Video URL

https://pirsa.org/23040158

Searching for the fundamental nature of dark matter in the cosmic large-scale structure

Rogers, K. (2023). Searching for the fundamental nature of dark matter in the cosmic large-scale structure . Perimeter Institute for Theoretical Physics. https://pirsa.org/23040158

Rogers, Keir. Searching for the fundamental nature of dark matter in the cosmic large-scale structure . Perimeter Institute for Theoretical Physics, Apr. 17, 2023, https://pirsa.org/23040158 

          @misc{ scivideos_PIRSA:23040158,
            doi = {10.48660/23040158},
            url = {https://pirsa.org/23040158},
            author = {Rogers, Keir},
            keywords = {Cosmology},
            language = {en},
            title = {Searching for the fundamental nature of dark matter in the cosmic large-scale structure },
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2023},
            month = {apr},
            note = {PIRSA:23040158 see, \url{https://scivideos.org/pirsa/23040158}}
          }

Keir Rogers University College London

DOI 10.48660/23040158
Source Repository PIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

The fundamental nature of dark matter (DM) so far eludes direct detection experiments, but it has left its imprint in the large-scale structure (LSS) of the Universe. I will present a search using cosmic microwave background (CMB) and galaxy surveys for ultra-light DM particle candidates called axions that are well motivated from high energy theory. In combining these datasets, I will discuss how the presence of axions can improve consistency between these probes and, in particular, help alleviate the S_8 cosmological parameter tension (the discrepancy in the amplitude of density fluctuations as inferred from CMB and galaxy data). I will then present complementary searches for ultra-light and light (sub-GeV) DM using a LSS probe called the Lyman-alpha forest. By combining complementary large- and small-scale structure probes, I will demonstrate how current and forthcoming cosmological data will systematically test the nature of DM. In order to model novel DM physics accurately and efficiently in CMB and LSS probes, I will present new machine learning approaches using neural network "emulators" to accelerate DM parameter inference from days to seconds and active learning to reduce massively the computational expense.

Zoom Link: TBD