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https://www.cbc.ca/news/science/partial-lunar-eclipse-nov-19-1.6252140

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Skygazers are in for a treat overnight between Thursday and Friday, when much of the planet will be able to see the longest partial lunar eclipse in almost 600 years.

The only hitch is that for much of Canada, the peak of the eclipse will take place in the middle of the night.

But if the weather co-operates, it might be worth braving the cold night air for a glimpse of the longest partial lunar eclipse since 1440, when Henry VI was the King of England and the Inca Empire was expanding.

nasa-world-map-lunar-eclipse-nov-19.jpg

3 am for us here in Winnipeg - weather permitting.

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https://www.jpl.nasa.gov/news/new-deep-learning-method-adds-301-planets-to-keplers-total-count

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Scientists recently added a whopping 301 newly validated exoplanets to the total exoplanet tally. The throng of planets is the latest to join the 4,569 already validated planets orbiting a multitude of distant stars. How did scientists discover such a huge number of planets, seemingly all at once? The answer lies with a new deep neural network called ExoMiner.

 

Deep neural networks are machine learning methods that automatically learn a task when provided with enough data. ExoMiner is a new deep neural network that leverages NASA’s Supercomputer, Pleiades, and can distinguish real exoplanets from different types of imposters, or “false positives.” Its design is inspired by various tests and properties human experts use to confirm new exoplanets. And it learns by using past confirmed exoplanets and false positive cases.

ExoMiner supplements people who are pros at combing through data and deciphering what is and isn't a planet. Specifically, data gathered by NASA's Kepler spacecraft and K2, its follow-on mission. For missions like Kepler, with thousands of stars in its field of view, each holding the possibility to host multiple potential exoplanets, it's a hugely time-consuming task to pore over massive datasets. ExoMiner solves this dilemma.

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