Automatic Design of Artificial Neural Networks for Gamma-Ray Detection
This work addresses gamma-ray detection for astrophysics by improving discrimination accuracy, though it is incremental as it applies existing evolutionary methods to a specific domain.
The paper tackled gamma/hadron discrimination by using automatically designed Convolutional Neural Networks (CNNs) via NeuroEvolution, specifically Fast-DENSER++, resulting in a factor of 2 improvement over classic statistical approaches and a factor of 2.3 with ensembling.
The goal of this work is to investigate the possibility of improving current gamma/hadron discrimination based on their shower patterns recorded on the ground. To this end we propose the use of Convolutional Neural Networks (CNNs) for their ability to distinguish patterns based on automatically designed features. In order to promote the creation of CNNs that properly uncover the hidden patterns in the data, and at same time avoid the burden of hand-crafting the topology and learning hyper-parameters we resort to NeuroEvolution; in particular we use Fast-DENSER++, a variant of Deep Evolutionary Network Structured Representation. The results show that the best CNN generated by Fast-DENSER++ improves by a factor of 2 when compared with the results reported by classic statistical approaches. Additionally, we experiment ensembling the 10 best generated CNNs, one from each of the evolutionary runs; the ensemble leads to an improvement by a factor of 2.3. These results show that it is possible to improve the gamma/hadron discrimination based on CNNs that are automatically generated and are trained with instances of the ground impact patterns.