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    • Artificial Intelligence for the Electron Ion Collider (AI4EIC).

    C. Allaire, R. Ammendola，N. Zachariou,P. Zurita

    Computing and Software for Big Science（2024）

    Université Paris-Saclay | INFN | Brookhaven National Lab | Meta | National Institute for Nuclear Physics | Stony Brook University | Drexel University | Jefferson Lab | Laboratory Nuclear and High-Energy Physics | University of Michigan | Indian Institute of Technology Bombay | Massachusetts Institute of Technology | University of Manitoba | Charles University | CERN | University of Regina | University of Virginia | Central University of Karnataka | Facultad de Ciencias Físico-Matemáticas | TübingenUniversity | The Catholic University of America | University of Jammu | Hampton University | Oak Ridge National Laboratory | Virginia Union University | Lawrence Livermore National Laboratory | Center for Nuclear Femtography | Vanderbilt University | Old Dominion University | Duke University | University of Cincinnati | Fermilab | Berkeley Laboratory | University of Glasgow | Lawrence Berkeley National Laboratory | National Centre For Nuclear Research | William & Mary | Warsaw University of Technology | Florida International University | Argonne National Laboratory | Christopher Newport University | Baruch College | Bhabha Atomic Research Centre | Rutgers University | Universidad de Salamanca | Panjab University | Brookhaven National Laboratory | Jagiellonian University | Future University in Egypt | University of California Los Angeles | Pacific Northwest National Laboratory | University of California Irvine | University of York | Universidad Complutense de Madrid

    Cited 4|Views49
    Abstract
    The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.
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    Artificial Intelligence,Deep learning,EIC,ePIC,Machine learning,QCD,Physics
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
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    【要点】：本研究提出了一种新的机器学习方法——流匹配后验估计（FMPE），并结合重要性采样（IS）和噪声水平条件，实现了对系外行星大气特性的高效、灵活且可靠的检索。
    

    【方法】：通过采用流匹配后验估计方法，该方法在保持神经后验估计优点的同时，提供了更大的网络架构灵活性和可扩展性。
    

    【实验】：在模拟数据上，所提出的噪声水平条件FMPE和NPE模型性能与嵌套采样相当，且FMPE训练速度比NPE快约三倍，IS成功纠正了不准确的ML结果，并通过低效率识别模型失败，同时提供了准确的贝叶斯证据估计。数据集名称未在摘要中明确提及。
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