Orateur
Description
Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the astronomer is left to independently analyze the data in order to understand why a given event is tagged as an anomaly. Interpretable AI on the other hand provides clues to the analyst, often in the form of feature importance. However, generalist methods on the market tends to exhibit slow performances when applied to big data.
Here I’ll present how the Signatures method, a post-hoc method-specific scheme providing a surrogate anomaly model, can reach tractable performances when applied on the 720 million light-curves from ZTF DR23 and enable active learning for feature selection and unknown unknows searc, therefore improving the rate at which anomalies are found in the sample.