<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Instrumental Noise | A Quest After Perspectives</title><link>https://iphysresearch.github.io/blog/tag/instrumental-noise/</link><atom:link href="https://iphysresearch.github.io/blog/tag/instrumental-noise/index.xml" rel="self" type="application/rss+xml"/><description>Instrumental Noise</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Tue, 29 Sep 2026 00:00:00 +0000</lastBuildDate><image><url>https://iphysresearch.github.io/blog/media/sharing.png</url><title>Instrumental Noise</title><link>https://iphysresearch.github.io/blog/tag/instrumental-noise/</link></image><item><title>Learning Instrumental Noise Profiles in Signal-Confusion Scenarios for Space-Based Gravitational-Wave Detectors</title><link>https://iphysresearch.github.io/blog/mypublication/2026_noise_profiles/</link><pubDate>Tue, 29 Sep 2026 00:00:00 +0000</pubDate><guid>https://iphysresearch.github.io/blog/mypublication/2026_noise_profiles/</guid><description>&lt;p>The analysis links simulated foreground subtraction to Bayesian noise inference rather than assuming a signal-free interval. Smooth residual-model corrections account for foreground mismatch in the spectral likelihood.&lt;/p>
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