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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/2411.17644">arXiv:2411.17644</a> <span> [<a href="https://arxiv.org/pdf/2411.17644">pdf</a>, <a href="https://arxiv.org/format/2411.17644">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> Virtual Pulse Reconstruction Diagnostic for Single-Shot Measurement of Free Electron Laser Radiation Power </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Korten%2C+T">Till Korten</a>, <a href="/search/physics?searchtype=author&query=Rybnikov%2C+V">Vladimir Rybnikov</a>, <a href="/search/physics?searchtype=author&query=Steinbach%2C+P">Peter Steinbach</a>, <a href="/search/physics?searchtype=author&query=Mirian%2C+N">Najmeh Mirian</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="2411.17644v1-abstract-short" style="display: inline;"> Accurate characterization of radiation pulse profiles is crucial for optimizing beam quality and enhancing experimental outcomes in Free Electron Laser (FEL) research. In this paper, we present a novel approach that employs machine learning techniques for real-time virtual diagnostics of FEL radiation pulses. Our advanced artificial intelligence (AI)-based diagnostic tool utilizes longitudinal pha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.17644v1-abstract-full').style.display = 'inline'; document.getElementById('2411.17644v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.17644v1-abstract-full" style="display: none;"> Accurate characterization of radiation pulse profiles is crucial for optimizing beam quality and enhancing experimental outcomes in Free Electron Laser (FEL) research. In this paper, we present a novel approach that employs machine learning techniques for real-time virtual diagnostics of FEL radiation pulses. Our advanced artificial intelligence (AI)-based diagnostic tool utilizes longitudinal phase space data obtained from the X-band transverse deflecting structure to reconstruct the temporal profile of FEL pulses in real time. Unlike traditional single-shot methods, this AI-driven solution provides a non-invasive, highly efficient alternative for pulse characterization. By leveraging state-of-the-art machine learning models, our method facilitates precise single-shot measurements of FEL pulse power, offering significant advantages for FEL science research. This work outlines the conceptual framework, methodology, and validation results of our virtual diagnostic tool, demonstrating its potential to significantly impact FEL research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.17644v1-abstract-full').style.display = 'none'; document.getElementById('2411.17644v1-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> 26 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </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">9 pages , 5 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/2411.09468">arXiv:2411.09468</a> <span> [<a href="https://arxiv.org/pdf/2411.09468">pdf</a>, <a href="https://arxiv.org/format/2411.09468">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="Accelerator Physics">physics.acc-ph</span> </div> </div> <p class="title is-5 mathjax"> Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Korten%2C+T">Till Korten</a>, <a href="/search/physics?searchtype=author&query=Rybnikov%2C+V">Vladimir Rybnikov</a>, <a href="/search/physics?searchtype=author&query=Vogt%2C+M">Mathias Vogt</a>, <a href="/search/physics?searchtype=author&query=Roensch-Schulenburg%2C+J">Juliane Roensch-Schulenburg</a>, <a href="/search/physics?searchtype=author&query=Steinbach%2C+P">Peter Steinbach</a>, <a href="/search/physics?searchtype=author&query=Mirian%2C+N">Najmeh Mirian</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="2411.09468v2-abstract-short" style="display: inline;"> Electron beam accelerators are essential in many scientific and technological fields. Their operation relies heavily on the stability and precision of the electron beam. Traditional diagnostic techniques encounter difficulties in addressing the complex and dynamic nature of electron beams. Particularly in the context of free-electron lasers (FELs), it is fundamentally impossible to measure the las… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09468v2-abstract-full').style.display = 'inline'; document.getElementById('2411.09468v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.09468v2-abstract-full" style="display: none;"> Electron beam accelerators are essential in many scientific and technological fields. Their operation relies heavily on the stability and precision of the electron beam. Traditional diagnostic techniques encounter difficulties in addressing the complex and dynamic nature of electron beams. Particularly in the context of free-electron lasers (FELs), it is fundamentally impossible to measure the lasing-on and lasingoff electron power profiles for a single electron bunch. This is a crucial hurdle in the exact reconstruction of the photon pulse profile. To overcome this hurdle, we developed a machine learning model that predicts the temporal power profile of the electron bunch in the lasing-off regime using machine parameters that can be obtained when lasing is on. The model was statistically validated and showed superior predictions compared to the state-of-the-art batch calibrations. The work we present here is a critical element for a virtual pulse reconstruction diagnostic (VPRD) tool designed to reconstruct the power profile of individual photon pulses without requiring repeated measurements in the lasing-off regime. This promises to significantly enhance the diagnostic capabilities in FELs at large. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09468v2-abstract-full').style.display = 'none'; document.getElementById('2411.09468v2-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> 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </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, 4 figures, Machine Learning and the Physical Sciences Workshop, NeurIPS 2024 https://neurips.cc/virtual/2024/100009</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.14226">arXiv:2204.14226</a> <span> [<a href="https://arxiv.org/pdf/2204.14226">pdf</a>, <a href="https://arxiv.org/format/2204.14226">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</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="Computer Vision and Pattern Recognition">cs.CV</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="Medical Physics">physics.med-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/s41379-022-01147-y">10.1038/s41379-022-01147-y <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Recommendations on test datasets for evaluating AI solutions in pathology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Homeyer%2C+A">Andr茅 Homeyer</a>, <a href="/search/physics?searchtype=author&query=Gei%C3%9Fler%2C+C">Christian Gei脽ler</a>, <a href="/search/physics?searchtype=author&query=Schwen%2C+L+O">Lars Ole Schwen</a>, <a href="/search/physics?searchtype=author&query=Zakrzewski%2C+F">Falk Zakrzewski</a>, <a href="/search/physics?searchtype=author&query=Evans%2C+T">Theodore Evans</a>, <a href="/search/physics?searchtype=author&query=Strohmenger%2C+K">Klaus Strohmenger</a>, <a href="/search/physics?searchtype=author&query=Westphal%2C+M">Max Westphal</a>, <a href="/search/physics?searchtype=author&query=B%C3%BClow%2C+R+D">Roman David B眉low</a>, <a href="/search/physics?searchtype=author&query=Kargl%2C+M">Michaela Kargl</a>, <a href="/search/physics?searchtype=author&query=Karjauv%2C+A">Aray Karjauv</a>, <a href="/search/physics?searchtype=author&query=Munn%C3%A9-Bertran%2C+I">Isidre Munn茅-Bertran</a>, <a href="/search/physics?searchtype=author&query=Retzlaff%2C+C+O">Carl Orge Retzlaff</a>, <a href="/search/physics?searchtype=author&query=Romero-L%C3%B3pez%2C+A">Adri脿 Romero-L贸pez</a>, <a href="/search/physics?searchtype=author&query=So%C5%82tysi%C5%84ski%2C+T">Tomasz So艂tysi艅ski</a>, <a href="/search/physics?searchtype=author&query=Plass%2C+M">Markus Plass</a>, <a href="/search/physics?searchtype=author&query=Carvalho%2C+R">Rita Carvalho</a>, <a href="/search/physics?searchtype=author&query=Steinbach%2C+P">Peter Steinbach</a>, <a href="/search/physics?searchtype=author&query=Lan%2C+Y">Yu-Chia Lan</a>, <a href="/search/physics?searchtype=author&query=Bouteldja%2C+N">Nassim Bouteldja</a>, <a href="/search/physics?searchtype=author&query=Haber%2C+D">David Haber</a>, <a href="/search/physics?searchtype=author&query=Rojas-Carulla%2C+M">Mateo Rojas-Carulla</a>, <a href="/search/physics?searchtype=author&query=Sadr%2C+A+V">Alireza Vafaei Sadr</a>, <a href="/search/physics?searchtype=author&query=Kraft%2C+M">Matthias Kraft</a>, <a href="/search/physics?searchtype=author&query=Kr%C3%BCger%2C+D">Daniel Kr眉ger</a>, <a href="/search/physics?searchtype=author&query=Fick%2C+R">Rutger Fick</a> , et al. (5 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="2204.14226v1-abstract-short" style="display: inline;"> Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recom… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.14226v1-abstract-full').style.display = 'inline'; document.getElementById('2204.14226v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.14226v1-abstract-full" style="display: none;"> Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations for the collection of test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help regulatory agencies and end users verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.14226v1-abstract-full').style.display = 'none'; document.getElementById('2204.14226v1-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> 21 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Mod Pathol (2022) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1807.01498">arXiv:1807.01498</a> <span> [<a href="https://arxiv.org/pdf/1807.01498">pdf</a>, <a href="https://arxiv.org/format/1807.01498">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Space Physics">physics.space-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</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/10.1029/2018JA025802">10.1029/2018JA025802 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> VLF transmitters as tools for monitoring the plasmasphere </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Koronczay%2C+D">David Koronczay</a>, <a href="/search/physics?searchtype=author&query=Lichtenberger%2C+J">Janos Lichtenberger</a>, <a href="/search/physics?searchtype=author&query=Juhasz%2C+L">Lilla Juhasz</a>, <a href="/search/physics?searchtype=author&query=Steinbach%2C+P">Peter Steinbach</a>, <a href="/search/physics?searchtype=author&query=Hospodarsky%2C+G">George Hospodarsky</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="1807.01498v2-abstract-short" style="display: inline;"> Continuous burst mode VLF measurements were recorded on the RBSP/Van Allen Probes satellites and are analyzed to detect pulses from the Russian Alpha (RSDN-20) ground-based navigational system. Based on the wave characteristics of these pulses and on the position of the spacecraft, the signals propagated mostly in ducted mode in the plasmasphere. Knowledge of the propagation path allowed us to car… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.01498v2-abstract-full').style.display = 'inline'; document.getElementById('1807.01498v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1807.01498v2-abstract-full" style="display: none;"> Continuous burst mode VLF measurements were recorded on the RBSP/Van Allen Probes satellites and are analyzed to detect pulses from the Russian Alpha (RSDN-20) ground-based navigational system. Based on the wave characteristics of these pulses and on the position of the spacecraft, the signals propagated mostly in ducted mode in the plasmasphere. Knowledge of the propagation path allowed us to carry out a monochromatic wave propagation inversion to obtain plasmaspheric electron densities. We compared the obtained densities with independent in-situ measurements on the spacecraft. The results show good agreement, validating our inversion process. This contributes to validating the field-aligned density profile model routinely used in the inversion of whistlers detected on the ground. Furthermore, our method can provide electron densities at regimes where no alternative measurements are available on the spacecraft. This raises the possibility of using this method as an additional tool to measure and monitor plasmaspheric electron densities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.01498v2-abstract-full').style.display = 'none'; document.getElementById('1807.01498v2-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 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2018. </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">21 pages, 5 figures, accepted by Journal of Geophysical Research</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Journal of Geophysical Research: Space Physics, 123. (2018) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1505.04604">arXiv:1505.04604</a> <span> [<a href="https://arxiv.org/pdf/1505.04604">pdf</a>, <a href="https://arxiv.org/format/1505.04604">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="Software Engineering">cs.SE</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 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/10.1088/1742-6596/664/6/062048">10.1088/1742-6596/664/6/062048 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> How do particle physicists learn the programming concepts they need? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Kluth%2C+S">Stefan Kluth</a>, <a href="/search/physics?searchtype=author&query=Pia%2C+M+G">Maria Grazia Pia</a>, <a href="/search/physics?searchtype=author&query=Schoerner-Sadenius%2C+T">Thomas Schoerner-Sadenius</a>, <a href="/search/physics?searchtype=author&query=Steinbach%2C+P">Peter Steinbach</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="1505.04604v1-abstract-short" style="display: inline;"> The ability to read, use and develop code efficiently and successfully is a key ingredient in modern particle physics. We report the experience of a training program, identified as "Advanced Programming Concepts", that introduces software concepts, methods and techniques to work effectively on a daily basis in a HEP experiment or other programming intensive fields. This paper illustrates the princ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1505.04604v1-abstract-full').style.display = 'inline'; document.getElementById('1505.04604v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1505.04604v1-abstract-full" style="display: none;"> The ability to read, use and develop code efficiently and successfully is a key ingredient in modern particle physics. We report the experience of a training program, identified as "Advanced Programming Concepts", that introduces software concepts, methods and techniques to work effectively on a daily basis in a HEP experiment or other programming intensive fields. This paper illustrates the principles, motivations and methods that shape the "Advanced Computing Concepts" training program, the knowledge base that it conveys, an analysis of the feedback received so far, and the integration of these concepts in the software development process of the experiments as well as its applicability to a wider audience. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1505.04604v1-abstract-full').style.display = 'none'; document.getElementById('1505.04604v1-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> 18 May, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2015. </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">8 pages, 2 figures, CHEP2015 proceedings</span> 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