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Loïc Thomas Davies Coyle

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Related publications (4)

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Data-driven modeling of beam loss in the LHC

Tatiana Pieloni, Michael Schenk, Ekaterina Krymova, Loïc Thomas Davies Coyle

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscilla-tion amplitudes or large momentum error are scrape ...

FRONTIERS MEDIA SA2023

Application of machine learning techniques at the CERN Large Hadron Collider

Frédéric Blanc, Tatiana Pieloni, Michael Schenk, Leonid Rivkin, Loïc Thomas Davies Coyle

Machine learning techniques have been used extensively in several domains of Science and Engineering for decades. These powerful tools have been applied also to the domain of high-energy physics, in the analysis of the data from particle collisions, for ye ...

Trieste2020

A Novel Method for Detecting Unidentified Falling Object Loss Patterns in the LHC

Frédéric Blanc, Tatiana Pieloni, Davide Di Croce, Loïc Thomas Davies Coyle

Understanding and mitigating particle losses in the Large Hadron Collider (LHC) is essential for both machine safety and efficient operation. Abnormal loss distributions are tell- tale signs of abnormal beam behaviour or incorrect machine configuration. By ...

Journals of Accelerator Conferences Website (JACoW)2022

Machine learning for beam dynamics studies at the CERN Large Hadron Collider

Frédéric Blanc, Tatiana Pieloni, Michael Schenk, Loïc Thomas Davies Coyle

Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profiled from the power of these tools for advanced analysis of colliders data. It is only up until recen ...

2021