[HN Gopher] New planets confirmed in machine learning first ___________________________________________________________________ New planets confirmed in machine learning first Author : dnetesn Score : 21 points Date : 2020-08-26 10:55 UTC (12 hours ago) (HTM) web link (phys.org) (TXT) w3m dump (phys.org) | antognini wrote: | To be clear, there have been prior studies which have discovered | exoplanets with machine learning. One example (cited in this | paper) was from Chris Shallue at Google and Andrew Vanderberg at | CfA, who discovered the first eight planet system in 2017 by | training convolutional neural networks on light curve data. [1] | | The authors explain their contribution to be: | | > To date these [previous studies] have all focused on | identifying [false positives] or ranking candidates within a | survey. We build on past work by focusing on separating true | planets from [false positives], rather than just planetary | candidates, and in doing so probabilistically to allow planet | validation. | | To translate, these previous studies focused on identifying | exoplanet _candidates_. In the early days of exoplanet research, | these candidates were then followed up with more detailed | observations to confirm that the candidate was a true planet. | | With the advent of Kepler, there are now way too many candidates | to get follow-up observations on all of them. So now exoplanets | are "validated" by calculating the probability of a false | positive. I am not in this field, but it seems that the most | common algorithm to do this is called VESPA [2]. The contribution | these authors make is to use a machine learning system for the | _validation_ process, i.e., calculating the probability that the | candidate is a false positive. | | [1]: https://arxiv.org/abs/1712.05044 | | [2]: https://github.com/timothydmorton/VESPA | arkanciscan wrote: | *exoplanets | dylan604 wrote: | I was hoping for Planet 9, 10 & 11. ___________________________________________________________________ (page generated 2020-08-26 23:01 UTC)