Exploring Nuclear Structure with Machine Learning

Thursday 15 June 2017, 2.00PM to 3.00pm

Speaker(s): Dr Sam Bailey, University of Birmingham

Machine learning has been shown to be incredible powerful for pattern recognition and making predictions in a variety of research fields. Here it is applied to classify the structure of nuclei, based on experimental measurements of resonant scattering reactions.

The nuclear structure of interest is nuclear clustering, the phenomenon whereby nucleons form substructures within the nucleus. This is a well-established concept in light nuclei, however the extent to which clustering persists into medium-mass and heavy nuclei is unclear. One reason for this is that the increasing complexity observed in heavier systems reduces the effectiveness of the traditional experimental techniques and analysis methodologies that have been used to identify clustered structures in light nuclei.

In this seminar, the results of the analysis of alpha-clustering in 44Ti, 48Ti and 52Ti are presented.

Location: P/T/111