Inspired by movie streaming services such as Netflix or Hulu, a researcher from the Southwest Research Institute developed a technology for looking for stars that will likely host giant Jupiter planets outside our solar system. She developed an algorithm for identifying stars that can host giant exoplanets, based on the composition of stars known to have planets.
"My viewing habits have trained Netflix to recommend sci-fi movies that I might like ̵
"We found that the most influential elements in predicting the planet's host stars are coal, oxygen, iron and sodium," Hinkel said. "The funny thing was that we didn't expect sodium to be an important ingredient for predicting a planet. But it must be an important link between stars and planets, because it continued to appear, even when looking at different combinations of element."  Hinkel used Hypatia Catalog, a publicly available star database that she developed to educate and test the algorithm. It is the largest database of stars and their elements for the population within 500 light years of our Sun. In the case of distress, Hypatia had stellar element data for 6,193 stars, of which 401 are known to host planets. The database also catalogs 73 stellar elements from hydrogen to lead.
The algorithm, which will be available to the public, has looked at more than 4,200 stars and assessed their likelihood of hosting planets based solely on the elements or ingredients within the star. In addition, Hinkel saw various combinations of these ingredients to see how they affected the algorithm.
Hinkels team identified around 360 potential giant planet's host stars that have more than 90 percent probability of hosting a giant exoplanet. "We were happy, so we used archive telescope data to search for some signs of planets around these likely host stars," Hinkel said. "We identified possible planets of Jupiter size around three stars predicted by the algorithm."
When asked about how reliable her algorithm is, she explained that "we have no true negatives in our data, that is, stars we know do not have planets – so we hid some known planet-host stars in data to see what their prediction points would be. On average, they made more than 75 percent, which is good! It's probably a higher average than I like the sci-fi movies Netflix chooses for me. "
Moving forward, these results can revolutionize target star selection future research and clear role elements play in the giant planet's detection and formation. Hinkel is the leading author of the paper "A recommendation algorithm for predicting giant exoplanet host stars using stellar elemental abundance" which will be published in a forthcoming issue of The Astrophysical Journal .
Researchers develop database for stellar-exoplanet "exploration"
Natalie R. Hinkel, et al. A recommendation algorithm for predicting giant exoplanet value stars using Stellar Elemental Abundances. arXiv: 1805.12144v2 [astro-ph.EP]: arxiv.org/abs/1805.12144
Researchers develop new algorithm to help the search for exoplanets (2019, June 25)
June 25, 2019
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