Kounkel, Marina.; Sizemore, Logan.; Shen, Hidemi Mitani.; Chandler, Nicholas.; Reneau, Noah.; Pourlotfali, Ian.; Payton, Ronald L.; Hutchinson, Brian.; Medan, Ilija.; Stassun, Keivan. (2026).听.听Astronomical Journal, 171(5).听
Eclipsing binaries are pairs of stars that pass in front of each other from our point of view, and they are one of the best ways to measure basic stellar properties such as mass and radius. The challenge is that working out these properties has usually taken a lot of time and computing power, so only a small number of systems have been fully analyzed. To speed this up, the authors created a neural network, a type of artificial intelligence that learns patterns from data, which can use light curves from many common filters, radial velocity measurements for both stars, and information about the stars鈥� brightness across the spectrum to estimate the stars鈥� and orbit鈥檚 properties. The model was designed to handle messy real-world data, including extra light from nearby stars, starspots, and missing measurements, and it can also report uncertainty in each prediction. After training on simulated data, the researchers tested it on about 200 eclipsing binaries that had already been studied in detail. The model could estimate masses and radii to within about 20% and surface temperature to within about 500 K, and it did so much faster than traditional methods. Although it is not as precise as a detailed star-by-star analysis, it is well suited to the huge surveys now producing thousands of eclipsing binaries, helping researchers quickly find the most interesting systems for deeper study.
Figure 1.聽Distribution of the parameter space covered by the synthetic EBs.