MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition

Abstract

We introduce MANTRA, an annotated dataset of 4869 transient and 71207 non-transient object lightcurves built from the Catalina Real Time Transient Survey. We provide public access to this dataset as a plain text file to facilitate standardized quantitative comparison of astronomical transient event recognition algorithms. Some of the classes included in the dataset are: supernovae, cataclysmic variables, active galactic nuclei, high proper motion stars, blazars and flares. As an example of the tasks that can be performed on the dataset we experiment with multiple data pre-processing methods, feature selection techniques and popular machine learning algorithms (Support Vector Machines, Random Forests and Neural Networks). We assess quantitative performance in two classification tasks: binary (transient/non-transient) and eight-class classification. The best performing algorithm in both tasks is the Random Forest Classifier. It achieves an F1-score of 96.25% in the binary classification and 52.79% in the eight-class classification. For the eight-class classification, non-transients ( 96.83% ) is the class with the highest F1-score, while the lowest corresponds to high-proper-motion stars ( 16.79% ); for supernovae it achieves a value of 54.57% , close to the average across classes. The next release of MANTRA includes images and benchmarks with deep learning models.

ApJS accepted, 17 pages, 14 figures

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• Machine Learning based search for Cataclysmic Variables within Gaia Science Alerts
• The four cosmic tidal web elements from the $\beta$-skeleton
• Machine Learning applications for Cataclysmic Variable discovery in the ZTF alert stream
• Automatic detection of solar flares observed at 45 GHz by the POEMAS telescope
• Predicting Stellar Parameters of Massive Stars from Light Curves with Machine Learning