Finished! Looks like this project is out of data at the moment!
Wow! We've run out of subjects to classify through the desktop interface! Huge thanks from the Euclid: Challenge the Machines team! If you're a fan of the Zooniverse Mobile App, then the good news is that Euclid: Challenge the Machines for mobile is now up and running and we'd really appreciate your help.
The Team
Andrew Davies
 | I am a third-year PhD student at the Open University. I am interested in the use of machine learning techniques, such as Neural Networks, for identifying images containing Strong Gravitational Lenses. As well as identifying the images, I am working on automated methods to "deblend" the images into foreground lens galaxy and background source galaxy in order to model the lens galaxy and better understand the strong gravitational lensing system. |
Stephen Serjeant
 | Stephen Serjeant is a professor of astronomy at the Open University in the UK, who describes himself as a "recovering blob counter". He has spent most of the last two decades working in infrared astronomy, in which distant starbursting galaxies appear as blobs in infrared images. His team discovered a wonderful new way of finding gravitational lenses with infrared data from the Herschel Space Observatory: scan the sky, and pick out the brightest blobs, which turn out to be bright because of lensing magnification. The Euclid space telescope promises to do much better though, and to Euclid the galaxies will no longer appear as blobs. He is the vice-president of the Society for Popular Astronomy, and fell in love with astronomy aged about five, when he would not eat baked beans without counting them first. You can follow Stephen on Twitter at @stephenserjeant. |
Jane Bromley
 | I am an academic at the Open University teaching computer science. My research is at the interface of AI and psychology – for example how do we learn and how can we make machines that learn. I am hoping that Euclid – Challenge the Machines will help to further our understanding of how humans do image classification tasks like spotting lensed galaxies and how we can make machines to do the same task. |
Hugh Dickinson
 | I work as a postdoctoral researcher at the University of Minnesota in Minneapolis. I'm interested in the numerous ways that human beings and computers can collaborate to solve scientific problems that each group finds challenging in different ways. Computers are very good a processing large volumes of data very fast, and modern machine learning algorithms are improving all the time. However, these algorithms need train using many thousands of example problems before they are able to perform a single analytical task. That means that when they tackle a problem that their programmers didn't anticipate, they often fail. On the other hand human beings are amazing generalists and can often learn to solve a problem with just a few examples. In the future, we hope to use the classifications provided by the amazing Zooniverse volunteers to help train new machine learning algorithms. These algorithms will be needed to handle the huge volumes of data that forthcoming telescopes like Euclid will deliver. |
Lucy Fortson
 | Lucy is an Astrophysicist working at the University of Minnesota. She is interested in galaxy evolution, black holes and the jets of material beaming from the centers of active galactic nuclei. She started the Zooniverse effort at the Adler Planetarium, and is now bringing the light to the University of Minnesota. On the odd weekend, when she's not preparing lecture or writing grants, Lucy can be found hanging out with her husband and son at one of Minneapolis' fine dining establishments. |
