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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/2306.11330">arXiv:2306.11330</a> <span> [<a href="https://arxiv.org/pdf/2306.11330">pdf</a>, <a href="https://arxiv.org/format/2306.11330">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huang%2C+S">Shi-Yu Huang</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+Y">Yun-Chen Yang</a>, <a href="/search/cs?searchtype=author&query=Su%2C+Y">Yu-Ru Su</a>, <a href="/search/cs?searchtype=author&query=Lai%2C+B">Bo-Cheng Lai</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">Javier Duarte</a>, <a href="/search/cs?searchtype=author&query=Hauck%2C+S">Scott Hauck</a>, <a href="/search/cs?searchtype=author&query=Hsu%2C+S">Shih-Chieh Hsu</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+J">Jin-Xuan Hu</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</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="2306.11330v2-abstract-short" style="display: inline;"> In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits. Using GNN (Graph Neural Network) on FPGA has enabled superior accuracy with flexible trajectory classification. However, existing GNN architectures have inefficient resource usage and insufficient parallelism for edge classification. This paper introduce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.11330v2-abstract-full').style.display = 'inline'; document.getElementById('2306.11330v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.11330v2-abstract-full" style="display: none;"> In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits. Using GNN (Graph Neural Network) on FPGA has enabled superior accuracy with flexible trajectory classification. However, existing GNN architectures have inefficient resource usage and insufficient parallelism for edge classification. This paper introduces a resource-efficient GNN architecture on FPGAs for low latency particle tracking. The modular architecture facilitates design scalability to support large graphs. Leveraging the geometric properties of hit detectors further reduces graph complexity and resource usage. Our results on Xilinx UltraScale+ VU9P demonstrate 1625x and 1574x performance improvement over CPU and GPU respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.11330v2-abstract-full').style.display = 'none'; document.getElementById('2306.11330v2-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> 27 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.05081">arXiv:2212.05081</a> <span> [<a href="https://arxiv.org/pdf/2212.05081">pdf</a>, <a href="https://arxiv.org/format/2212.05081">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/2632-2153/ad12e3">10.1088/2632-2153/ad12e3 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> FAIR AI Models in High Energy Physics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Duarte%2C+J">Javier Duarte</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H">Haoyang Li</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+A">Avik Roy</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+R">Ruike Zhu</a>, <a href="/search/cs?searchtype=author&query=Huerta%2C+E+A">E. A. Huerta</a>, <a href="/search/cs?searchtype=author&query=Diaz%2C+D">Daniel Diaz</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">Philip Harris</a>, <a href="/search/cs?searchtype=author&query=Kansal%2C+R">Raghav Kansal</a>, <a href="/search/cs?searchtype=author&query=Katz%2C+D+S">Daniel S. Katz</a>, <a href="/search/cs?searchtype=author&query=Kavoori%2C+I+H">Ishaan H. Kavoori</a>, <a href="/search/cs?searchtype=author&query=Kindratenko%2C+V+V">Volodymyr V. Kindratenko</a>, <a href="/search/cs?searchtype=author&query=Mokhtar%2C+F">Farouk Mokhtar</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Park%2C+S+E">Sang Eon Park</a>, <a href="/search/cs?searchtype=author&query=Quinnan%2C+M">Melissa Quinnan</a>, <a href="/search/cs?searchtype=author&query=Rusack%2C+R">Roger Rusack</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+Z">Zhizhen Zhao</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="2212.05081v3-abstract-short" style="display: inline;"> The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning (ML) models -- algorithms that have been trained on data without being explicitly… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.05081v3-abstract-full').style.display = 'inline'; document.getElementById('2212.05081v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.05081v3-abstract-full" style="display: none;"> The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning (ML) models -- algorithms that have been trained on data without being explicitly programmed -- and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template's use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.05081v3-abstract-full').style.display = 'none'; document.getElementById('2212.05081v3-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> 29 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </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">34 pages, 9 figures, 10 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Mach. Learn.: Sci. Technol. 4 (2023) 045062 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.08973">arXiv:2210.08973</a> <span> [<a href="https://arxiv.org/pdf/2210.08973">pdf</a>, <a href="https://arxiv.org/ps/2210.08973">ps</a>, <a href="https://arxiv.org/format/2210.08973">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41597-023-02298-6">10.1038/s41597-023-02298-6 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> FAIR for AI: An interdisciplinary and international community building perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huerta%2C+E+A">E. A. Huerta</a>, <a href="/search/cs?searchtype=author&query=Blaiszik%2C+B">Ben Blaiszik</a>, <a href="/search/cs?searchtype=author&query=Brinson%2C+L+C">L. Catherine Brinson</a>, <a href="/search/cs?searchtype=author&query=Bouchard%2C+K+E">Kristofer E. Bouchard</a>, <a href="/search/cs?searchtype=author&query=Diaz%2C+D">Daniel Diaz</a>, <a href="/search/cs?searchtype=author&query=Doglioni%2C+C">Caterina Doglioni</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J+M">Javier M. Duarte</a>, <a href="/search/cs?searchtype=author&query=Emani%2C+M">Murali Emani</a>, <a href="/search/cs?searchtype=author&query=Foster%2C+I">Ian Foster</a>, <a href="/search/cs?searchtype=author&query=Fox%2C+G">Geoffrey Fox</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">Philip Harris</a>, <a href="/search/cs?searchtype=author&query=Heinrich%2C+L">Lukas Heinrich</a>, <a href="/search/cs?searchtype=author&query=Jha%2C+S">Shantenu Jha</a>, <a href="/search/cs?searchtype=author&query=Katz%2C+D+S">Daniel S. Katz</a>, <a href="/search/cs?searchtype=author&query=Kindratenko%2C+V">Volodymyr Kindratenko</a>, <a href="/search/cs?searchtype=author&query=Kirkpatrick%2C+C+R">Christine R. Kirkpatrick</a>, <a href="/search/cs?searchtype=author&query=Lassila-Perini%2C+K">Kati Lassila-Perini</a>, <a href="/search/cs?searchtype=author&query=Madduri%2C+R+K">Ravi K. Madduri</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Psomopoulos%2C+F+E">Fotis E. Psomopoulos</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+A">Avik Roy</a>, <a href="/search/cs?searchtype=author&query=R%C3%BCbel%2C+O">Oliver R眉bel</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+Z">Zhizhen Zhao</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+R">Ruike Zhu</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="2210.08973v2-abstract-short" style="display: inline;"> A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.08973v2-abstract-full').style.display = 'inline'; document.getElementById('2210.08973v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.08973v2-abstract-full" style="display: none;"> A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.08973v2-abstract-full').style.display = 'none'; document.getElementById('2210.08973v2-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> 1 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </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, comments welcome!; v2: 12 pages, accepted to Scientific Data</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.0; E.0 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Scientific Data 10, 487 (2023) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.04371">arXiv:2210.04371</a> <span> [<a href="https://arxiv.org/pdf/2210.04371">pdf</a>, <a href="https://arxiv.org/format/2210.04371">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/2632-2153/ace0a1">10.1088/2632-2153/ace0a1 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Detailed Study of Interpretability of Deep Neural Network based Top Taggers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khot%2C+A">Ayush Khot</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+A">Avik Roy</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="2210.04371v4-abstract-short" style="display: inline;"> Recent developments in the methods of explainable AI (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input-output relationships and realizing how data connects with machine learning models. In this paper we explore interpretability of DNN models designed to identify jets coming from top quark decay in high energy proton-prot… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.04371v4-abstract-full').style.display = 'inline'; document.getElementById('2210.04371v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.04371v4-abstract-full" style="display: none;"> Recent developments in the methods of explainable AI (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input-output relationships and realizing how data connects with machine learning models. In this paper we explore interpretability of DNN models designed to identify jets coming from top quark decay in high energy proton-proton collisions at the Large Hadron Collider (LHC). We review a subset of existing top tagger models and explore different quantitative methods to identify which features play the most important roles in identifying the top jets. We also investigate how and why feature importance varies across different XAI metrics, how correlations among features impact their explainability, and how latent space representations encode information as well as correlate with physically meaningful quantities. Our studies uncover some major pitfalls of existing XAI methods and illustrate how they can be overcome to obtain consistent and meaningful interpretation of these models. We additionally illustrate the activity of hidden layers as Neural Activation Pattern (NAP) diagrams and demonstrate how they can be used to understand how DNNs relay information across the layers and how this understanding can help to make such models significantly simpler by allowing effective model reoptimization and hyperparameter tuning. These studies not only facilitate a methodological approach to interpreting models but also unveil new insights about what these models learn. Incorporating these observations into augmented model design, we propose the Particle Flow Interaction Network (PFIN) model and demonstrate how interpretability-inspired model augmentation can improve top tagging performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.04371v4-abstract-full').style.display = 'none'; document.getElementById('2210.04371v4-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> 5 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </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">Repository: https://github.com/FAIR4HEP/xAI4toptagger. Some figure cosmetics have been changed. Accepted at Machine Learning: Science and Technology</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.08868">arXiv:2209.08868</a> <span> [<a href="https://arxiv.org/pdf/2209.08868">pdf</a>, <a href="https://arxiv.org/format/2209.08868">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Lattice">hep-lat</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Theory">hep-th</span> </div> </div> <p class="title is-5 mathjax"> Snowmass 2021 Computational Frontier CompF4 Topical Group Report: Storage and Processing Resource Access </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhimji%2C+W">W. Bhimji</a>, <a href="/search/cs?searchtype=author&query=Carder%2C+D">D. Carder</a>, <a href="/search/cs?searchtype=author&query=Dart%2C+E">E. Dart</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">J. Duarte</a>, <a href="/search/cs?searchtype=author&query=Fisk%2C+I">I. Fisk</a>, <a href="/search/cs?searchtype=author&query=Gardner%2C+R">R. Gardner</a>, <a href="/search/cs?searchtype=author&query=Guok%2C+C">C. Guok</a>, <a href="/search/cs?searchtype=author&query=Jayatilaka%2C+B">B. Jayatilaka</a>, <a href="/search/cs?searchtype=author&query=Lehman%2C+T">T. Lehman</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+M">M. Lin</a>, <a href="/search/cs?searchtype=author&query=Maltzahn%2C+C">C. Maltzahn</a>, <a href="/search/cs?searchtype=author&query=McKee%2C+S">S. McKee</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">M. S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Rind%2C+O">O. Rind</a>, <a href="/search/cs?searchtype=author&query=Shadura%2C+O">O. Shadura</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N+V">N. V. Tran</a>, <a href="/search/cs?searchtype=author&query=van+Gemmeren%2C+P">P. van Gemmeren</a>, <a href="/search/cs?searchtype=author&query=Watts%2C+G">G. Watts</a>, <a href="/search/cs?searchtype=author&query=Weaver%2C+B+A">B. A. Weaver</a>, <a href="/search/cs?searchtype=author&query=W%C3%BCrthwein%2C+F">F. W眉rthwein</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="2209.08868v2-abstract-short" style="display: inline;"> Computing plays a significant role in all areas of high energy physics. The Snowmass 2021 CompF4 topical group's scope is facilities R&D, where we consider "facilities" as the computing hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commer… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.08868v2-abstract-full').style.display = 'inline'; document.getElementById('2209.08868v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.08868v2-abstract-full" style="display: none;"> Computing plays a significant role in all areas of high energy physics. The Snowmass 2021 CompF4 topical group's scope is facilities R&D, where we consider "facilities" as the computing hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commercial clouds, federally funded High Performance Computing (HPC) systems for all of science, and systems funded explicitly for a given experimental or theoretical program. This topical group report summarizes the findings and recommendations for the storage, processing, networking and associated software service infrastructures for future high energy physics research, based on the discussions organized through the Snowmass 2021 community study. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.08868v2-abstract-full').style.display = 'none'; document.getElementById('2209.08868v2-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> 29 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </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">Snowmass 2021 Computational Frontier CompF4 topical group report. v2: Expanded introduction. Updated author list. 52 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.09060">arXiv:2207.09060</a> <span> [<a href="https://arxiv.org/pdf/2207.09060">pdf</a>, <a href="https://arxiv.org/format/2207.09060">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Physics Education">physics.ed-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Data Science and Machine Learning in Education </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Benelli%2C+G">Gabriele Benelli</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+T+Y">Thomas Y. Chen</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">Javier Duarte</a>, <a href="/search/cs?searchtype=author&query=Feickert%2C+M">Matthew Feickert</a>, <a href="/search/cs?searchtype=author&query=Graham%2C+M">Matthew Graham</a>, <a href="/search/cs?searchtype=author&query=Gray%2C+L">Lindsey Gray</a>, <a href="/search/cs?searchtype=author&query=Hackett%2C+D">Dan Hackett</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">Phil Harris</a>, <a href="/search/cs?searchtype=author&query=Hsu%2C+S">Shih-Chieh Hsu</a>, <a href="/search/cs?searchtype=author&query=Kasieczka%2C+G">Gregor Kasieczka</a>, <a href="/search/cs?searchtype=author&query=Khoda%2C+E+E">Elham E. Khoda</a>, <a href="/search/cs?searchtype=author&query=Komm%2C+M">Matthias Komm</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+M">Mia Liu</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Norberg%2C+S">Scarlet Norberg</a>, <a href="/search/cs?searchtype=author&query=Perloff%2C+A">Alexx Perloff</a>, <a href="/search/cs?searchtype=author&query=Rieger%2C+M">Marcel Rieger</a>, <a href="/search/cs?searchtype=author&query=Savard%2C+C">Claire Savard</a>, <a href="/search/cs?searchtype=author&query=Terao%2C+K">Kazuhiro Terao</a>, <a href="/search/cs?searchtype=author&query=Thais%2C+S">Savannah Thais</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+A">Avik Roy</a>, <a href="/search/cs?searchtype=author&query=Vlimant%2C+J">Jean-Roch Vlimant</a>, <a href="/search/cs?searchtype=author&query=Chachamis%2C+G">Grigorios Chachamis</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="2207.09060v1-abstract-short" style="display: inline;"> The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. HEP researchers benefit gr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09060v1-abstract-full').style.display = 'inline'; document.getElementById('2207.09060v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.09060v1-abstract-full" style="display: none;"> The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. HEP researchers benefit greatly from materials widely available materials for use in education, training and workforce development. They are also contributing to these materials and providing software to DS/ML-related fields. Increasingly, physics departments are offering courses at the intersection of DS, ML and physics, often using curricula developed by HEP researchers and involving open software and data used in HEP. In this white paper, we explore synergies between HEP research and DS/ML education, discuss opportunities and challenges at this intersection, and propose community activities that will be mutually beneficial. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09060v1-abstract-full').style.display = 'none'; document.getElementById('2207.09060v1-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> 19 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </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">Contribution to Snowmass 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.06632">arXiv:2206.06632</a> <span> [<a href="https://arxiv.org/pdf/2206.06632">pdf</a>, <a href="https://arxiv.org/format/2206.06632">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Explainable AI for High Energy Physics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+A">Avik Roy</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="2206.06632v1-abstract-short" style="display: inline;"> Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as \textit{black boxes}- whose inner workings to convey information and build the desired input-output relationship are often intractable. Explainable AI (xAI) methods can be useful in determining a neural model's relationship with data toward making it \textit{interp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.06632v1-abstract-full').style.display = 'inline'; document.getElementById('2206.06632v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.06632v1-abstract-full" style="display: none;"> Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as \textit{black boxes}- whose inner workings to convey information and build the desired input-output relationship are often intractable. Explainable AI (xAI) methods can be useful in determining a neural model's relationship with data toward making it \textit{interpretable} by establishing a quantitative and tractable relationship between the input and the model's output. In this letter of interest, we explore the potential of using xAI methods in the context of problems in high energy physics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.06632v1-abstract-full').style.display = 'none'; document.getElementById('2206.06632v1-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> 14 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </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">Contribution to Snowmass 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.12852">arXiv:2203.12852</a> <span> [<a href="https://arxiv.org/pdf/2203.12852">pdf</a>, <a href="https://arxiv.org/format/2203.12852">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> </div> </div> <p class="title is-5 mathjax"> Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Thais%2C+S">Savannah Thais</a>, <a href="/search/cs?searchtype=author&query=Calafiura%2C+P">Paolo Calafiura</a>, <a href="/search/cs?searchtype=author&query=Chachamis%2C+G">Grigorios Chachamis</a>, <a href="/search/cs?searchtype=author&query=DeZoort%2C+G">Gage DeZoort</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">Javier Duarte</a>, <a href="/search/cs?searchtype=author&query=Ganguly%2C+S">Sanmay Ganguly</a>, <a href="/search/cs?searchtype=author&query=Kagan%2C+M">Michael Kagan</a>, <a href="/search/cs?searchtype=author&query=Murnane%2C+D">Daniel Murnane</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Terao%2C+K">Kazuhiro Terao</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="2203.12852v2-abstract-short" style="display: inline;"> Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs. This allows a wide variety of high- and low-level physical featur… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.12852v2-abstract-full').style.display = 'inline'; document.getElementById('2203.12852v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.12852v2-abstract-full" style="display: none;"> Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs. This allows a wide variety of high- and low-level physical features to be attached to measurements and, by the same token, a wide variety of HEP tasks to be accomplished by the same GNN architectures. GNNs have found powerful use-cases in reconstruction, tagging, generation and end-to-end analysis. With the wide-spread adoption of GNNs in industry, the HEP community is well-placed to benefit from rapid improvements in GNN latency and memory usage. However, industry use-cases are not perfectly aligned with HEP and much work needs to be done to best match unique GNN capabilities to unique HEP obstacles. We present here a range of these capabilities, predictions of which are currently being well-adopted in HEP communities, and which are still immature. We hope to capture the landscape of graph techniques in machine learning as well as point out the most significant gaps that are inhibiting potentially large leaps in research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.12852v2-abstract-full').style.display = 'none'; document.getElementById('2203.12852v2-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> 25 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </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">contribution to Snowmass 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.13041">arXiv:2110.13041</a> <span> [<a href="https://arxiv.org/pdf/2110.13041">pdf</a>, <a href="https://arxiv.org/format/2110.13041">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="Hardware Architecture">cs.AR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3389/fdata.2022.787421">10.3389/fdata.2022.787421 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Applications and Techniques for Fast Machine Learning in Science </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Deiana%2C+A+M">Allison McCarn Deiana</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N">Nhan Tran</a>, <a href="/search/cs?searchtype=author&query=Agar%2C+J">Joshua Agar</a>, <a href="/search/cs?searchtype=author&query=Blott%2C+M">Michaela Blott</a>, <a href="/search/cs?searchtype=author&query=Di+Guglielmo%2C+G">Giuseppe Di Guglielmo</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">Javier Duarte</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">Philip Harris</a>, <a href="/search/cs?searchtype=author&query=Hauck%2C+S">Scott Hauck</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+M">Mia Liu</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Ngadiuba%2C+J">Jennifer Ngadiuba</a>, <a href="/search/cs?searchtype=author&query=Ogrenci-Memik%2C+S">Seda Ogrenci-Memik</a>, <a href="/search/cs?searchtype=author&query=Pierini%2C+M">Maurizio Pierini</a>, <a href="/search/cs?searchtype=author&query=Aarrestad%2C+T">Thea Aarrestad</a>, <a href="/search/cs?searchtype=author&query=Bahr%2C+S">Steffen Bahr</a>, <a href="/search/cs?searchtype=author&query=Becker%2C+J">Jurgen Becker</a>, <a href="/search/cs?searchtype=author&query=Berthold%2C+A">Anne-Sophie Berthold</a>, <a href="/search/cs?searchtype=author&query=Bonventre%2C+R+J">Richard J. Bonventre</a>, <a href="/search/cs?searchtype=author&query=Bravo%2C+T+E+M">Tomas E. Muller Bravo</a>, <a href="/search/cs?searchtype=author&query=Diefenthaler%2C+M">Markus Diefenthaler</a>, <a href="/search/cs?searchtype=author&query=Dong%2C+Z">Zhen Dong</a>, <a href="/search/cs?searchtype=author&query=Fritzsche%2C+N">Nick Fritzsche</a>, <a href="/search/cs?searchtype=author&query=Gholami%2C+A">Amir Gholami</a>, <a href="/search/cs?searchtype=author&query=Govorkova%2C+E">Ekaterina Govorkova</a>, <a href="/search/cs?searchtype=author&query=Hazelwood%2C+K+J">Kyle J Hazelwood</a> , et al. (62 additional authors not shown) </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="2110.13041v1-abstract-short" style="display: inline;"> In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML ac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.13041v1-abstract-full').style.display = 'inline'; document.getElementById('2110.13041v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.13041v1-abstract-full" style="display: none;"> In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.13041v1-abstract-full').style.display = 'none'; document.getElementById('2110.13041v1-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> 25 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </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">66 pages, 13 figures, 5 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-21-502-AD-E-SCD </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Front. Big Data 5, 787421 (2022) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.02214">arXiv:2108.02214</a> <span> [<a href="https://arxiv.org/pdf/2108.02214">pdf</a>, <a href="https://arxiv.org/format/2108.02214">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</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="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41597-021-01109-0">10.1038/s41597-021-01109-0 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A FAIR and AI-ready Higgs boson decay dataset </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yifan Chen</a>, <a href="/search/cs?searchtype=author&query=Huerta%2C+E+A">E. A. Huerta</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">Javier Duarte</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">Philip Harris</a>, <a href="/search/cs?searchtype=author&query=Katz%2C+D+S">Daniel S. Katz</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Diaz%2C+D">Daniel Diaz</a>, <a href="/search/cs?searchtype=author&query=Mokhtar%2C+F">Farouk Mokhtar</a>, <a href="/search/cs?searchtype=author&query=Kansal%2C+R">Raghav Kansal</a>, <a href="/search/cs?searchtype=author&query=Park%2C+S+E">Sang Eon Park</a>, <a href="/search/cs?searchtype=author&query=Kindratenko%2C+V+V">Volodymyr V. Kindratenko</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+Z">Zhizhen Zhao</a>, <a href="/search/cs?searchtype=author&query=Rusack%2C+R">Roger Rusack</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="2108.02214v2-abstract-short" style="display: inline;"> To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.02214v2-abstract-full').style.display = 'inline'; document.getElementById('2108.02214v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.02214v2-abstract-full" style="display: none;"> To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.02214v2-abstract-full').style.display = 'none'; document.getElementById('2108.02214v2-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> 16 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </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">13 pages, 3 figures. v2: Accepted to Nature Scientific Data. Learn about the FAIR4HEP project at https://fair4hep.github.io. See our invited Behind the Paper Blog in Springer Nature Research Data Community at https://go.nature.com/3oMVYxo</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2; J.2 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Scientific Data volume 9, Article number: 31 (2022) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.05102">arXiv:2010.05102</a> <span> [<a href="https://arxiv.org/pdf/2010.05102">pdf</a>, <a href="https://arxiv.org/format/2010.05102">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.5281/zenodo.4082137">10.5281/zenodo.4082137 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Software Sustainability & High Energy Physics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Katz%2C+D+S">Daniel S. Katz</a>, <a href="/search/cs?searchtype=author&query=Malik%2C+S">Sudhir Malik</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Stewart%2C+G+A">Graeme A. Stewart</a>, <a href="/search/cs?searchtype=author&query=Assamagan%2C+K+A">K茅t茅vi A. Assamagan</a>, <a href="/search/cs?searchtype=author&query=Becker%2C+E+A">Erin A. Becker</a>, <a href="/search/cs?searchtype=author&query=Hong%2C+N+P+C">Neil P. Chue Hong</a>, <a href="/search/cs?searchtype=author&query=Cosden%2C+I+A">Ian A. Cosden</a>, <a href="/search/cs?searchtype=author&query=Meehan%2C+S">Samuel Meehan</a>, <a href="/search/cs?searchtype=author&query=Moyse%2C+E+J+W">Edward J. W. Moyse</a>, <a href="/search/cs?searchtype=author&query=Price-Whelan%2C+A+M">Adrian M. Price-Whelan</a>, <a href="/search/cs?searchtype=author&query=Sexton-Kennedy%2C+E">Elizabeth Sexton-Kennedy</a>, <a href="/search/cs?searchtype=author&query=Evans%2C+M+O">Meirin Oan Evans</a>, <a href="/search/cs?searchtype=author&query=Feickert%2C+M">Matthew Feickert</a>, <a href="/search/cs?searchtype=author&query=Lange%2C+C">Clemens Lange</a>, <a href="/search/cs?searchtype=author&query=Lieret%2C+K">Kilian Lieret</a>, <a href="/search/cs?searchtype=author&query=Quick%2C+R">Rob Quick</a>, <a href="/search/cs?searchtype=author&query=Pineda%2C+A+S">Arturo S谩nchez Pineda</a>, <a href="/search/cs?searchtype=author&query=Tunnell%2C+C">Christopher Tunnell</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="2010.05102v2-abstract-short" style="display: inline;"> New facilities of the 2020s, such as the High Luminosity Large Hadron Collider (HL-LHC), will be relevant through at least the 2030s. This means that their software efforts and those that are used to analyze their data need to consider sustainability to enable their adaptability to new challenges, longevity, and efficiency, over at least this period. This will help ensure that this software will b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.05102v2-abstract-full').style.display = 'inline'; document.getElementById('2010.05102v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.05102v2-abstract-full" style="display: none;"> New facilities of the 2020s, such as the High Luminosity Large Hadron Collider (HL-LHC), will be relevant through at least the 2030s. This means that their software efforts and those that are used to analyze their data need to consider sustainability to enable their adaptability to new challenges, longevity, and efficiency, over at least this period. This will help ensure that this software will be easier to develop and maintain, that it remains available in the future on new platforms, that it meets new needs, and that it is as reusable as possible. This report discusses a virtual half-day workshop on "Software Sustainability and High Energy Physics" that aimed 1) to bring together experts from HEP as well as those from outside to share their experiences and practices, and 2) to articulate a vision that helps the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) to create a work plan to implement elements of software sustainability. Software sustainability practices could lead to new collaborations, including elements of HEP software being directly used outside the field, and, as has happened more frequently in recent years, to HEP developers contributing to software developed outside the field rather than reinventing it. A focus on and skills related to sustainable software will give HEP software developers an important skill that is essential to careers in the realm of software, inside or outside HEP. The report closes with recommendations to improve software sustainability in HEP, aimed at the HEP community via IRIS-HEP and the HEP Software Foundation (HSF). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.05102v2-abstract-full').style.display = 'none'; document.getElementById('2010.05102v2-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> 16 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </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">A report from the "Sustainable Software in HEP" IRIS-HEP blueprint workshop: https://indico.cern.ch/event/930127/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.00522">arXiv:1902.00522</a> <span> [<a href="https://arxiv.org/pdf/1902.00522">pdf</a>, <a href="https://arxiv.org/ps/1902.00522">ps</a>, <a href="https://arxiv.org/format/1902.00522">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Relativity and Quantum Cosmology">gr-qc</span> </div> </div> <p class="title is-5 mathjax"> Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Allen%2C+G">Gabrielle Allen</a>, <a href="/search/cs?searchtype=author&query=Andreoni%2C+I">Igor Andreoni</a>, <a href="/search/cs?searchtype=author&query=Bachelet%2C+E">Etienne Bachelet</a>, <a href="/search/cs?searchtype=author&query=Berriman%2C+G+B">G. Bruce Berriman</a>, <a href="/search/cs?searchtype=author&query=Bianco%2C+F+B">Federica B. Bianco</a>, <a href="/search/cs?searchtype=author&query=Biswas%2C+R">Rahul Biswas</a>, <a href="/search/cs?searchtype=author&query=Kind%2C+M+C">Matias Carrasco Kind</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+K">Kyle Chard</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+M">Minsik Cho</a>, <a href="/search/cs?searchtype=author&query=Cowperthwaite%2C+P+S">Philip S. Cowperthwaite</a>, <a href="/search/cs?searchtype=author&query=Etienne%2C+Z+B">Zachariah B. Etienne</a>, <a href="/search/cs?searchtype=author&query=George%2C+D">Daniel George</a>, <a href="/search/cs?searchtype=author&query=Gibbs%2C+T">Tom Gibbs</a>, <a href="/search/cs?searchtype=author&query=Graham%2C+M">Matthew Graham</a>, <a href="/search/cs?searchtype=author&query=Gropp%2C+W">William Gropp</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+A">Anushri Gupta</a>, <a href="/search/cs?searchtype=author&query=Haas%2C+R">Roland Haas</a>, <a href="/search/cs?searchtype=author&query=Huerta%2C+E+A">E. A. Huerta</a>, <a href="/search/cs?searchtype=author&query=Jennings%2C+E">Elise Jennings</a>, <a href="/search/cs?searchtype=author&query=Katz%2C+D+S">Daniel S. Katz</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+A">Asad Khan</a>, <a href="/search/cs?searchtype=author&query=Kindratenko%2C+V">Volodymyr Kindratenko</a>, <a href="/search/cs?searchtype=author&query=Kramer%2C+W+T+C">William T. C. Kramer</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+X">Xin Liu</a>, <a href="/search/cs?searchtype=author&query=Mahabal%2C+A">Ashish Mahabal</a> , et al. (23 additional authors not shown) </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="1902.00522v1-abstract-short" style="display: inline;"> This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, compu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.00522v1-abstract-full').style.display = 'inline'; document.getElementById('1902.00522v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.00522v1-abstract-full" style="display: none;"> This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.00522v1-abstract-full').style.display = 'none'; document.getElementById('1902.00522v1-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> 1 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </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">15 pages, no figures. White paper based on the "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at NCSA, October 17-19, 2018 http://www.ncsa.illinois.edu/Conferences/DeepLearningLSST/</span> </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"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 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