PIRSA: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. https://pirsa.org/23040158

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

          @misc{ pirsa_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},
            year = {2023},
            month = {apr},
            note = {PIRSA:23040158 see, \url{https://pirsa.org}}
          }

Keir Rogers

University College London

Talk number
PIRSA:23040158
Collection
Talk Type
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