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Machine Learning for the Search for Extraterrestrial Intelligence Hackathon & Code Challenge

Machine Learning for the Search for Extraterrestrial Intelligence Hackathon & Code Challenge

Illustration of numbers over ATA

The SETI Institute is inviting all citizen data scientists and technologists to join us as collaborators in our mission to find intelligent radio signals from beyond our solar system. We are issuing a worldwide, public code challenge for the purpose of expanding our radio-telescope signal classification tools using the latest developments available in machine- and deep-learning.

The Mission

We are looking for signal classification algorithms and models that can accurately identify the various types of radio signals we observe each night. We have constructed a set of simulated signals (thus, they are a labeled training data set) that mimic our observations.

By framing the radio signal data into spectrogram (a 2D visual representation), we can convert the problem into something akin to an image classification problem. We are looking for participants to build machine learning and deep learning techniques to construct highly accurate classification systems that will be used in our data analysis pipeline at our telescope array.

Code Challenge

A hackathon was held June 10 -11 kicked off a two-month long online code challenge, ending July 31. The goals of the hackathon and code challenge are the same -- build the best signal classifier using our simulated data set. The two-month code challenge allows you and your team to refine your algorithms and models to their best.

Awards and Prizes

Code Challenge

The algorithm developed by the winning team at the end of the code challenge will be installed as part of the real time data anlaysis pipeline at the Allen Telescope Array! Additionally, the winning team will get 

  • Co-authorship with SETI Institute researchers on a paper to be submitted in a peer-reviewed scientific journal
  • Assistance presenting their work at a SETI conference or meetup (assistance with slides, abstract submission or introduction to meetup organizers)

Judging will be an objective measure of the algorithm/model's classification accuracy and speed of classifying single events. These exact details will be defined before the beginning of the challenge.


Join our Slack Team in order to communicate with us and other participants: We'll be using Slack to help you to self-organize into teams and discuss everything from logistics to data analysis details.

We are really excited and looking forward to working with you all!

Academic Participants:

  • University of California, Berkeley


  • IBM
  • Galvanize
  • SETI League
  • Nimbix
  • Skymind

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