Input parameters, code and synthetic data for High-order spectral deconvolution of complex x-ray distributions using machine learning
| dc.contributor.author | Brainthra, Anandaeaswaran | |
| dc.contributor.author | Armstrong, C. D | |
| dc.contributor.author | Scott, Graeme | |
| dc.contributor.author | Thiyagalingam, Jeyan | |
| dc.contributor.author | Rajeev, Paramel Pattathil | |
| dc.contributor.author | Villarini, Barbara | |
| dc.date.accessioned | 2026-09-21T13:16:31Z | |
| dc.date.issued | 2026-09 | |
| dc.description.abstract | The data associated with this paper is as follows: input parameters to generate spectra, initialise spectrometer designs and ml model, code for data generation, training and testing and trained models and generated results. A readme file is included containing detailed information about the included files and their roles, installing the provided python libraries and usage on how to perform training and evaluation. | |
| dc.identifier.uri | https://edata.stfc.ac.uk/handle/edata/1015 | |
| dc.identifier.uri | https://doi.org/10.5286/edata/983 | |
| dc.language.iso | en | |
| dc.relation.isreferencedby | https://doi.org/10.1088/2632-2153/ae9f15 | |
| dc.rights | Creative Commons Attribution 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.other | x-ray diagnostics | |
| dc.subject.other | machine learning | |
| dc.subject.other | high repetition rate | |
| dc.subject.other | laser-plasma | |
| dc.title | Input parameters, code and synthetic data for High-order spectral deconvolution of complex x-ray distributions using machine learning | |
| dc.type | Collection |