discuss how frequentist and Bayesian methods can give important complementary information in the context of beyond-LCDM models.

Zoom link:  https://pitp.zoom.us/j/97257428766?pwd=ckx4ajRsQVRQUnpXaGEvZEtEWW9ldz09

Optimal extraction of the non-Gaussian information encoded in the Large-Scale Structure (LSS) of the universe lies at the forefront of modern precision cosmology. In this talk, I will discuss recent efforts to achieve this task using the Wavelet Scattering Transform (WST), which subjects an input field to a layer of non-linear transformations that are sensitive to non-Gaussianity in spatial density distributions through a generated set of WST coefficients. In order to assess its applicability in the context of LSS surveys, I will present the first WST application to actual galaxy observations, through a WST re-analysis of the BOSS DR12 CMASS dataset. After laying out the procedure on how to capture all necessary layers of realism for an application on data obtained from a spectroscopic survey, I will show results for the marginalized posterior probability distributions of 5 cosmological parameters obtained from a WST likelihood analysis of the CMASS data. The WST is found to deliver a substantial improvement in the values of the predicted 1σ errors compared to the regular galaxy power spectrum, both in the case of flat and uninformative priors and also when a Big Bang Nucleosynthesis prior is applied to the value of ω_b. Finally, I will discuss ongoing follow-up work towards applying this estimator to the next generation of spectroscopic observations to be obtained by the DESI and Euclid surveys.

Zoom link:  https://pitp.zoom.us/j/96291506998?pwd=TVVFYnNIQ1F0cktna000cUp3SU1kQT09

Sofia Gonzalez Garcia, Perimeter Institute & University of Waterloo

Tensor Networks in a Nutshell

This seminar will be an introductory 'class' to Tensor Networks, so I will assume no previous knowledge on the subject. I aim to provide motivation for this powerful ansatz as well as some of its applications. I will introduce the main architectures (MPS, PEPS and MERA) and their characteristics. Throughout the seminar I will try to include part of the material I have produced for a 2017 PSI course taught by Guifre Vidal. 

If time permits I will also briefly introduce my research on the subject, which is on 2D tensor networks contraction and its accuracy convergence. 

The dark universe may host physics as rich and complex as the visible sector, but the only guaranteed window to the dark sector(s) is through gravity. If the dark matter has a dissipative self-interaction, dark gas can cool and collapse to form compact object whose mergers may be accessible to LIGO. The mass spectrum of the merging compact objects encodes fundamental physical information--a purely gravitational probe of dark matter microphysics.
In this talk, I will present our work to forward-model the gas collapse process in the "atomic dark matter" model, beginning with a retelling of the standard cosmological history including this new ingredient and culminating in a description of the fragmentation scale of the dark gas.

Zoom link:  https://pitp.zoom.us/j/99141938599?pwd=T0I1d1A5R0JBNWFlSHlCREl5dElTUT09