PIRSA:18100041

Detecting Dark Blobs

APA

Grabowska, D. (2018). Detecting Dark Blobs. Perimeter Institute for Theoretical Physics. https://pirsa.org/18100041

MLA

Grabowska, Dorota. Detecting Dark Blobs. Perimeter Institute for Theoretical Physics, Oct. 02, 2018, https://pirsa.org/18100041

BibTex

          @misc{ scivideos_PIRSA:18100041,
            doi = {10.48660/18100041},
            url = {https://pirsa.org/18100041},
            author = {Grabowska, Dorota},
            keywords = {Particle Physics},
            language = {en},
            title = {Detecting Dark Blobs},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2018},
            month = {oct},
            note = {PIRSA:18100041 see, \url{https://scivideos.org/index.php/pirsa/18100041}}
          }
          

Dorota Grabowska Lawrence Berkeley National Laboratory

Talk numberPIRSA:18100041
Source RepositoryPIRSA
Collection

Abstract

Most current dark matter detection strategies, including both direct and indirect efforts, are based on the assumption that the galactic dark matter number density is quite high, allowing for the detection of rare scattering events. Such a paradigm arises naturally if the dark matter self-interactions are weak. However, strong interactions within the dark sector can give rise to large composite objects, whose detection requires a different experimental paradigm. We call these object Dark Blobs. In such theories, the energy transfer due to a single collision with a Standard Model particle tends to be small, below the energy threshold of many nuclear recoil experiments. Fortunately, due to their exponentially large composite nature, the interactions between these objects and a terrestrial detector can be coherently enhanced. Therefore, while the effect on a single probe is small, the large collective effect can be quite dramatic and, importantly, above experimental thresholds. In this talk, I will briefly motivate the early Universe formation of certain types of Dark Blobs and then focus on multiple detection avenues for these objects.