A Classifier-Based Approach to Multi-Class Anomaly Detection for
  Astronomical Transients
Rithwik Gupta Daniel Muthukrishna Michelle Lochner
Automating real-time anomaly detection is essential for identifying rare
transients in the era of large-scale astronomical surveys. Modern survey
telescopes are generating tens of thousands of alerts per night, and future
telescopes, such as the Vera C. Rubin Observatory, are projected to increase
this number dramatically. Currently, most anomaly detection algorithms for
astronomical transients rely either on hand-crafted features extracted from
light curves or on features generated through unsupervised representation
learning, which are then coupled with standard machine learning anomaly
detection algorithms. In this work, we introduce an alternative approach to
detecting anomalies: using the penultimate layer of a neural network classifier
as the latent space for anomaly detection. We then propose a novel method,
named Multi-Class Isolation Forests (MCIF), which trains separate isolation
forests for each class to derive an anomaly score for a light curve from the
latent space representation given by the classifier. This approach
significantly outperforms a standard isolation forest. We also use a simpler
input method for real-time transient classifiers which circumvents the need for
interpolation in light curves and helps the neural network model inter-passband
relationships and handle irregular sampling. Our anomaly detection pipeline
identifies rare classes including kilonovae, pair-instability supernovae, and
intermediate luminosity transients shortly after trigger on simulated Zwicky
Transient Facility light curves. Using a sample of our simulations that matched
the population of anomalies expected in nature (54 anomalies and 12,040 common
transients), our method was able to discover $41\pm3$ anomalies (~75% recall)
after following up the top 2000 (~15%) ranked transients. Our novel method
shows that classifiers can be effectively repurposed for real-time anomaly
detection.