📝 SummarXiv

Abstract

We implement an outlier detection model, an Isolation Foest (iForest), to uncover anomalous objects in the Galaxy and Mass Assembly Fourth Data Release (GAMA DR4). The iForest algorithm is an unsupervise Machine Learning (ML) technique. The data used is the spectroscopic and photometric data from GAMA DR4, which compiless information for over 300000 objects. We select two samples of galaxies to isolate, high signal-to-noise galaxies, to analyse the iForest's robustness, and E+A galaxies, to study the extremes of their population. This results in six-subsamples of spectroscopic, photometric and combined data isolations, finding 101 anomalous objects, half of which have not been identified as outliers in other works. We also find a number of fringing errors and false emission lines, displaying the iForest's potential in detecting these errors. Finding anomalous E+A galaxies, that although selected in a normal manner, using low [OII] and strong H{\delta} absorption, are still star-forming, with strong H{\alpha} emission. We propose two solutions to why these E+A galaxies are still star-forming but also question if these galaxies can be truly classified as E+A galaxies. We suggest that small-scale interactions on the galaxies causes small star bursts. The radiative pressure when forming high mass stars form expels the accreting material quicker than it can be accreted. We also suggest that the Jeans limit in our anomalous E+A galaxies is so low that it is simply not possible to form O and B class stars, but not low enough to fully prevent star-formation.