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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Surveying the reach and maturity of machine learning and artificial intelligence in astronomy · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1912.02934/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1912.02934/feed.xml" rel="self" type="application/rss+xml" /><item><title>Surveying the reach and maturity of machine learning and artificial intelligence in astronomy — mentioned by karthikb</title><link>https://news.ycombinator.com/item?id=21867148</link><guid isPermaLink="false">urn:hackerpapers:mention:1912.02934:21867148</guid><pubDate>Mon, 23 Dec 2019 20:38:55 GMT</pubDate><description>&lt;p&gt;Take a look here: https://arxiv.org/search/?query=astronomy+machine+learning&amp;amp;s... Plenty of ML work being applied to astronomy. Much of the code is open source too so you can dive right in and contribute. Here&amp;#x27;s a recent survey you might start with: https://arxiv.org/pdf/1912.02934.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1912.02934/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>astro-ph.IM</category></item></channel></rss>
