Enhanced Rotational Invariant Convolutional Neural Network for Supernovae Detection

Understand

In this paper, we propose an enhanced CNN model for detecting supernovae (SNe).

• This is done by applying a new method for obtaining rotational invariance that exploits cyclic symmetry.
• In addition, we use a visualization approach, the layer-wise relevance propagation (LRP) method, which allows finding the relevant pixels in each image that contribute to discriminate between SN candidates and artifacts.
• We introduce a measure to assess quantitatively the effect of the rotational invariant methods on the LRP relevance heatmaps.