Abstract: acorns generates a hierarchical system of clusters within discrete data by using an n-dimensional unsupervised machine-learning algorithm that clusters spectroscopic position-position-velocity data. The algorithm is based on a technique known as hierarchical agglomerative clustering. Although acorns was designed with the analysis of discrete spectroscopic position-position-velocity (PPV) data in mind (rather than uniformly spaced data cubes), clustering can be performed in n-dimensions and the algorithm can be readily applied to other data sets in addition to PPV measurements.
Credit: Henshaw, Jonathan; Sokolov, Vlas; Ginsburg, Adam
Preferred citation method: https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.2457H