Overview

Proposed by: Ashish Mahabal, Ashley Villar, Federica Bianco

Optical astronomy has become increasingly data rich, and LSST/Rubin will take it to the next level by going much deeper than any current large area survey. Many methods are being planned to use data-driven ways to classify objects, be they in the Solar System, or variable stars in our Galaxy, or extra-galactic objects. While current surveys and experience over a few decades has prepared us for well understood classes, data covering wider parameter spaces are likely to harbor classes and phenomena not encountered before.

In the Solar System, for instance, there could be retrograde asteroids, or those with orbits unusually inclined, or with not-seen-before compositions. In the Galaxy there could be stars that show mixed types of variability or anomalous compositions (for instance like the H-He Janus star seen recently). Deeper images could reveal earlytime supernova pre-flashes. These are just a few examples. In addition there would be in-class extreme outliers, and totally new phenomena. Identifying all kinds of anomalies is crucial to advance our understanding of the cosmos.

The most exciting anomalies would be those that reveal our biases and selection effects, and lead us to entire populations unkown so far. We are also prepared for finding a lot of artifacts in the process. A bit like bugs, these could reveal shortcomings in the processing pipelines, and an opportunity for improvement.

The LSST/Rubin collaboration has wide expertise in running a variety of algorithms to look for anomalies using supervised and unsupervised datasets. We propose forming an interest group to focus such efforts within ISSC and work with various science collaborations to develop methodologies that will be useful across the board, and avoid duplication.

We will have members summarize data types, datasets (including external datasets worth fusing with LSST data), discuss methodologies, and develop solutions. We will also encourage Science Collaborations to bring to ISSC problems related to anomaly detection that they may not be able to handle. One recent problem that came to our attention was audio data from the testing of the camera and filters at SLAC. Anomalies in the audio signals could reveal potential problems the project may want to be aware of.

Leadership

Co-Conveners

Projects

ELAsTiCC AD challenge

Stamp AD