Learning to Ignore Uncertainties with Adversaries at the LHC

我在ATLAS的项目重点是使用数据密集型方法改进b标记。这将涉及重新训练标准的基本b标记MVA算法，重点是弱学习和无监督学习。增强算法的结果将被传播，成为默认的ATLAS b标记工具，供所有分析使用。此外，我们将使用经过数据校正的模拟样本训练一个系统感知的人工神经网络。这将大大减少系统不确定性对系统有限H- b> bb分析的影响。该项目的最终目标是进行世界上最精确的H- b> bb测量。

My project at ATLAS is focused on improving b-tagging using data intensive methods. This will involve retraining the standard basic b-tagging MVA algorithms with a focus on both weak and unsupervised learning. The results from the enhanced algorithm will be propagated to become the default ATLAS b-tagging tool, used by all analyses. Additionally, we will train a systematics aware ANN using data-corrected simulated samples. This will significantly reduce the impact of systematic uncertainties on the systematically limited H->bb analysis. The final goal of the project is to perform the world's most precise H->bb measurement.