ИСТИНА

The next generation of large scale sky surveys will certainly detect a variety of new astrophysical sources. However, since every photometrically observed candidate requires further investigation via spectroscopy, the development of automated anomaly detection algorithms with low incidence of false positives is crucial. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Our method follows an active learning strategy where the learning algorithm chooses objects which 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 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 at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the PLAsTiCC challenge and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static Isolation Forest. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. We show that, in the real data scenario, AAD was able to identify ~80% more true anomalies than the Isolation Forest. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.