  Survey telescopes such as the Vera C. Rubin Observatory and the Square
Kilometre Array will discover billions of static and dynamic astronomical
sources. Properly mined, these enormous datasets will likely be wellsprings of
rare or unknown astrophysical phenomena. The challenge is that the datasets are
so large that most data will never be seen by human eyes; currently the most
robust instrument we have to detect relevant anomalies. Machine learning is a
useful tool for anomaly detection in this regime. However, it struggles to
distinguish between interesting anomalies and irrelevant data such as
instrumental artefacts or rare astronomical sources that are simply not of
interest to a particular scientist. Active learning combines the flexibility
and intuition of the human brain with the raw processing power of machine
learning. By strategically choosing specific objects for expert labelling, it
minimises the amount of data that scientists have to look through while
maximising potential scientific return. Here we introduce Astronomaly: a
general anomaly detection framework with a novel active learning approach
designed to provide personalised recommendations. Astronomaly can operate on
most types of astronomical data, including images, light curves and spectra. We
use the Galaxy Zoo dataset to demonstrate the effectiveness of Astronomaly, as
well as simulated data to thoroughly test our new active learning approach. We
find that for both datasets, Astronomaly roughly doubles the number of
interesting anomalies found in the first 100 objects viewed by the user.
Astronomaly is easily extendable to include new feature extraction techniques,
anomaly detection algorithms and even different active learning approaches. The
code is publicly available at https://github.com/MichelleLochner/astronomaly.
