Researchers are recruiting global volunteers through a citizen science platform to analyze space telescope data, combining machine learning with human classification to hunt for tens of millions of hidden stellar-mass black holes across the Milky Way.
Astronomers have confirmed only about 50 to 70 of these elusive objects while estimating the Milky Way conceals dozens or even hundreds of millions of stellar-mass black holes. Because black holes do not emit direct light and trap any illumination beyond their event horizons, finding them requires indirect detection methods. A team of researchers from the United Kingdom is tapping into public participation to bridge that gap through the Black Hole Hunters initiative.
We're back! 🎉 We have new data, a (slightly) different name, and more chance than ever of finding the elusive, hidden black holes that we're looking for. Find out more here https://t.co/GOh5OkXKeR
TESS Monitors Stars to Find Gravitational Microlensing
The project uses observations gathered by NASA’s Transiting Exoplanet Survey Satellite (TESS), a spacecraft originally designed to find planets outside our solar system. TESS monitors the changing brightness of hundreds of thousands of stars. It generates massive quantities of light curves that can reveal the subtle optical distortions caused by gravity.
A heavy object like a black hole can pass directly between a distant star and Earth. Its massive gravitational field warps and magnifies the starlight in a phenomenon known as gravitational microlensing.
When another object passes, it warps the starlight. The project focuses particularly on binary systems where a star orbits a black hole. If the orbit aligns favorably, the invisible companion periodically crosses in front of the star, creating a predictable increase in brightness.
Human Volunteers Help Machine Learning Sift Stellar Records
Researchers employ machine learning algorithms to perform an initial pre-selection of interesting light curves to sift through millions of stellar records. While the software can process massive datasets in days rather than years, automated routines frequently stumble over natural noise, stellar flares, star spots, and instrumental glitches.
That is where human eyes become indispensable. Hosted on the Zooniverse citizen science platform, the project requires no specialized astronomy degree. Participants examine graphic patterns of stellar brightness after reviewing introductory training materials. Each data point is evaluated by multiple people to minimize interpretation errors before professional astronomers conduct follow-up observations.
Project Expands Search Using Upcoming Space Missions
Nearly 23,000 volunteers have contributed over 12 million classifications, according to project figures. The initiative launched in October 2021 using data from the SuperWASP terrestrial survey that monitored stars between 2004 and 2016, and the project has since expanded its scope.
Organizers plan to widen the search further while results from the SuperWASP dataset undergo analysis. Researchers intend to incorporate data from upcoming missions including the European Space Agency’s Euclid mission and NASA’s Nancy Grace Roman Space Telescope to cross-reference multiple information sets.
The Hunt for "Missing" Black Holes in the Milky Way 🕵️♂️🌌Over the past 13 billion years, more than 10 million black holes may have formed in our galaxy, yet only about 70 have been confirmed. British astronomers are now calling for volunteers to help in the search.The "Black… pic.twitter.com/hcav9ysol4