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
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