Paper
Classifying High-Energy Celestial Objects with Machine Learning Methods
Authors
Alexis Mathis, Daniel Yu, Nolan Faught, Tyrian Hobbs.
Abstract
Machine learning is a field that has been growing in importance since the early 2010s due to the increasing accuracy of classification models and hardware advances that have enabled faster training on large datasets. In the field of astronomy, tree-based models and simple neural networks have recently garnered attention as a means of classifying celestial objects based on photometric data. We apply common tree-based models to assess performance of these models for discriminating objects with similar photometric signals, pulsars and black holes.
We also train a RNN on a downsampled and normalized version of the raw signal data to examine its potential as a model capable of object discrimination and classification in real-time.