Researchers from Yale University has identified 60 potential new hot Jupiters. Hot Jupiters found a class of gas giant planets located. The astronomy and second-year Ph.D. student Sarah Millholland recognized the planet candidates through a novel application of big data techniques, used a machine learning algorithm. A machine learning algorithm is a sophisticated program that can be trained to recognize patterns in data to identify the amplitude variations in detected light that consequence as an orbiting planet replicates heats of light from its host star. Laughlin said, “Sarah’s work has given us what amounts to a ‘class portrait’ of extrasolar planets at their most alien”. He added, “It’s amazing how the latest techniques in machine learning, compounded with high-performance computing, are allowing us to mine classic data sets for extraordinary discoveries”.
At a Kepler Science Conference at the NASA Ames Research Center in California Millholland recently presented the research. The researcher stated that it is difficult to distinguish the reflected light from stellar, but a big data method permitted them to pull out the faint signals. The method created thousands of the datasets and trained an algorithm to recognize the properties of the reflected light signals. Millholland said, “I’ve been told by members of the Kepler science team that a search for reflected star-shine was part of the early renditions of the Kepler pipeline”. He added, “They called it the Reflected Light Search, or RLS module, in this sense, we’re simply addressing one of the original intentions for the Kepler data”. The researcher stated that the reflected light control rich information about the planets’ atmospheres. The reflected light contains characteristics which involve atmospheric composition, wind patterns, cloud existence as well as day-night temperature contrasts.