Searching for hidden particles with the SHiP experiment and development of machine learning methods
Lead Research Organisation:
University of Bristol
Department Name: Physics
Abstract
Using Deep Learning to:
- Generate vast numbers of protons-on-target and simulate the detector response.
- Suppress the large combinatorial muon background
- Generate vast numbers of protons-on-target and simulate the detector response.
- Suppress the large combinatorial muon background
People |
ORCID iD |
| Konstantinos Petridis (Primary Supervisor) | |
| Alex Marshall (Student) |
Publications
Ahdida C
(2019)
Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
in Journal of Instrumentation
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/P006779/1 | 01/10/2017 | 30/09/2024 | |||
| 2028277 | Studentship | ST/P006779/1 | 01/10/2017 | 30/09/2021 | Alex Marshall |
| Description | SHiP |
| Organisation | European Organization for Nuclear Research (CERN) |
| Country | Switzerland |
| Sector | Academic/University |
| PI Contribution | SHiP collaboration at CERN |
| Collaborator Contribution | Responsible for RPV SUSY sensitivity, muon combinatorial background and fast simulations |
| Impact | Paper |
| Start Year | 2015 |