Galaxy Zoo : Weird and Wonderful

For the last fifteen years, Galaxy Zoo volunteers have helped sort through millions of images of distant galaxies. The shape and color of these systems reveals their history, telling us how they've interacted with their surroundings, and how and when they've formed stars.

Along the way, Galaxy Zoo volunteers found some of the most unusual and fascinating objects in the Universe, from Hanny's Voorwerp - a gas cloud the size of a galaxy heated by activity associated with a neighbouring black hole - to a set of small round green galaxies known as the Green Peas.

In this new project, we want to focus on searching for the truly unusual. Using new images taken with the Hyper-Suprime Cam (HSC) Instrument on the Japanese Subaru telescope as a part of the HSC survey, we can see fainter details in images of the sky than has ever been possible on this scale. All you have to do is take a tour - and tell us when you find something interesting.

What's interesting?

It may seem odd that we don't tell you what we want you to look for. This project aims to make truly serendipitous discoveries - to find things in the datasets that even the astronomers who built and operated the survey didn't know to look for.

We're currently experimenting with different ways to explore the data. It's possible that once we have a better idea of what's hidden amongst the millions of everyday galaxies we can provide more guidance, but for now, we're interested in what you find. Relax, and click on anything you think is unusual and interesting!

If you're new to Galaxy Zoo in general, we encourage you to explore its classic workflow and classify some 30 galaxies, so that you can get a sense of how run-of-the-mill galaxies appear.

Anomaly detection and serendipitous detection

The primary goal of this project is to find rare and interesting galaxies, but we're also interested in understanding how to search for the unexpected in general. Artificial Intelligence (AI) is being used actively to help identify such anomalous systems among large data sets, however, they lag behind humans in their ability to discover rare and scientifically interesting phenomenon. As such, there is a strong need to "bridge the knowledge gap" between what AI identifies as anomalous and what humans consider interesting, and meld the human and machine strengths towards building an efficient discovery framework. This is especially critical in the current era of big data astrophysics and upcoming large-scale sky surveys such as the Vera Rubin Observatory's LSST. In running projects like this one, we hope we can learn things which will inspire future Zooniverse projects in fields far from astrophysics.