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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.17372">arXiv:2312.17372</a> <span> [<a href="https://arxiv.org/pdf/2312.17372">pdf</a>, <a href="https://arxiv.org/format/2312.17372">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> </div> </div> <p class="title is-5 mathjax"> Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+C">Chenwei Xu</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+J+Y">Jerry Yao-Chieh Hu</a>, <a href="/search/cs?searchtype=author&query=Narayanan%2C+A">Aakaash Narayanan</a>, <a href="/search/cs?searchtype=author&query=Thieme%2C+M">Mattson Thieme</a>, <a href="/search/cs?searchtype=author&query=Nagaslaev%2C+V">Vladimir Nagaslaev</a>, <a href="/search/cs?searchtype=author&query=Austin%2C+M">Mark Austin</a>, <a href="/search/cs?searchtype=author&query=Arnold%2C+J">Jeremy Arnold</a>, <a href="/search/cs?searchtype=author&query=Berlioz%2C+J">Jose Berlioz</a>, <a href="/search/cs?searchtype=author&query=Hanlet%2C+P">Pierrick Hanlet</a>, <a href="/search/cs?searchtype=author&query=Ibrahim%2C+A">Aisha Ibrahim</a>, <a href="/search/cs?searchtype=author&query=Nicklaus%2C+D">Dennis Nicklaus</a>, <a href="/search/cs?searchtype=author&query=Mitrevski%2C+J">Jovan Mitrevski</a>, <a href="/search/cs?searchtype=author&query=John%2C+J+M+S">Jason Michael St. John</a>, <a href="/search/cs?searchtype=author&query=Pradhan%2C+G">Gauri Pradhan</a>, <a href="/search/cs?searchtype=author&query=Saewert%2C+A">Andrea Saewert</a>, <a href="/search/cs?searchtype=author&query=Seiya%2C+K">Kiyomi Seiya</a>, <a href="/search/cs?searchtype=author&query=Schupbach%2C+B">Brian Schupbach</a>, <a href="/search/cs?searchtype=author&query=Thurman-Keup%2C+R">Randy Thurman-Keup</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N">Nhan Tran</a>, <a href="/search/cs?searchtype=author&query=Shi%2C+R">Rui Shi</a>, <a href="/search/cs?searchtype=author&query=Ogrenci%2C+S">Seda Ogrenci</a>, <a href="/search/cs?searchtype=author&query=Shuping%2C+A+M">Alexis Maya-Isabelle Shuping</a>, <a href="/search/cs?searchtype=author&query=Hazelwood%2C+K">Kyle Hazelwood</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+H">Han Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.17372v1-abstract-short" style="display: inline;"> We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at addressing the challenge of maintaining a uniform proton beam intensity delivery in the Muon to Electron Conversion Experiment (Mu2e) at Fermi National Accelerator Laboratory (Fermilab). Our primary objective is to regulate the spill process to ensure a consistent intensity profile, with the ultimate goal of creating an aut… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.17372v1-abstract-full').style.display = 'inline'; document.getElementById('2312.17372v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.17372v1-abstract-full" style="display: none;"> We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at addressing the challenge of maintaining a uniform proton beam intensity delivery in the Muon to Electron Conversion Experiment (Mu2e) at Fermi National Accelerator Laboratory (Fermilab). Our primary objective is to regulate the spill process to ensure a consistent intensity profile, with the ultimate goal of creating an automated controller capable of providing real-time feedback and calibration of the Spill Regulation System (SRS) parameters on a millisecond timescale. We treat the Mu2e accelerator system as a Markov Decision Process suitable for Reinforcement Learning (RL), utilizing PPO to reduce bias and enhance training stability. A key innovation in our approach is the integration of a neuralized Proportional-Integral-Derivative (PID) controller into the policy function, resulting in a significant improvement in the Spill Duty Factor (SDF) by 13.6%, surpassing the performance of the current PID controller baseline by an additional 1.6%. This paper presents the preliminary offline results based on a differentiable simulator of the Mu2e accelerator. It paves the groundwork for real-time implementations and applications, representing a crucial step towards automated proton beam intensity control for the Mu2e experiment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.17372v1-abstract-full').style.display = 'none'; document.getElementById('2312.17372v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, accepted at NeurIPS 2023 ML4Phy Workshop</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.05716">arXiv:2311.05716</a> <span> [<a href="https://arxiv.org/pdf/2311.05716">pdf</a>, <a href="https://arxiv.org/format/2311.05716">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> </div> </div> <p class="title is-5 mathjax"> ML-based Real-Time Control at the Edge: An Approach Using hls4ml </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shi%2C+R">R. Shi</a>, <a href="/search/cs?searchtype=author&query=Ogrenci%2C+S">S. Ogrenci</a>, <a href="/search/cs?searchtype=author&query=Arnold%2C+J+M">J. M. Arnold</a>, <a href="/search/cs?searchtype=author&query=Berlioz%2C+J+R">J. R. Berlioz</a>, <a href="/search/cs?searchtype=author&query=Hanlet%2C+P">P. Hanlet</a>, <a href="/search/cs?searchtype=author&query=Hazelwood%2C+K+J">K. J. Hazelwood</a>, <a href="/search/cs?searchtype=author&query=Ibrahim%2C+M+A">M. A. Ibrahim</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+H">H. Liu</a>, <a href="/search/cs?searchtype=author&query=Nagaslaev%2C+V+P">V. P. Nagaslaev</a>, <a href="/search/cs?searchtype=author&query=1%2C+A+N">A. Narayanan 1</a>, <a href="/search/cs?searchtype=author&query=Nicklaus%2C+D+J">D. J. Nicklaus</a>, <a href="/search/cs?searchtype=author&query=Mitrevski%2C+J">J. Mitrevski</a>, <a href="/search/cs?searchtype=author&query=Pradhan%2C+G">G. Pradhan</a>, <a href="/search/cs?searchtype=author&query=Saewert%2C+A+L">A. L. Saewert</a>, <a href="/search/cs?searchtype=author&query=Schupbach%2C+B+A">B. A. Schupbach</a>, <a href="/search/cs?searchtype=author&query=Seiya%2C+K">K. Seiya</a>, <a href="/search/cs?searchtype=author&query=Thieme%2C+M">M. Thieme</a>, <a href="/search/cs?searchtype=author&query=Thurman-Keup%2C+R+M">R. M. Thurman-Keup</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N+V">N. V. Tran</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.05716v1-abstract-short" style="display: inline;"> This study focuses on implementing a real-time control system for a particle accelerator facility that performs high energy physics experiments. A critical operating parameter in this facility is beam loss, which is the fraction of particles deviating from the accelerated proton beam into a cascade of secondary particles. Accelerators employ a large number of sensors to monitor beam loss. The data… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.05716v1-abstract-full').style.display = 'inline'; document.getElementById('2311.05716v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.05716v1-abstract-full" style="display: none;"> This study focuses on implementing a real-time control system for a particle accelerator facility that performs high energy physics experiments. A critical operating parameter in this facility is beam loss, which is the fraction of particles deviating from the accelerated proton beam into a cascade of secondary particles. Accelerators employ a large number of sensors to monitor beam loss. The data from these sensors is monitored by human operators who predict the relative contribution of different sub-systems to the beam loss. Using this information, they engage control interventions. In this paper, we present a controller to track this phenomenon in real-time using edge-Machine Learning (ML) and support control with low latency and high accuracy. We implemented this system on an Intel Arria 10 SoC. Optimizations at the algorithm, high-level synthesis, and interface levels to improve latency and resource usage are presented. Our design implements a neural network, which can predict the main source of beam loss (between two possible causes) at speeds up to 575 frames per second (fps) (average latency of 1.74 ms). The practical deployed system is required to operate at 320 fps, with a 3ms latency requirement, which has been met by our design successfully. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.05716v1-abstract-full').style.display = 'none'; document.getElementById('2311.05716v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> 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