Active anomaly detection for time-domain discoveries

Abstract

Aims. We present the first piece of evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects that can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new piece of information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional isolation forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery algorithm to two data sets: simulated light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the Active Anomaly Discovery results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ∼2% highest anomaly scores. Results. We show that, in the real data scenario, Active Anomaly Discovery was able to identify ∼80% more true anomalies than the IF. This result is the first piece of evidence that active anomaly detection algorithms can play a central role in the search for new physics in the era of large-scale sky surveys.