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endnote citation export for MOP6307: Online tuning of the NSLS-II injector using Bayesian optimization with different packages

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  <records>
    <record>
      <contributors>
        <authors>
          <author>Song, M.</author>
          <author>Hidaka, Y.</author>
          <author>Wang, G.</author>
          <author>Yang, X.</author>
        </authors>
      </contributors>
      <titles>
        <title>Online tuning of the NSLS-II injector using Bayesian optimization with different packages</title>
      </titles>
      <publisher>JACoW Publishing</publisher>
      <pub-location>Geneva, Switzerland</pub-location>
      <issn>2673-5350</issn>
      <isbn>978-3-95450-252-3</isbn>
      <language>English</language>
      <pages>352-354</pages>
      <keywords>
        <keyword>linac</keyword>
        <keyword>booster</keyword>
        <keyword>injection</keyword>
        <keyword>acceleration</keyword>
        <keyword>electron</keyword>
        <keyword>electronics</keyword>
        <keyword>timing</keyword>
        <keyword>controls</keyword>
      </keywords>
      <work-type>Contribution to a conference proceedings</work-type>
      <dates>
        <year>2026</year>
        <pub-dates>
          <date>2026-05</date>
        </pub-dates>
      </dates>
      <urls>
        <related-urls>
          <url>https://jacow.org/ipac2026/pdf/MOP6307.pdf</url>
        </related-urls>
      </urls>
      <abstract>The injector of the NSLS-II consists of a linear accelerator (LINAC) that accelerates the electron beam to 170 MeV, followed by a linac-to-booster (LTB) transport line and a booster synchrotron that further increases the beam energy to 3 GeV. The performance of LINAC and LTB is critical to achieve efficient and stable beam injection. Automated online tuning is an effective method to improve injector performance. In this paper, we present an automated tuning approach based on Bayesian optimization, using different software packages to optimize the LINAC and LTB. We evaluate and compare these packages based on their ability to improve injection efficiency. Our results demonstrate that Bayesian optimization can significantly enhance injector performance and show differences in performance between different packages.</abstract>
    </record>
  </records>
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