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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Parton distributions with scale uncertainties: a MonteCarlo sampling approach · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2207.07616/</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/2207.07616/feed.xml" rel="self" type="application/rss+xml" /><item><title>Parton distributions with scale uncertainties: a MonteCarlo sampling approach — mentioned by zeristor</title><link>https://news.ycombinator.com/item?id=36488236</link><guid isPermaLink="false">urn:hackerpapers:mention:2207.07616:36488236</guid><pubDate>Tue, 27 Jun 2023 02:25:49 GMT</pubDate><description>&lt;p&gt;I think this might be the paper this is based on: https://link.springer.com/article/10.1007/JHEP03(2023)148 “ Abstract: We present the MCscales approach for incorporating scale uncertainties in parton distribution functions (PDFs). The new methodology builds on the Monte Carlo sampling for propagating experimental uncertainties into the PDF space that underlies the NNPDF approach, but it extends it to the space of factorisation and renomalisation scales. A prior probability is assigned to each scale combinations set in the theoretical predictions used to obtain each PDF replica in the Monte…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2207.07616/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>hep-ph</category></item></channel></rss>
