Identifying Transients in the Dark Energy Survey using Convolutional Neural Networks

Abstract

The ability to discover new transients via image differencing without direct human intervention is an important task in observational astronomy. For these kind of image classification problems, machine Learning techniques such as Convolutional Neural Networks (CNNs) have shown remarkable success. In this work, we present the results of an automated transient identification on images with CNNs for an extant dataset from the Dark Energy Survey Supernova program (DES-SN), whose main focus was on using Type Ia supernovae for cosmology. By performing an architecture search of CNNs, we identify networks that efficiently select non-artifacts (e.g. supernovae, variable stars, AGN, etc.) from artifacts (image defects, mis-subtractions, etc.), achieving the efficiency of previous work performed with random Forests, without the need to expend any effort in feature identification. The CNNs also help us identify a subset of mislabeled images. Performing a relabeling of the images in this subset, the resulting classification with CNNs is significantly better than previous results.

arXiv comment14 pages, 13 figures

References in corpus (14)

• Array Programming with NumPy
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• Real-bogus classification for the Zwicky Transient Facility using deep learning
• Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
• Automated Transient Identification in the Dark Energy Survey
• Machine Learning for the Zwicky Transient Facility
• Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
• Machine learning for transient discovery in Pan-STARRS1 difference imaging
• How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging
• Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys
• Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks -- sifting the GOTO candidate stream
• Vetting the optical transient candidates detected by the GWAC network using convolutional neural networks
• Classifying Image Sequences of Astronomical Transients with Deep Neural Networks
• The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments

Cited by in corpus (3)

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• The automation of optical transient discovery and classification in Rubin-era time-domain astronomy
• Impact of PSF misestimation and galaxy population bias on precision shear measurement using a CNN