Elements of effective machine learning datasets in astronomy by Bernie Boscoe et al. on Wednesday 30 November

In this work, we identify elements of effective machine learning datasets in

astronomy and present suggestions for their design and creation. Machine

learning has become an increasingly important tool for analyzing and

understanding the large-scale flood of data in astronomy. To take advantage of

these tools, datasets are required for training and testing. However, building

machine learning datasets for astronomy can be challenging. Astronomical data

is collected from instruments built to explore science questions in a

traditional fashion rather than to conduct machine learning. Thus, it is often

the case that raw data, or even downstream processed data is not in a form

amenable to machine learning. We explore the construction of machine learning

datasets and we ask: what elements define effective machine learning datasets?

We define effective machine learning datasets in astronomy to be formed with

well-defined data points, structure, and metadata. We discuss why these

elements are important for astronomical applications and ways to put them in

practice. We posit that these qualities not only make the data suitable for

machine learning, they also help to foster usable, reusable, and replicable

science practices.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.14401v2