Conference: EPSCoR Workshop on Machine Learning for Analysis of High-Energy Cosmic Particles

该项目将支持一个关于机器学习在高能宇宙粒子分析中的应用的讲习班。研讨会将由特拉华大学主办，将在主题之间架起桥梁：“宇宙之窗”和“驾驭数据革命”。宇宙中尚不为人所知的自然物体将粒子加速到远远超出人类技术能力的能量。粒子天体物理学的一个主要目标是定位和理解这些宇宙粒子的来源。为了实现这一目标，已经部署了专门的天体粒子观测站来收集各种数据。这一领域的科学进步在很大程度上取决于数据分析的质量，这得益于包括人工神经网络在内的机器学习方面的最新进展。除了交流高能粒子天体物理学机器学习的最新成果和想法外，讲习班还将加强该领域的科学能力，并服务于培训未来熟练应用机器学习技术的劳动力的目的。将特别关注公平、多样性和包容性，采用混合形式实现广泛参与，包括面对面和远程与会者。为了最大限度地扩大影响，研讨会将有来自几个EPSCoR国家的联合组织者和参与者参加。组织者将主办一个类似会议的混合研讨会，重点是机器学习在分析宇宙粒子方面的应用，包括宇宙射线、高能伽马射线和超高能中微子--多信使天文学的组成部分。EPSCoR辖区的机构已经开始参与这一努力，并预计未来将在NSF实验中发挥重要作用，例如南极的冰立方。认识到面对面会议对有效讨论和建立网络的价值，这一混合研讨会将于2025年初将约50名面对面参与者带到特拉华大学，为期一周，同时还将促进更多在线参与者的参与。研讨会的组织建立在通过“NSF冰立方EPSCoR倡议：冰立方与数据革命”建立的成功的多机构合作的基础上，并将进一步鼓励EPSCoR机构参与这一研究领域。学术价值将通过研讨会期间的科学交流来传递，从长远来看，将通过以可引用的方式在开放获取平台上发布研讨会产品来实现。研讨会以各种方式支持国家科学基金会的EPSCoR任务，将通过促进科学知识的交流和促进参与者之间的新联系，产生更广泛的影响并刺激研究倡议。通过为EPSCoR辖区的学生和STEM中代表性不足的群体提供支持，研讨会将在准备下一代STEM工作人员的同时促进平等、多样性和包容性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will support a workshop on the application of Machine Learning for the Analysis of High-Energy Cosmic Particles. The workshop will be hosted by the University of Delaware and will bridge the themes: "Windows of the Universe" and "Harnessing the Data Revolution." Natural objects in the Universe, which are yet unknown, accelerate particles to energies far beyond human technological capabilities. A primary objective of particle astrophysics is to locate and comprehend the sources of these cosmic particles. To achieve this, specialized astroparticle observatories have been deployed to collect a diverse range of data. The advancement of science in this field hinges significantly on the quality of data analysis, benefiting from recent progress in machine learning, including artificial neural networks. Beyond the exchange of recent results and ideas in machine learning for high-energy particle astrophysics, the workshop will enhance scientific capabilities in this domain and serve the purpose of training a future workforce proficient in applying machine learning techniques. Special attention will be given to equity, diversity, and inclusion, with a hybrid format enabling broad participation, including both in-person and remote attendees. To maximize its impact, the workshop will involve co-organizers and participants from several EPSCoR states.The organizers will host a hybrid workshop with a conference-like format focusing on the application of machine learning for analyzing cosmic particles, including cosmic rays, high-energy gamma rays, and ultra-high-energy neutrinos—integral components of multi-messenger astronomy. Institutions in EPSCoR jurisdictions have begun to engage in this endeavor and anticipate a significant future role in NSF experiments, such as IceCube at the South Pole. Recognizing the value of in-person meetings for effective discussions and networking, this hybrid workshop will bring approximately 50 in-person participants to the University of Delaware for one week in early 2025, while also facilitating participation from additional online attendees. The organization of the workshop builds upon the successful multi-institutional collaboration established through the "NSF IceCube EPSCoR Initiative: IceCube and the Data Revolution" and will further encourage the involvement of EPSCoR institutions in this research area. Intellectual merits will be delivered through scientific exchanges during the workshop and, in the long term, by publishing workshop products on an open access platform in a citable manner. Supporting the NSF EPSCoR mission in various ways, the workshop will generate broader impacts and spur research initiatives by facilitating the exchange of scientific knowledge and fostering new connections among participants. By offering support to students from EPSCoR jurisdictions and underrepresented groups in STEM, the workshop will contribute to equality, diversity, and inclusion while preparing the next generation of the STEM workforce.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.