Domain Generalization with Machine Learning in the Nova Experiment
暫譯: 在Nova實驗中使用機器學習的領域泛化

Sutton, Andrew T. C.

Domain Generalization with Machine Learning in the Nova Experiment
  • 出版商: Springer
  • 出版日期: 2024-11-09
  • 售價: $6,100
  • 貴賓價: 9.5 折 $5,795
  • 語言: 英文
  • 頁數: 170
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3031435850
  • ISBN-13: 9783031435850
  • 相關分類: Machine Learning

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商品描述

This thesis presents significant advances in the use of neural networks to study the properties of neutrinos. Machine learning tools like neural networks (NN) can be used to identify the particle types or determine their energies in detectors such as those used in the NOvA neutrino experiment, which studies changes in a beam of neutrinos as it propagates approximately 800 km through the earth. NOvA relies heavily on simulations of the physics processes and the detector response; these simulations work well, but do not match the real experiment perfectly. Thus, neural networks trained on simulated datasets must include systematic uncertainties that account for possible imperfections in the simulation. This thesis presents the first application in HEP of adversarial domain generalization to a regression neural network. Applying domain generalization to problems with large systematic variations will reduce the impact of uncertainties while avoiding the risk of falselyconstraining the phase space. Reducing the impact of systematic uncertainties makes NOvA analysis more robust, and improves the significance of experimental results.

商品描述(中文翻譯)

本論文提出了在使用神經網絡研究中微子性質方面的重要進展。像神經網絡（NN）這樣的機器學習工具可以用來識別粒子類型或確定它們在檢測器中的能量，例如在NOvA中微子實驗中使用的檢測器，該實驗研究中微子束在地球中約800公里的傳播過程中的變化。NOvA在很大程度上依賴於物理過程和檢測器響應的模擬；這些模擬運作良好，但並不完全符合真實實驗。因此，基於模擬數據集訓練的神經網絡必須包含系統性不確定性，以考慮模擬中的可能不完美之處。本論文首次在高能物理（HEP）中將對抗性領域泛化應用於回歸神經網絡。將領域泛化應用於具有大系統變異的問題將減少不確定性的影響，同時避免錯誤限制相位空間的風險。減少系統性不確定性的影響使NOvA分析更加穩健，並提高實驗結果的顯著性。

作者簡介

I am an experimental particle physicist focusing on neutrino physics as part of the NOvA and ANNIE experiments located at the Fermi National Accelerator Laboratory (Fermilab) in Batavia Illinois, USA. After graduating cum laude from the University of North Carolina at Charlotte with scientific bachelor degrees in Mechanical Engineering and Physics, I went on to pursue my Ph.D. at the University of Virginia in Charlottesville Virginia. Under the supervision of Craig Group, I studied neutrino physics as a member of the NOvA collaboration. Putting my engineering degree to good use, I received the US Department of Energy Office of Science Graduate Student Research Award to travel to Fermilab and assist in the construction of a Test Beam experiment for NOvA. Alongside the NOvA Test Beam, I also contributed to the main 3-flavor oscillation analysis and was selected as part of the three-person writing committee to draft the paper summarizing our 2020 results (M.A Acero et al. 2022, doi: 10.1103/PhysRevD.106.032004). My graduate education culminated in the machine learning project detailed in this book, which focuses on a technique to train more robust neural networks and reduce the impact of systematic uncertainties that limit the precision of our measurements.

作者簡介(中文翻譯)

我是一名實驗粒子物理學家，專注於中微子物理，參與位於美國伊利諾伊州巴塔維亞的費米國家加速器實驗室（Fermilab）的NOvA和ANNIE實驗。在北卡羅來納州夏洛特大學以優異成績畢業，獲得機械工程和物理學的科學學士學位後，我繼續在維吉尼亞大學（University of Virginia）攻讀博士學位。在Craig Group的指導下，我作為NOvA合作組的一員研究中微子物理。充分利用我的工程學位，我獲得了美國能源部科學辦公室的研究生研究獎，前往Fermilab協助建設NOvA的測試束實驗。除了NOvA測試束外，我還參與了主要的三味振盪分析，並被選為三人寫作委員會的一部分，負責撰寫總結我們2020年結果的論文（M.A Acero等，2022，doi: 10.1103/PhysRevD.106.032004）。我的研究生教育 culminated 在本書詳細介紹的機器學習項目中，該項目專注於一種訓練更穩健神經網絡的技術，並減少系統性不確定性對我們測量精度的影響。

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