Description:

There has been a surge of interest in hidden valley models with new, strong forces, sometimes called "dark QCD". These models propose asymmetric, composite dark matter in the form of "dark hadrons" that would evade direct and indirect bounds as well as typical collider DM searches for large missing transverse momentum accompanied by radiation. However, evidence of these models can still be found in collider datasets by targeting their unique phenomenological signatures, which include semi-visible jets, emerging jets, and soft unclustered energy patterns. We will present the first experimental results for all these signatures, which have made significant strides in exploring the vast space of dark QCD models. We will further discuss the prospects for dramatic improvements in sensitivity using machine learning.

Kevin Pedro, Associate Scientist, Fermilab

Host: Mayda Velasco