Phenomenology for the Large Hadron Collider
Lead Research Organisation:
University of Glasgow
Department Name: College of Science and Engineering
Abstract
The student will be involved in aspects of Higgs and BSM phenomenology at the Large Hadron Collider and will further clarify the phenomenological cases of future particle physics experiments. The obtained results will allow the community to formulate predictions and proposals for measurements at present and future collider experiments and gain a deeper understanding of the nature of the TeV scale and its relation to phenomena that are not explained in the current formulation of particle physics. As part of this project, new statistical approaches will be developed. A particular focus will be the novel application of machine learning techniques to model-independent searches for new physics.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/T506102/1 | 01/10/2019 | 30/09/2023 | |||
| 2284574 | Studentship | ST/T506102/1 | 01/10/2019 | 31/03/2023 | Panagiotis Stylianou |
| Description | The discovery of the Higgs particle in 2012 has established the "Standard Model of Particle Physics" as the best theory to describe the fundamental particles and their interactions. However, the flaws of the theory (no explanation of dark matter, no explanation of baryogenesis, gravity is not incorporated, etc) still leave many unanswered questions regarding the origins of our Universe. As part of this award, different avenues to push for new future discoveries were considered: A) Exploration of different particle interactions that can be detected in future experiments that are currently under consideration, within specific particle physics theories but also with model-independent approaches. B) Incorporation of highly-efficient machine-learning techniques to fully exploit information in data and ultimately enhance the discovery potential of experiments. C) Scrutiny of experimental results in order to understand whether they can be arising from new interactions in particle physics. |
| Exploitation Route | The outcomes of the award are expected to be taken forward mostly within academic routes due to the nature of the research. In particular: A) Collaborators and other researchers are expected to use conclusions or results for further studies. B) The scrutiny of experimental results and enhancement of significance through Machine Learning algorithms are expected to help experimental collaborations (e.g. at CERN) when designing future analyses. |
| Sectors | Other |