<?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>ATLAS Experiment on Tuan Pham's homepage</title><link>https://tuanmp.github.io/tags/atlas-experiment/</link><description>Recent content in ATLAS Experiment on Tuan Pham's homepage</description><generator>Hugo -- 0.128.0</generator><language>en</language><lastBuildDate>Mon, 06 May 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://tuanmp.github.io/tags/atlas-experiment/index.xml" rel="self" type="application/rss+xml"/><item><title>Physics Performance of the ATLAS GNN4ITk Track Reconstruction Chain</title><link>https://tuanmp.github.io/papers/paper1/</link><pubDate>Mon, 06 May 2024 00:00:00 +0000</pubDate><guid>https://tuanmp.github.io/papers/paper1/</guid><description>This paper presents an algorithm based on Graph Neural Network for charged-particle track reconstruction in the ATLAS Inner Tracker. Using realistic simulation data, we demonstrate the performance of the algorithm in comparison with the state-of-the-art technique.</description></item></channel></rss>