Speaker
Description
Historically, students entering the field of nuclear structure learn about the topic through encountering models. Often, the order in which models are presented echoes the chronology of discoveries within the topic. Students are then provided with one or two selected examples of nuclear data which strongly support a particular model. In this way, they often fail to achieve a wider understanding of the field or the utility of particular models.
We have been pioneering an alternative pedagogical approach to nuclear structure, starting with the data and seeing how that suggests models rather than reverse. Access to large databases of validated data is a particular feature of nuclear structure, unusual to other fields, and naturally associated with the societal relevance of such data. This abundance of data encourages student-led investigations. It also leads to an appreciation of which models have the widest utility in describing nuclei such as the rotational model while the extreme independent particle model is applicable to very few extant cases. We have developed a series of textbooks that follow this Nuclear Data approach, starting with Nuclear Data: A Primer. This was followed with intermediate level textbooks entitled and Nuclear Data: A Collective Motion View and Nuclear Data: An Independent-Particle Motion View.
In this presentation, we will give an overview of the philosophy beyond our pedagogical approach providing some interesting examples. We will emphasise the potential for future applications of techniques in Data Science and Machine Learning. Such interdisciplinary approaches to the data may lead to new insights especially those driven by students who approach the subject with a fresh pair of eyes.