There are a variety of interesting theoretical models that predict the signature known as Semivisible Jets. These are jets of hidden particles of which only a fraction decay visibly. Many of these models provide an elegant solution to open questions concerning the origin of dark matter and/or the problem of the apparent unnaturalness of the Standard Model. Due to the challenges of their experimental signatures, these classes of models are underdeveloped and poorly explored. Recent developments in reconstruction and identification techniques made it possible to probe such models at the Large Hadron Collider experiments.
This Workshop intends to foster the collaboration of the experimental and theory communities to establish a set of realistic benchmark models that will drive future search strategies. Theorists and experimentalists will work together in the period leading to the Workshop to focus on potentially interesting models. The outcome of these feasibility studies will inform and guide the workshop discussion. The Workshop will also feature talks from experts in Monte Carlo generators, QCD-related jet variables, and machine learning tools.
The outcome of the workshop studies and discussions will be summarised in a white paper.
The Workshop will be in hybrid mode: at the Hönggerberg campus of ETH Zürich and online via zoom:
Mattermost team: SVJCommunity
- Kathryn Zurek: "Theory of Hidden Sector Dark Matter"
- Huegues Beauchesne : "Phenomenology of Hidden Sector Dark Matter"
- Torbjorn Sjostrand: "Hidden Valleys in Phythia"
- Simon Plaetzer: "Hidden Valleys in Herwig"
- Suchita Kulkarni: "Dark Showers - Snowmass paper"
- Thea Aarrestad: "Machine Learning Applications: an experimental perspective"
- Barry Dillon: "Machine Learning Applications: a theoretical perspective"
- Kevin Pedro: "Semivisible Jets at CMS"
- Sukanya Sinha: "Semivisible Jets at ATLAS"
- Frederic Dryer: "Jet Substructure Overview"
- Alejandro Gomez Espinoza: "Jets at CMS"
- Matt Le Blanc: "Jets at ATLAS"
The Swiss National Science Foundation kindly supports the event.