![]() |
COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. | ![]() |
University of Cambridge > Talks.cam > Institute of Astronomy Galaxies Discussion Group > Lensed Dusty Star-Forming Galaxies and Dark Matter Substructure
Lensed Dusty Star-Forming Galaxies and Dark Matter SubstructureAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Sergey Koposov. Recently, wide-area mm/submm-wave surveys have discovered a large, new population of strongly lensed dusty, star-forming sources at high redshift (z~2-6). I will present ALMA resolved imaging of a sample of lensed sources discovered by the South Pole Telescope, in addition to the current state of lens models, which are allowing us to learn important properties about the sources. I will show that generalizing this lens modeling technique to fit the velocity components of molecular lines separately, effectively creating a 3D lens model of spatially resolved spectroscopic data, will drastically increase the sensitivity to the lensing signal of dark matter substructure in the lens halos. Using this method, I predict that there is >55% probability of detecting a substructure in a lens with a one-hour observation with ALMA if the mass fraction of substructure is f=1%. Given >100 lensing systems, this sample will be a promising place to look for dark matter substructure, and to measure the subhalo mass function with a high statistical significance. This talk is part of the Institute of Astronomy Galaxies Discussion Group series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsCambridge Genomic Services Seminars Extraordinary Category Theory Seminar Emmy Noether SocietyOther talksCosmological Probes of Light Relics The Ethical and Legal Elements of Capacity and Consent A sex-linked supergene controls sperm morphology and swimming speed in a songbird Large Scale Ubiquitous Data Sources for Crime Prediction |