Video URL
https://pirsa.org/15100057Unlocking Dark Matter Physics out of Galactic Substructures
APA
(2015). Unlocking Dark Matter Physics out of Galactic Substructures. Perimeter Institute for Theoretical Physics. https://pirsa.org/15100057
MLA
Unlocking Dark Matter Physics out of Galactic Substructures. Perimeter Institute for Theoretical Physics, Oct. 27, 2015, https://pirsa.org/15100057
BibTex
@misc{ scivideos_PIRSA:15100057, doi = {10.48660/15100057}, url = {https://pirsa.org/15100057}, author = {}, keywords = {Cosmology}, language = {en}, title = {Unlocking Dark Matter Physics out of Galactic Substructures}, publisher = {Perimeter Institute for Theoretical Physics}, year = {2015}, month = {oct}, note = {PIRSA:15100057 see, \url{https://scivideos.org/pirsa/15100057}} }
Abstract
Despite being ubiquitous throughout the Universe, the fundamental physics governing dark matter remains a mystery. While this physics plays little role in the current evolution of large-scale cosmic structures, it did have a major impact in the early epochs of the Universe on the evolution of cosmological density fluctuations on small causal length scales. Studying the astrophysical structures that resulted from the gravitational collapse of fluctuations on these small scales can thus yield important clues about the physics of dark matter. Today, most of these structures are locked in deep inside the potential wells of galaxies, making the study of their properties difficult. Fortunately, due to fortuitous alignments between high-redshift bright sources and us, some of these galaxies act as spectacular strong gravitational lenses, allowing us to probe their inner structure. In this talk, we present a unified framework to extract information about the power spectrum of gravitational potential fluctuations inside any type of lens galaxies. We argue that fully exploiting this new approach will likely require a paradigm shift in how we describe structures on sub-galactic scales. We finally discuss which properties of mass substructures are most readily constrained by lensing data.