<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Galaxies: Bar on Mi Chen</title><link>https://chenmi0619.github.io/tags/galaxies-bar/</link><description>Recent content in Galaxies: Bar on Mi Chen</description><generator>Hugo -- 0.147.2</generator><language>en</language><lastBuildDate>Thu, 23 Jan 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://chenmi0619.github.io/tags/galaxies-bar/index.xml" rel="self" type="application/rss+xml"/><item><title>From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption</title><link>https://chenmi0619.github.io/publications/gzdecals2bass_mzls/</link><pubDate>Thu, 23 Jan 2025 00:00:00 +0000</pubDate><guid>https://chenmi0619.github.io/publications/gzdecals2bass_mzls/</guid><description>This paper introduce an unsupervised domain adaptation (UDA) method that fine-tunes a source domain model trained on DECaLS images with GZD-5 labels to BMz images, aiming to reduce bias in galaxy morphology classification within the BMz survey. Published in Monthly Notices of the Royal Astronomical Society, 2025.</description></item><item><title>From images to features: unbiased morphology classification via variational auto-encoders and domain adaptation</title><link>https://chenmi0619.github.io/publications/images2features/</link><pubDate>Fri, 20 Oct 2023 00:00:00 +0000</pubDate><guid>https://chenmi0619.github.io/publications/images2features/</guid><description>This paper introduce a novel approach for the dimensionality reduction of galaxy images by leveraging a combination of variational auto-encoders (VAE) and domain adaptation (DA). Published in Monthly Notices of the Royal Astronomical Society, 2023.</description></item></channel></rss>