ISIS Neutron and Muon Source Data Journal

Prediction of non-measured time spectra from measured one by Random Forest and Kolmogorov Arnold Network (KAN) regressors

Abstract: The machine-learning (ML) technique is a strong tool to handle big data. In the future the SuperMuSR spectrometer will give us huge amount of data rather than now making SR data analysis complicate and harder. We are developing ML data analysis method by using Random Forest and Kolmogorov Arnold Network regressors to predict non-measured time spectra from the measured one in order to realize a ML-controlled SR data taking and analysis. For this purpose, we propose to measure the high-purity Cu and obtained reference data which can be used to train those ML regressors.

Principal Investigator: Dr Muhammad Rabie Bin Omar
Experimenter: Professor Takayuki Goto
Experimenter: Dr Wan Nur Aini Wan Mokhtar
Experimenter: Dr Wan Nurfadhilah Binti Zaharim
Experimenter: Mr KEN FUI CHIN
Experimenter: Dr Ahmad Rujhan Bin Mohd Rais
Experimenter: Dr Lee Sin Ang
Experimenter: Mrs Anita Eka Putri
Local Contact: Dr James Lord
Experimenter: Dr Isao Watanabe

DOI: 10.5286/ISIS.E.RB2510286

ISIS Experiment Number: RB2510286

Publisher: STFC ISIS Neutron and Muon Source

Data format: RAW/Nexus
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Data Citation

The recommended format for citing this dataset in a research publication is as:
[author], [date], [title], [publisher], [doi]

For Example:
Dr Muhammad Rabie Bin Omar et al; (2025): Prediction of non-measured time spectra from measured one by Random Forest and Kolmogorov Arnold Network (KAN) regressors, STFC ISIS Neutron and Muon Source, https://doi.org/10.5286/ISIS.E.RB2510286

Data is released under the CC-BY-4.0 license.