Summary
Researchers at Columbia University, led by senior applied math major Chris Lim and Professor Kipping, explore the potential of artificial neural networks to predict the existence of planets in extrasolar systems before they are detected. Their study focuses on utilizing these networks to analyze data from the upcoming TESS space mission, which aims to discover thousands of new planets but will primarily identify those with short orbital periods.
By training their neural network on known planets from the Kepler mission, the team seeks to identify which systems may harbor additional planets beyond TESS's observational capabilities. The neural network is designed to analyze various features of these planetary systems, such as the number of planets and their sizes, to make predictions about potential outer companions. Initial results indicate that the neural network performs well in predicting these systems, suggesting a more efficient approach to targeting telescopes for follow-up observations.
The implications of this research extend to the future of artificial intelligence in astronomy, with the potential to streamline resource allocation and enhance the discovery of new celestial bodies. However, the researchers acknowledge the risks of relying solely on machine learning, including the possibility of overlooking anomalous systems that do not fit established patterns.
Show Notes
Exciting new research from the Cool Worlds Lab has delved into the use of machine learning and artificial neural networks for astronomy. In new work from David Kipping & Chris Lam here at Columbia, we've shown how a machine can predict the presence of extra planets in known planetary systems using just a few pieces of information about the system. Chris Lam gives a neural network 101 and explains our implementation works.::More about this Video::► Kipping & Lam 2016, "Transit Clairvoyance: Enhancing TESS follow-up using artificial neural networks": https://arxiv.org/abs/1611.04904► Tamayo et al. (2016), "A Machine Learns to Predict the Stability of Tightly Packed Planetary Systems": https://arxiv.org/abs/1610.05359► Graff et al. (2013), "SKYNET: an efficient and robust neural network training tool for machine learning in astronomy": https://arxiv.org/abs/1309.0790► Waldmann (2016), "Dreaming of atmospheres": https://arxiv.org/abs/1511.08339► Outro music by Taylor Davis: https://www.youtube.com/watch?v=dl9kI1yQKZk::Playlists For Channel::Latest Cool Worlds Videos ► http://bit.ly/NewCoolWorldsCool Worlds Research ► http://bit.ly/CoolWorldsResearchGuest Videos ► http://bit.ly/CoolWorldsGuestsQ&A Videos ►http://bit.ly/CoolWorldsQAScience of TV/Film ► http://bit.ly/ScienceMovies::Follow us::SUBSCRIBE to the channel http://bit.ly/CoolWorldsSubscribeCool Worlds Lab http://coolworlds.astro.columbia.eduTwitter https://twitter.com/david_kippingInstagram https://www.instagram.com/cool.worldsTHANKS FOR WATCHING!! Learn more about your ad choices. Visit megaphone.fm/adchoices
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