Search Results for author: S. R. Giblin

Found 3 papers, 0 papers with code

Joint machine learning analysis of muon spectroscopy data from different materials

no code implementations17 Dec 2021 T. Tula, G. Möller, J. Quintanilla, S. R. Giblin, A. D. Hillier, E. E. McCabe, S. Ramos, D. S. Barker, S. Gibson

A change in the asymmetry function might indicate a phase transition; however, these changes can be very subtle, and existing methods of analyzing the data require knowledge about the specific physics of the material.

BIG-bench Machine Learning

Phase Slips and Metastability in Granular Boron-doped Nanocrystalline Diamond Microbridges

no code implementations5 Jan 2021 G. M. Klemencic, D. T. S. Perkins, J. M. Fellows, C. M. Muirhead, R. A. Smith, S. Mandal, S. Manifold, M. Salman, S. R. Giblin, O. A. Williams

A phase slip is a localized disturbance in the coherence of a superconductor allowing an abrupt 2$\pi$ phase shift.

Superconductivity Mesoscale and Nanoscale Physics Materials Science

Machine Learning approach to muon spectroscopy analysis

no code implementations9 Oct 2020 T. Tula, G. Möller, J. Quintanilla, S. R. Giblin, A. D. Hillier, E. E. McCabe, S. Ramos, D. S. Barker, S. Gibson

We discovered that the PCA method works well in detecting phase transitions in muon spectroscopy experiments and can serve as an alternative to current analysis, especially if the physics of the studied material are not entirely known.

BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.