Active Anomaly Detection for time-domain discoveries

Abstract

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 (IF) 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 IF. 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 IF. 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.

10 pages, 5 figures, updated to include PLAsTiCC results

References in corpus (14)

• The NumPy array: a structure for efficient numerical computation
• The Spectroscopic Diversity of Type Ia Supernovae
• Massive stars exploding in a He-rich circumstellar medium. I. Type Ibn (SN 2006jc-like) events
• Super-luminous supernovae at redshifts of 2.05 and 3.90
• The weirdest SDSS galaxies: results from an outlier detection algorithm
• Super Luminous Supernovae as standardizable candles and high redshift distance probes
• Anomaly detection in the Zwicky Transient Facility DR3
• Supervised detection of anomalous light-curves in massive astronomical catalogs
• Systematic Serendipity: A Test of Unsupervised Machine Learning as a Method for Anomaly Detection
• Space Telescope and Optical Reverberation Mapping Project. XII. Broad-Line Region Modeling of NGC 5548
• Progenitors of low-luminosity Type II-Plateau supernovae
• Incorporating Feedback into Tree-based Anomaly Detection
• Massive stars exploding in a He-rich circumstellar medium. VI. Observations of two distant Type Ibn supernova candidates discovered by La Silla-QUEST
• Defining Photometric Peculiar Type Ia Supernovae

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