Speaker
Description
The teaching of nuclear physics has traditionally followed a rather rigid pedagogical perspective offering a chronological view of the evolution of the topic. Theoretical perspectives are emphasised such as the independent-particle shell model, moving on to collective models such as the rotational model. Comparison of model predictions with experimental data is frequently limited leaving the student unclear about the limitations of the different models.
We argue for an alternative pedagogical perspective on the subject, starting with the data and seeing how the patterns observed necessarily lead to concepts such as nuclear rotation. Indeed, rotational behaviour in nuclei seems to emerge as the one paradigm strongly supported by data while evidence appears scarce, for example, in the case of true independent-particle motion behaviour. Moreover, the data points to emerging complexity in nuclear structure even in systems we might expect to be simple such as doubly-magic nuclei. Indeed, shape coexistence appears to be a ubiquitous perspective although historically this has been confused with different naming conventions in different mass regions such as “islands of inversion” and “fission isomers”.
A new approach to nuclear physics teaching starting with data has been outlined in a trilogy of electronic textbooks written by the presenter and John Wood. The first book, Nuclear Data: A Primer, provides an introduction to nuclear structure with the two later volumes, Nuclear Data: A Collective Motion View and Nuclear Data: An Independent-Particle Motion View focussing on more specific topics in nuclear structure. Through video-based tutorials and exercises, these books encourage the reader’s independent explorations making use of the contents of databases such as ENSDF. Indeed, the time is ripe to apply recently developed data science techniques to this data and discover further hidden trends. This presentation will highlight some of the key approaches found in this text book series.