Journey to the Dark Side: Exploring Dark Meson Decays in Unconventional Signatures at Colliders with Machine Learning

This project targets a hidden dark sector that could explain dark matter.

At its core are dark mesons that decay into standard model particles, resulting in intricate final states with unique features, like collimated jets with substructure and long-lived signatures, that challenge conventional analysis techniques.The project targets an unexplored research frontier that promises both short- (LHC Run-3) and long-term (HL-LHC, future Higgs factories) impact on collider studies of dark matter.

It will push the boundaries of the research area by developing cutting-edge machine learning techniques to reconstruct and identify the signal while remaining physics-aware.The project will be led by the PI, who will oversee the project on a half-time basis.

One PhD student will be hired to join the team, that includes a dedicated postdoc, two PhD students, and a group of enthusiastic project and master students.

The PI has the necessary expertise and is strategically positioned in the field, having led the team behind the only existing pilot studies.Building on the success of a previously awarded grant, this project has the potential to make significant contributions to the scientific domain, potentially leading to a discovery that could revolutionize our understanding of the Universe.

By positioning Swedish fundamental research at the forefront of dark matter searches in colliders and machine learning for high-energy physics, this project is set to leave a lasting impact on the field.