Refining Searches for Physics Beyond the Standard Model

该奖项资助密歇根州立大学Kirtimaan Mohan教授的研究活动。世界各地的一些实验，如瑞士日内瓦欧洲核子研究中心实验室的大型强子对撞机，正在收集大量数据，以研究物质的组成部分——即夸克和轻子等基本粒子——是如何相互作用和行为的。我们目前对这些基本粒子及其相互作用的理解是有缺陷的，需要新的物理定律来解释一些观察到的现象，比如暗物质的存在。为了检验和发现这些新的物理定律，理论和实验都需要更高的精确度。Mohan教授的研究将有助于实现现代工具和方法，通过使用现代机器学习方法，改进理论计算和设计新的搜索方法来改进我们对新物理定律的搜索。这项研究符合并支持美国国家科学基金会促进基础科学进步的使命。预计该项目还将产生更广泛的重大影响。Mohan教授将让研究生和博士后参与他的研究，从而为刚开始研究该领域的初级物理学家提供关键的培训。此外，Mohan教授将参与推广活动，并利用基于研究的教学实践来帮助改善物理学的本科教育。更具体地说，寻找新物理需要更精确的理论计算和预测，以及探索和测试不同新物理场景现象学的新方法。莫汉教授的研究将涉及所有这些方面。例如，Mohan教授将使用现代机器学习技术来提供对Parton分布函数中不确定性的更准确估计。此外，Mohan教授将为新的物理场景探索新的想法和信号，并在新的物理模型中改进计算，为这些模型提供更准确的理论预测。Mohan教授还将参与更广泛的影响活动，包括为更大的物理社区服务，以促进多样性为明确目标的指导，参与外展活动，以及使用循证实践改善本科教育和课程。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award funds the research activities of Professor Kirtimaan Mohan at Michigan State University.Several experiments around the world, such as the Large Hadron Collider at the CERN Laboratory in Geneva, Switzerland, are collecting large volumes of data to study how the building blocks of matter --- namely fundamental particles such as quarks and leptons --- interact and behave. Our current understanding of these fundamental particles and their interactions is deficient and new physical laws are needed in order to explain several observed phenomena, such as the existence of dark matter. Greater precision from both theory and experiment is necessary in order to test and discover such new physical laws. Professor Mohan’s research will help implement modern tools and methods to improve our search for new physics laws by using modern machine learning methods, refining theoretical calculations, and designing novel search methods. This research aligns with and supports the mission of the National Science Foundation to promote the progress of basic science. This project is also envisioned to have significant broader impacts. Professor Mohan will involve graduate students and postdocs in his research, and thereby provide critical training for junior physicists beginning research in this field. Additionally, Professor Mohan will engage in outreach activities and help improve undergraduate education in physics using research based pedagogical practices.More specifically, the search for new physics requires more precise theoretical calculations and predictions as well as novel ways to explore and test the phenomenology of different new physics scenarios. Professor Mohan’s research will touch upon all of these aspects. For example, Professor Mohan will use modern machine learning techniques to provide a more accurate estimate of uncertainties in Parton Distribution Functions. Additionally, Professor Mohan will explore novel ideas and signals for new physics scenarios as well as refine calculations in new physics models to provide more accurate theoretical predictions of such models. Professor Mohan will also engage in broader-impact activities which involve service to the larger physics community, mentoring with an explicit goal of promoting diversity, engaging in outreach activities, and improving undergraduate education and curriculum using evidence-based practices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.