Classifying image sequences of astronomical transients with deep neural networks. Issue 3 (2nd October 2020)

• Record Type: Journal Article
• Authors: Gómez, Catalina; Neira, Mauricio; Hernández Hoyos, Marcela; Arbeláez, Pablo; Forero-Romero, Jaime E
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 499:Issue 3(2020)
• Page Start: 3130
• Page End: 3138
• Publication Date: 2020-10-02
• Subjects: methods: numerical -- astronomical data bases -- transients: supernovae; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/staa2973
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: Supervised classification of temporal sequences of astronomical images into meaningful transient astrophysical phenomena has been considered a hard problem because it requires the intervention of human experts. The classifier uses the expert's knowledge to find heuristic features to process the images, for instance, by performing image subtraction or by extracting sparse information such as flux time-series, also known as light curves. We present a successful deep learning approach that learns directly from imaging data. Our method models explicitly the spatiotemporal patterns with deep convolutional neural networks and gated recurrent units. We train these deep neural networks using 1.3 million real astronomical images from the Catalina Real-Time Transient Survey to classify the sequences into five different types of astronomical transient classes. The TAO-Net (for Transient Astronomical Objects Network) architecture outperforms the results from random forest classification on light curves by 10 percentage points as measured by the F1 score for each class; the average F1 over classes goes from $45{{\ \rm percent}}$ with random forest classification to $55{{\ \rm percent}}$ with TAO-Net. This achievement with TAO-Net opens the possibility to develop new deep learning architectures for early transient detection. We make available the training data set and trained models of TAO-Net to allow for future extensions of this work.