“Citizen scientists have successfully located thousands of previously unknown pairs of ‘eclipsing binary’ stars,” reports the Washington Post, citing a recent announcement from NASA.
The ongoing initiative helps space researchers hunt for “eclipsing binary” stars, a rare phenomenon in which two stars orbit one another, periodically blocking each other’s light. These star pairs offer important data to astrophysicists, who consider the many measurable properties of eclipsing binaries — and the information they bear about the history of star formation and destruction — as a foundation of the field…
The citizen science project in question, the Eclipsing Binary Patrol, validates images from NASA’s Transiting Exoplanet Survey Satellite (TESS) mission. The satellite, launched in 2018, is “exceptionally capable at detecting varying stars,” the researchers write in a preprint paper describing the initiative. The researchers used machine learning to identify about 1.2 million potential eclipsing star pairs. Citizen scientists then validated a subset of about 60,000… manually inspecting hundreds of thousands of images of eclipse-like events and weeding out actual binaries from images that tricked the algorithm. “Thankfully,” the researchers write, “to the rescue come volunteers from all walks of life that boost the capacity of bandwidth-limited professional astronomers many-fold and help tackle the ever-increasing volume of publicly available astronomical data.”
Universe Today describes how they limited the dataset to only stars with a magnitude brighter than 15, then used a Python tool to generate a massive dataset of millions of light curves…
The outcome of all the work resulted in the identification of 10,001 eclipsing binary systems. 7,936 of them are new to science, while the other 2,065 were previously known, but the study provided updated, more accurate, parameters for their periods, as TESS’ dataset provided better insight. There were also some particularly interesting systems that could hold new discoveries, including several that had variable eclipse timings, and plenty that might have a third star, and some that show a significant dynamic between the star being orbited and the one doing the orbiting.
All of those systems await further research, but there’s another, unspoken factor at play in this data — exoplanets. TESS was originally designed as an exoplanet hunter, and this kind of large scale AI/human collaboration of lightcurve analysis is exactly the kind of work that could potentially produce even more accurate exoplanet catalogues, as evidenced by some of the work already done in this paper. That seems to be the next step for this dataset, with Dr. Kostov telling an interviewer “I can’t wait to search them for exoplanets!” Given the data has already been collected, and the team has already been assembled, it’s very likely he’ll get his chance soon.
About The Author
