PIRSA:23010057

Video URL

https://pirsa.org/23010057

Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with Wavelet Scattering Transforms

Valogiannis, G. (2023). Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with Wavelet Scattering Transforms. Perimeter Institute for Theoretical Physics. https://pirsa.org/23010057

Valogiannis, Georgios. Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with Wavelet Scattering Transforms. Perimeter Institute for Theoretical Physics, Jan. 17, 2023, https://pirsa.org/23010057 

          @misc{ scivideos_PIRSA:23010057,
            doi = {10.48660/23010057},
            url = {https://pirsa.org/23010057},
            author = {Valogiannis, Georgios},
            keywords = {Cosmology},
            language = {en},
            title = {Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with Wavelet Scattering Transforms},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2023},
            month = {jan},
            note = {PIRSA:23010057 see, \url{https://scivideos.org/index.php/pirsa/23010057}}
          }

Georgios Valogiannis Harvard University

Talk numberPIRSA:23010057
DOI10.48660/23010057
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

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