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| Funder | Science and Technology Facilities Council |
|---|---|
| Recipient Organization | University of Liverpool |
| Country | United Kingdom |
| Start Date | Sep 30, 2022 |
| End Date | Sep 29, 2026 |
| Duration | 1,460 days |
| Number of Grantees | 2 |
| Roles | Student; Supervisor |
| Data Source | UKRI Gateway to Research |
| Grant ID | 2760510 |
The Large Hadron Collider (LHC) is the world's largest and most powerful particle accelerator where two counter circulating beams of protons or ions are passed repeatedly though 27km of vacuum pipe. Their motion is guided by super conducting magnets and numerous radio frequency (RF) accelerating structures to achieve particle energies unmatched on Earth, before being brought into collision in the experiments.
The RF cavities necessitate the beam to be structured into bunches so that each bunch sees an accelerating voltage on every pass around the ring . As the beams are travelling at 99.9999991% of the speed of light, each bunch would have a minimum separation in time of 25 ns between bunches and so to for the injection and extraction magnets to effect only 1 bunch at a time, 9 out of every 10 possible bunches are left empty.
During operation, empty buckets can be filled with "ghost" and "satellite" bunches which can become a problem for machine protection and absolute luminosity calibration. To this end the Longitudinal Density Monitor (LDM) was developed and uses synchrotron radiation from the beam to non-invasively and continuously monitor the beam.
The LDM has a dynamic range sufficient to monitor both the nominal and "ghost" bunches with a time resolution on the order of 50 ps allowing, over many turns, to build a complete bunch profile of the beams in the LHC.
This project will analyse vast volumes of data using modern machine learning and data science techniques to develop a novel tool for precise real-time luminosity measurements, including techniques to correct for bias associated with different monitor types, beam distributions and beam-beam effects as well as an investigation in to the impact of pileup effects and the development of strategies for its mitigation.
University of Liverpool
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