<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dark Matter | A Quest After Perspectives</title><link>https://iphysresearch.github.io/blog/tag/dark-matter/</link><atom:link href="https://iphysresearch.github.io/blog/tag/dark-matter/index.xml" rel="self" type="application/rss+xml"/><description>Dark Matter</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Tue, 27 May 2025 00:00:00 +0000</lastBuildDate><image><url>https://iphysresearch.github.io/blog/media/sharing.png</url><title>Dark Matter</title><link>https://iphysresearch.github.io/blog/tag/dark-matter/</link></image><item><title>Deep learning-driven likelihood-free parameter inference for 21-cm forest observations</title><link>https://iphysresearch.github.io/blog/mypublication/2025_21cm_forest/</link><pubDate>Tue, 27 May 2025 00:00:00 +0000</pubDate><guid>https://iphysresearch.github.io/blog/mypublication/2025_21cm_forest/</guid><description>&lt;p>Generative flows expand a limited simulation set, while inference flows estimate parameter posteriors from the forest power spectrum. The analysis uses simulated observations relevant to the Square Kilometre Array.&lt;/p>
&lt;h2 id="sources">Sources&lt;/h2>
&lt;ul>
&lt;li>&lt;a href="https://www.nature.com/articles/s42005-025-02139-5" target="_blank" rel="noopener">Publisher record&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://arxiv.org/abs/2407.14298" target="_blank" rel="noopener">arXiv abstract and submission history&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://api.crossref.org/works/10.1038/s42005-025-02139-5" target="_blank" rel="noopener">Publisher metadata via Crossref&lt;/a>&lt;/li>
&lt;/ul></description></item></channel></rss>