ATLAS Bridging Application: Di-Higgs Processes as a Window to the Standard Model and Beyond

ATLAS 桥接应用：Di-Higgs 过程作为标准模型及其他模型的窗口

• 批准号: SAPPJ-2020-00032
• 负责人: Swiatlowski, Maximilian
• 金额: $ 7.29万
• 依托单位: TRIUMF
• 依托单位国家: 加拿大
• 项目类别: Subatomic Physics Envelope - Project
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718494
• 关键词: ATLAS; Bridging; Application; Di; Higgs

The Large Hadron Collider (LHC) collides protons at the highest energy conditions of any facility in the world, recreating conditions a fraction of a second after the Big Bang. We record the outcomes of these collisions with the ATLAS detector, and we use this data to understand the fundamental building blocks of the universe and the forces which bind them together. In 2012 ATLAS (and another experiment, CMS) discovered a new particle, the Higgs boson. This particle is thought to play a critical role in our understanding of the universe by providing other particles their masses. While this discovery of the Higgs Boson resolved one set of questions, a whole host of new ones has emerged. The most pressing is about the nature of the Higgs mechanism, the process which provides the other particles their masses. My research program addresses this question by searching for collisions producing not just one Higgs boson, but two. These events are extremely rare: only roughly one in a trillion collisions produces two Higgs bosons at once. We use advanced data analysis techniques to pick out this delicate needle from the haystack, but the challenge is worthwhile: these events can hold clues to the shape of the Higgs energy potential and the fundamental mechanism that provides particles their masses. Additionally, the conditions just moments after the Big Bang could be imprinted onto this same Higgs energy potential: if we are able to measure this, we could see a relic of the universe's birth. Discovering these double Higgs events could revolutionize our understanding of the universe, allowing us to see not just how the universe works but how it evolved to its current state, explaining the why we are made of only matter and not anti-matter. I intend to place Canadian scientists at the forefront of the global effort to discover these processes and understand their consequences. A second key component of my research program is the full utilization of information that our detectors provide. Since the Higgs boson is not stable, observing pairs of Higgs bosons requires that we accurately measure particles they decay into. The most common decay is into b-quarks, which themselves form complicated sprays of particles called jets which we can measure with ATLAS's inner detector and calorimeter systems. I intend to use deep learning techniques, inspired by developments in image recognition technology, to revolutionize how we measure the energy of the particles that make up jets. In the same way that machine learning has revolutionized photography through the combination of several images at once, we can combine low level information from the detector and algorithms designed and tuned by physicists to significantly sharpen our understanding of the collisions we are observing. By improving the quality of the data we are taking, we can make the most of the significant Canadian investment in ATLAS's upgrades and accelerate our road to discoveries.

大型强子对撞机 (LHC) 在世界上任何设施中最高能量条件下对质子进行碰撞，重现大爆炸后几分之一秒的条件。我们用 ATLAS 探测器记录这些碰撞的结果，并利用这些数据来了解宇宙的基本组成部分以及将它们结合在一起的力。 2012 年，ATLAS（以及另一个实验 CMS）发现了一种新粒子：希格斯玻色子。这种粒子被认为通过为其他粒子提供质量而在我们理解宇宙中发挥着关键作用。 虽然希格斯玻色子的发现解决了一系列问题，但一系列新问题也出现了。最紧迫的是希格斯机制的本质，即为其他粒子提供质量的过程。我的研究计划通过寻找不仅产生一个希格斯玻色子而是产生两个希格斯玻色子的碰撞来解决这个问题。这些事件极其罕见：只有大约万亿分之一的碰撞会同时产生两个希格斯玻色子。我们使用先进的数据分析技术从大海捞针中挑出这根精致的针，但挑战是值得的：这些事件可以为希格斯能量势的形状和提供粒子质量的基本机制提供线索。此外，大爆炸后不久的条件可能会被印在同样的希格斯能量势上：如果我们能够测量它，我们就可以看到宇宙诞生的遗迹。发现这些双希格斯事件可能会彻底改变我们对宇宙的理解，让我们不仅能够看到宇宙是如何运作的，还能看到它是如何演化到目前的状态，解释为什么我们只由物质而不是反物质组成。我打算让加拿大科学家站在全球努力的最前沿，以发现这些过程并了解其后果。 我的研究计划的第二个关键组成部分是充分利用我们的探测器提供的信息。由于希格斯玻色子不稳定，观察成对的希格斯玻色子需要我们准确测量它们衰变成的粒子。最常见的衰变是 b 夸克，它们本身会形成复杂的粒子喷雾，称为喷流，我们可以使用 ATLAS 的内部探测器和量热仪系统来测量它们。受图像识别技术发展的启发，我打算使用深度学习技术来彻底改变我们测量构成射流的粒子能量的方式。就像机器学习通过同时组合多个图像彻底改变了摄影一样，我们可以将探测器的低级信息与物理学家设计和调整的算法结合起来，以显着加深我们对所观察到的碰撞的理解。通过提高我们所获取数据的质量，我们可以充分利用加拿大对 ATLAS 升级的重大投资，并加快我们的发现之路。