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Your search keyword '"Kowalkowski, Jim"' showing total 8 results
Start Over Author "Kowalkowski, Jim" Topic high energy physics - experiment (hep-ex)
8 results on '"Kowalkowski, Jim"'

1. Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline

Author

Wang, Chun-Yi, Ju, Xiangyang, Hsu, Shih-Chieh, Murnane, Daniel, Calafiura, Paolo, Farrell, Steven, Spiropulu, Maria, Vlimant, Jean-Roch, Aurisano, Adam, Hewes, V, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Atkinson, Markus, Neubauer, Mark, DeZoort, Gage, Thais, Savannah, Ballow, Alexandra, Lazar, Alina, Caillou, Sylvain, Rougier, Charline, Stark, Jan, Vallier, Alexis, and Sardain, Jad

Subjects
High Energy Physics - Experiment (hep-ex), History, High Energy Physics - Phenomenology, High Energy Physics - Phenomenology (hep-ph), Physics - Instrumentation and Detectors, Physics - Data Analysis, Statistics and Probability, FOS: Physical sciences, Instrumentation and Detectors (physics.ins-det), Data Analysis, Statistics and Probability (physics.data-an), Computer Science Applications, Education, High Energy Physics - Experiment
Abstract

Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt tracks from the collision points. However, they require dedicated configuration and additional computing time to efficiently reconstruct the large radius tracks created away from the collision points. We developed an end-to-end machine learning-based track finding algorithm for the HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic about global track positions. In this work, we study the performance of the Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in the event, the pipeline simultaneously reconstructs prompt tracks and large radius tracks with high efficiencies. This new capability offered by the Exa.TrkX pipeline may enable us to search for new physics in real time., 5 pages, 3 figures. Proceedings of 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research

Published
2022

2. Track Seeding and Labelling with Embedded-space Graph Neural Networks

Author

Choma, Nicholas, Murnane, Daniel, Ju, Xiangyang, Calafiura, Paolo, Conlon, Sean, Farrell, Steven, Prabhat, Prabhat, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Spentzouris, Panagiotis, Vlimant, Jean-Roch, Spiropulu, Maria, Adam Aurisano, Hewes, Jeremy, Tsaris, Aristeidis, Terao, Kazuhiro, and Usher, Tracy

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Physics - Instrumentation and Detectors, hep-ex, cs.LG, FOS: Physical sciences, Instrumentation and Detectors (physics.ins-det), Computational Physics (physics.comp-ph), Machine Learning (cs.LG), High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), physics.comp-ph, Physics - Computational Physics, physics.ins-det
Abstract

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding., Proceedings submission in Connecting the Dots Workshop 2020, 10 pages

Published
2020

3. Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Author

Ju, Xiangyang, Farrell, Steven, Calafiura, Paolo, Murnane, Daniel, Prabhat, Gray, Lindsey, Klijnsma, Thomas, Pedro, Kevin, Cerati, Giuseppe, Kowalkowski, Jim, Perdue, Gabriel, Spentzouris, Panagiotis, Tran, Nhan, Vlimant, Jean-Roch, Zlokapa, Alexander, Pata, Joosep, Spiropulu, Maria, An, Sitong, Adam Aurisano, Hewes, V., Hewes, Jeremy, Tsaris, Aristeidis, Terao, Kazuhiro, and Usher, Tracy

Subjects
Physics - Instrumentation and Detectors, hep-ex, Other Fields of Physics, FOS: Physical sciences, Instrumentation and Detectors (physics.ins-det), Computational Physics (physics.comp-ph), High Energy Physics - Experiment, physics.data-an, High Energy Physics - Experiment (hep-ex), physics.comp-ph, Physics - Data Analysis, Statistics and Probability, Detectors and Experimental Techniques, physics.ins-det, Physics - Computational Physics, Particle Physics - Experiment, Data Analysis, Statistics and Probability (physics.data-an)
Abstract

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems., Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical Sciences"

Published
2020

4. HEP Software Foundation Community White Paper Working Group - Data Processing Frameworks

Author

Calafiura, Paolo, Clemencic, Marco, Grasland, Hadrien, Green, Chris, Hegner, Benedikt, Jones, Chris, Jouvin, Michel, Knoepfel, Kyle, Kuhr, Thomas, Kowalkowski, Jim, Leggett, Charles, Lyon, Adam, Malon, David, Paterno, Marc, Patton, Simon, Sexton-Kennedy, Elizabeth, Stewart, Graeme A, Tsulaia, Vakho, Laboratoire de l'Accélérateur Linéaire (LAL), and Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)

Subjects
hep-ex, Other Fields of Physics, FOS: Physical sciences, Computational Physics (physics.comp-ph), programming, computer: network, High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), physics.comp-ph, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], [INFO]Computer Science [cs], data management, Physics - Computational Physics, activity report, Particle Physics - Experiment
Abstract

Data processing frameworks are an essential part of HEP experiments' software stacks. Frameworks provide a means by which code developers can undertake the essential tasks of physics data processing, accessing relevant inputs and storing their outputs, in a coherent way without needing to know the details of other domains. Frameworks provide essential core services for developers and help deliver a configurable working application to the experiments' production systems. Modern HEP processing frameworks are in the process of adapting to a new computing landscape dominated by parallel processing and heterogeneity, which pose many questions regarding enhanced functionality and scaling that must be faced without compromising the maintainability of the code. In this paper we identify a program of work that can help further clarify the key concepts of frameworks for HEP and then spawn R&D activities that can focus the community's efforts in the most efficient manner to address the challenges of the upcoming experimental program.

Published
2018

5. Machine Learning in High Energy Physics Community White Paper

Author

Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Anderson, John, Andrews, Michael, Espinosa, Juan Pedro Araque, Aurisano, Adam, Basara, Laurent, Bevan, Adrian, Bhimji, Wahid, Bonacorsi, Daniele, Burkle, Bjorn, Calafiura, Paolo, Campanelli, Mario, Capps, Louis, Carminati, Federico, Carrazza, Stefano, Chen, Yi-fan, Childers, Taylor, Coadou, Yann, Coniavitis, Elias, Cranmer, Kyle, David, Claire, Davis, Douglas, De Simone, Andrea, Duarte, Javier, Erdmann, Martin, Eschle, Jonas, Farbin, Amir, Feickert, Matthew, Castro, Nuno Filipe, Fitzpatrick, Conor, Floris, Michele, Forti, Alessandra, Garra-Tico, Jordi, Gemmler, Jochen, Girone, Maria, Glaysher, Paul, Gleyzer, Sergei, Gligorov, Vladimir, Golling, Tobias, Graw, Jonas, Gray, Lindsey, Greenwood, Dick, Hacker, Thomas, Harvey, John, Hegner, Benedikt, Heinrich, Lukas, Heintz, Ulrich, Hooberman, Ben, Junggeburth, Johannes, Kagan, Michael, Kane, Meghan, Kanishchev, Konstantin, Karpi��ski, Przemys��aw, Kassabov, Zahari, Kaul, Gaurav, Kcira, Dorian, Keck, Thomas, Klimentov, Alexei, Kowalkowski, Jim, Kreczko, Luke, Kurepin, Alexander, Kutschke, Rob, Kuznetsov, Valentin, K��hler, Nicolas, Lakomov, Igor, Lannon, Kevin, Lassnig, Mario, Limosani, Antonio, Louppe, Gilles, Mangu, Aashrita, Mato, Pere, Meenakshi, Narain, Meinhard, Helge, Menasce, Dario, Moneta, Lorenzo, Moortgat, Seth, Neubauer, Mark, Newman, Harvey, Otten, Sydney, Pabst, Hans, Paganini, Michela, Paulini, Manfred, Perdue, Gabriel, Perez, Uzziel, Picazio, Attilio, Pivarski, Jim, Prosper, Harrison, Psihas, Fernanda, Radovic, Alexander, Reece, Ryan, Rinkevicius, Aurelius, Rodrigues, Eduardo, Rorie, Jamal, Rousseau, David, Sauers, Aaron, Schramm, Steven, Schwartzman, Ariel, Severini, Horst, Seyfert, Paul, Siroky, Filip, Skazytkin, Konstantin, Sokoloff, Mike, Stewart, Graeme, Stienen, Bob, Stockdale, Ian, Strong, Giles, Sun, Wei, Thais, Savannah, Tomko, Karen, Upfal, Eli, Usai, Emanuele, Ustyuzhanin, Andrey, Vala, Martin, Vasel, Justin, Vallecorsa, Sofia, Verzetti, Mauro, Vilas��s-Cardona, Xavier, Vlimant, Jean-Roch, Vukotic, Ilija, Wang, Sean-Jiun, Watts, Gordon, Williams, Michael, Wu, Wenjing, Wunsch, Stefan, Yang, Kun, Zapata, Omar, Physics, Centre National de la Recherche Scientifique (CNRS), and Université Paris-Sud - Paris 11 (UP11)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, History, Particle physics, data analysis method, cs.LG, Other Fields of Physics, FOS: Physical sciences, Machine Learning (stat.ML), Machine learning, computer.software_genre, 01 natural sciences, programming, High Energy Physics - Experiment, Education, Machine Learning (cs.LG), High Energy Physics - Experiment (hep-ex), White paper, Resource (project management), Software, Statistics - Machine Learning, 0103 physical sciences, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], ddc:530, Applied research, [INFO]Computer Science [cs], Electronics, Mathematical Physics and Mathematics, 010306 general physics, activity report, [PHYS]Physics [physics], Large Hadron Collider, hep-ex, 010308 nuclear & particles physics, Event (computing), business.industry, Computational Physics (physics.comp-ph), artificial intelligence, stat.ML, Computing and Computers, Computer Science Applications, Identification (information), physics.comp-ph, Artificial intelligence, business, Physics - Computational Physics, computer, Particle Physics - Experiment
Abstract

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit., Comment: Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm

Published
2018

6. Novel deep learning methods for track reconstruction

Author

Farrell, Steven, Calafiura, Paolo, Mudigonda, Mayur, Prabhat, Anderson, Dustin, Vlimant, Jean-Roch, Zheng, Stephan, Bendavid, Josh, Spiropulu, Maria, Cerati, Giuseppe, Gray, Lindsey, Kowalkowski, Jim, Spentzouris, Panagiotis, and Tsaris, Aristeidis

Subjects
physics.data-an, High Energy Physics - Experiment (hep-ex), hep-ex, Physics - Data Analysis, Statistics and Probability, FOS: Physical sciences, Data Analysis, Statistics and Probability (physics.data-an), High Energy Physics - Experiment
Abstract

For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration from computer vision applications and operated on an image-like representation of tracking detector data. While these approaches have shown some promise, image-based methods face challenges in scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In contrast, models that can operate on the spacepoint representation of track measurements ("hits") can exploit the structure of the data to solve tasks efficiently. In this paper we will show two sets of new deep learning models for reconstructing tracks using space-point data arranged as sequences or connected graphs. In the first set of models, Recurrent Neural Networks (RNNs) are used to extrapolate, build, and evaluate track candidates akin to Kalman Filter algorithms. Such models can express their own uncertainty when trained with an appropriate likelihood loss function. The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification. These models read a graph of connected hits and compute features on the nodes and edges. They adaptively learn which hit connections are important and which are spurious. The models are scaleable with simple architecture and relatively few parameters. Results for all models will be presented on ACTS generic detector simulated data., Comment: CTD 2018 proceedings

Published
2018
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7. Grid-based minimization at scale: Feldman-Cousins corrections for SBN

Author

Schulz, Holger, Wospakrik, Marianette, Ross-Lonergan, Mark, Ge, Guanqun, Saba Sehrish, Paterno, Marc, Kowalkowski, Jim, Ketchum, Wes, and Karagiorgi, Georgia

Subjects
High Energy Physics - Experiment (hep-ex), FOS: Physical sciences, Computational Physics (physics.comp-ph), Physics - Computational Physics, High Energy Physics - Experiment
Abstract

We present a computational model for the construction of Feldman-Cousins (FC) corrections frequently used in High Energy Physics (HEP) analysis. The program contains a grid-based minimization and is written in C++. Our algorithms exploit vectorization through Eigen3, yielding a single-core speed-up of 350 compared to the original implementation, and achieve MPI data parallelism by using DIY. We demonstrate the application to scale very well at High Performance Computing (HPC) sites. We use HDF5 in conjunction with HighFive to write results of the calculation to file.

8. A Roadmap for HEP Software and Computing R&D for the 2020s

Author

Robert Currie, A.A. Alves, Armin Michael Nairz, A. Valassi, Hadrien Grasland, Nurcan Ozturk, Gianluca Cerminara, Davide Costanzo, Gabriel Perdue, G Lima, W. Pokorski, Renaud Vernet, Jürgen Reuter, Marian Babik, Glenn Patrick, Giacomo Govi, David Crooks, Sergio F Novaes, Mihaly Novak, Paul Laycock, Eric Lancon, J. R. G. Fernandez, Torben Ferber, Steven Farrell, Nicolo Magini, Josh Mcfayden, Sean Murray, Gordon Watts, B. K. Jashal, Isidro Gonzalez Caballero, Jacob Linacre, Frank Wuerthwein, Margaret Votava, Dirk Duellmann, Alessandra Forti, Peter van Gemmeren, Patricia Mendez Lorenzo, Jan Iven, Giovanni Franzoni, Charles Leggett, Sw. Banerjee, T. Kuhr, Markus Schulz, Alberto Ribon, Christopher Tunnell, Valentin Kuznetsov, Roger Jones, Frank Winklmeier, Claudio Grandi, Peter Elmer, Sünje Dallmeier-Tiessen, Gordon Stewart, Giordon Stark, Benedikt Hegner, Giuseppe Bagliesi, Catrin Bernius, Burt Holzman, Nguyen Anh-Ky, Alex Pearce, Gancho Dimitrov, Ian Bird, H. Schellman, Scott Snyder, Gloria Corti, Walter Lampl, Edward Moyse, E. Cogneras, Alexei Klimentov, Frederic Derue, Taylor Childers, K. Genser, J. Cranshaw, Manuel Giffels, Andrew Buckley, Shawn McKee, Marilena Bandieramonte, C. Bozzi, Adam L. Lyon, Heather Gray, Oliver Keeble, Todor Ivanov, Vakhtang Tsulaia, Daniel S. Katz, Gerhard Raven, Renato Santana, Agnieszka Dziurda, Marco Clemencic, Borut Paul Kerševan, Philip Chang, Johannes Elmsheuser, Carl Vuosalo, Gian Piero Siroli, Javier Cervantes Villanueva, Daniele Bonacorsi, Mark Hodgkinson, Vladimir Gligorov, Graeme A. Stewart, L. Grillo, Jakob Blomer, Markus Klute, Kevin Pedro, Michela Paganini, Sabine Crépé-Renaudin, Marko Bracko, G. Cosmo, Eduardo Rodrigues, Dmitri Konstantinov, Kaushik De, Jose Benito Gonzalez Lopez, Oliver Gutsche, Miron Livny, Farah Hariri, Teng Li, Laurent Duflot, U. Tamponi, Alaettin Serhan Mete, Jose M Hernandez, Frank Jm Geurts, Ulrik Egede, Jose Flix, Richard Mount, G. Andronico, Dario Menasce, David Lange, Steven Schramm, Vardan Gyurjyan, Lukas Heinrich, Maria Girone, Dorian Kcira, Giulio Eulisse, Alberto Sánchez-Hernández, Francesco Polci, Antonio Boveia, T. J. Smith, Peter Clarke, Ivan Glushkov, Stewart Martin-Haugh, Jeff Templon, H. Meinhard, James Robinson, David Colling, Jean-Roch Vlimant, Michael Kagan, Luca Atzori, Riccardo-Maria Bianchi, Douglas Benjamin, Kyle Cranmer, S. Fuess, Andre Sailer, Sofia Vallecorsa, Gianpaolo Carlino, Torre Wenaus, Gang Chen, Salvatore Di Guida, Soon Yung Jun, Tomas Lindén, Giacomo Tenaglia, Grigori Rybkine, Michael H. Seymour, Albert De Roeck, Simone Campana, Mario Lassnig, Andrea Sartirana, B. Viren, Andrea Formica, Leo Piilonen, John Harvey, Birgit Lewendel, Panagiotis Spentzouris, Sally Seidel, Andrea Sciabà, Stefano Belforte, Dave Dykstra, Leonardo Cristella, Douglas Gingrich, Lorenzo Moneta, Gerardo Ganis, Lorenzo Rinaldi, Meghan Kane, Zhang Xiao-Mei, John Apostolakis, Bodhitha Jayatilaka, M. D. Sokoloff, Thomas Hauth, Carlos Vázquez Sierra, Laurent Bernard Aphecetche, Xingtao Huang, Kajari Mazumdar, R. Mehdiyev, Antonio María Pérez-Calero Yzquierdo, Thomas McCauley, Mike Williams, M. S. Neubauer, Michael Hildreth, Stefan Roiser, Stefano Giagu, Peter Zahariev Hristov, Anyes Taffard, A. Petzold, Martin Barisits, Edward Karavakis, Jing-Ge Shiu, Alessandro Di Girolamo, Jim Kowalkowski, Andreas Heiss, Tai Sakuma, V. Daniel Elvira, Jeremy Coles, Davide Sgalaberna, Johannes Albrecht, James Chapman, Tomasz Bold, Antonio Delgado Peris, Robert Gardner, Andrew McNab, Christoph Wissing, Romain Wartel, Sandro Christian Wenzel, Ian Fisk, Vladimir Ivanchenko, Ian Collier, Peter Love, Andres Pacheco Pages, Jim Pivarski, Edoardo Martelli, Vincent R. Pascuzzi, Michel Jouvin, Hartmut Stadie, Makoto Asai, Albert Puig Navarro, Gunter Quast, Troels Petersen, Wahid Bhimji, Brian Bockelman, Andrej Filipcic, B. Pearson, Stefan Hoeche, Luca Perrozzi, Jaroslava Schovancova, Z. Marshall, Marco Cattaneo, Vincent Garonne, Hannah Short, Danilo Piparo, Liviu Valsan, Lothar A. T. Bauerdick, Gareth Roy, Horst Severini, A. Oyanguren, Francesco Giacomini, Kenneth Bloom, A. Gellrich, Marko Petrič, Efe Yazgan, Samuel Cadellin Skipsey, Andrea Dotti, W. Gohn, Wen Guan, Tommaso Boccali, C. Biscarat, Katarzyna Maria Dziedziniewicz-Wojcik, L. Kreczko, Simon George, Arturo Sánchez, Philippe Canal, Illya Shapoval, Caterina Doglioni, Ilya Komarov, Nuno Filipe Castro, Patrick Koppenburg, Jérôme Pansanel, Pere Mato, Wei Yang, Paolo Calafiura, Sezen Sekmen, C. Voss, David Britton, Thomas Hartmann, Guilherme Amadio, Andrew Norman, Michel De Cian, James Letts, Sebastian Lopienski, David Rousseau, Andrew Hanushevsky, Jamie Shiers, Andrei Gheata, Martin Ritter, Karolos Potamianos, Elizabeth Sexton-Kennedy, Michele Michelotto, Luca Canali, Cedric Serfon, Beate Heinemann, Giuseppe Lo Presti, J. Piedra, Fernanda Psihas, Ben Couturier, Robert K. Kutschke, Jovan Mitrevski, Charles William Kalderon, Benjamin Wynne, B. Morgan, Frank Gaede, Mihaela Gheata, Michael H Kirby, P. Buncic, Eric Wayne Vaandering, James Frost, Conor Fitzpatrick, Laboratoire de physique subatomique et des technologies associées (SUBATECH), Université de Nantes - Faculté des Sciences et des Techniques, Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Clermont Auvergne (UCA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'Accélérateur Linéaire (LAL), Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Leprince-Ringuet (LLR), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Centre de Calcul de l'IN2P3 (CC-IN2P3), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), HEP Software Foundation, Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Nantes (UN)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Nantes (UN)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Argonne National Laboratory, Brookhaven National Laboratory, Centro Brasileiro de Pesquisas Físicas, Centre de Calcul de l’IN2P3, University of Wisconsin-Madison, Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Cinvestav, INFN, Center for High Performance Computing, California Institute of Technology, Cornell University, Deutsches Elektronen-Synchrotron, École Polytechnique Fédérale de Lausanne (EPFL), ETH Zürich-Institute for Particle Physics and Astrophysics (IPA), Fermi National Accelerator Laboratory, University of Hamburg, Helsinki Institute of Physics, International Center for Theoretical Physics, Institut de Física d’Altes Energies and Departament de Física de la Universitat Autònoma de Barcelona and ICREA, CSIC-Universidad de Cantabria, Centro Mixto Universidad de Valencia-CSIC, Chinese Academy of Sciences, Indiana University, Università di Bologna, Politecnico di Bari, Università di Catania, Università e INFN Ferrara, INFN Sezione di Genova, INFN Sezione di Milano-Bicocca, Università di Napoli, Scuola Normale Superiore di Pisa, Università di Padova b, Università La Sapienza, Università di Trieste, INFN Sezione di Torino, IPHC UMR 7178, Imperial College London, CEA Saclay (Commissariat à l’Énergie Atomique), Newport News, Jožef Stefan Institute, Karlsruhe Institute of Technology, Faculty of Physics and Applied Computer Science, Kyungpook National University, Université Paris-Sud and CNRS/IN2P3, Lawrence Berkeley National Laboratory and University of California, Laboratório de Instrumentação e Física Experimental de Partículas (LIP), Université Paris-Saclay, Ludwig-Maximilians-Universität München, CNRS/IN2P3, Institut National Polytechnique de Grenoble, Massachusetts Institute of Technology, Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), University of Copenhagen, Nikhef National Institute for Subatomic Physics, New York University, University of Oregon, Universitat Autònoma de Barcelona (UAB), High Energy Physics (IHEP), STFC Rutherford Appleton Laboratory, Royal Holloway University of London, SLAC National Accelerator Laboratory, Simons Foundation, SoundCloud, CNRS-IN2P3, National Taiwan University, Tata Institute of Fundamental Research, Technische Universität Dortmund, University of Alberta, University of Arizona, University of Bristol, University of British Columbia, University of California Irvine, San Diego, University of Cambridge, University of Cape Town, Carleton University, University of Illinois Urbana-Champaign, University of Chicago, University of Cincinnati, Duke University, University of Edinburgh, Gangneung-Wonju National University, Université de Genève, University of Glasgow, Ruprecht-Karls-Universitat Heidelberg, University of Kentucky, Lancaster University, Jožef Stefan Institute and University of Ljubljana, Lunds Universitet, University of Manchester, University of Maribor, University of Massachusetts, The University of Michigan, Universidade do Minho, University of Nebraska-Lincoln, University of New Mexico, University of Notre Dame, University of Oklahoma, The Ohio State University, University of Oslo, Universidad de Oviedo, Oxford University, University of Pittsburgh, Princeton University, Rice University, Universidade Estadual Paulista (UNESP), University of Washington, Shandong University, University of Sheffield, University of Sofia, The University of Texas at Arlington, National Research Tomsk Polytechnic University, University of Toronto, Università di Udine, University of Warwick, Yale University, Universität Zürich, Vietnam Academy of Science and Technology (VAST), Virginia Tech, Vrije Universiteit Amsterdam, CERN, Albrecht, Johanne, Alves, Antonio Augusto, Amadio, Guilherme, Andronico, Giuseppe, Anh-Ky, Nguyen, Aphecetche, Laurent, Apostolakis, John, Asai, Makoto, Atzori, Luca, Babik, Marian, Bagliesi, Giuseppe, Bandieramonte, Marilena, Banerjee, Sunanda, Barisits, Martin, Bauerdick, Lothar A. T., Belforte, Stefano, Benjamin, Dougla, Bernius, Catrin, Bhimji, Wahid, Bianchi, Riccardo Maria, Bird, Ian, Biscarat, Catherine, Blomer, Jakob, Bloom, Kenneth, Boccali, Tommaso, Bockelman, Brian, Bold, Tomasz, Bonacorsi, Daniele, Boveia, Antonio, Bozzi, Concezio, Bracko, Marko, Britton, David, Buckley, Andy, Buncic, Predrag, Calafiura, Paolo, Campana, Simone, Canal, Philippe, Canali, Luca, Carlino, Gianpaolo, Castro, Nuno, Cattaneo, Marco, Cerminara, Gianluca, Cervantes Villanueva, Javier, Chang, Philip, Chapman, John, Chen, Gang, Childers, Taylor, Clarke, Peter, Clemencic, Marco, Cogneras, Eric, Coles, Jeremy, Collier, Ian, Colling, David, Corti, Gloria, Cosmo, Gabriele, Costanzo, Davide, Couturier, Ben, Cranmer, Kyle, Cranshaw, Jack, Cristella, Leonardo, Crooks, David, Crépé-Renaudin, Sabine, Currie, Robert, Dallmeier-Tiessen, Sünje, De, Kaushik, De Cian, Michel, De Roeck, Albert, Delgado Peris, Antonio, Derue, Frédéric, Di Girolamo, Alessandro, Di Guida, Salvatore, Dimitrov, Gancho, Doglioni, Caterina, Dotti, Andrea, Duellmann, Dirk, Duflot, Laurent, Dykstra, Dave, Dziedziniewicz-Wojcik, Katarzyna, Dziurda, Agnieszka, Egede, Ulrik, Elmer, Peter, Elmsheuser, Johanne, Elvira, V. Daniel, Eulisse, Giulio, Farrell, Steven, Ferber, Torben, Filipcic, Andrej, Fisk, Ian, Fitzpatrick, Conor, Flix, José, Formica, Andrea, Forti, Alessandra, Franzoni, Giovanni, Frost, Jame, Fuess, Stu, Gaede, Frank, Ganis, Gerardo, Gardner, Robert, Garonne, Vincent, Gellrich, Andrea, Genser, Krzysztof, George, Simon, Geurts, Frank, Gheata, Andrei, Gheata, Mihaela, Giacomini, Francesco, Giagu, Stefano, Giffels, Manuel, Gingrich, Dougla, Girone, Maria, Gligorov, Vladimir V., Glushkov, Ivan, Gohn, Wesley, Gonzalez Lopez, Jose Benito, González Caballero, Isidro, González Fernández, Juan R., Govi, Giacomo, Grandi, Claudio, Grasland, Hadrien, Gray, Heather, Grillo, Lucia, Guan, Wen, Gutsche, Oliver, Gyurjyan, Vardan, Hanushevsky, Andrew, Hariri, Farah, Hartmann, Thoma, Harvey, John, Hauth, Thoma, Hegner, Benedikt, Heinemann, Beate, Heinrich, Luka, Heiss, Andrea, Hernández, José M., Hildreth, Michael, Hodgkinson, Mark, Hoeche, Stefan, Holzman, Burt, Hristov, Peter, Huang, Xingtao, Ivanchenko, Vladimir N., Ivanov, Todor, Iven, Jan, Jashal, Brij, Jayatilaka, Bodhitha, Jones, Roger, Jouvin, Michel, Jun, Soon Yung, Kagan, Michael, Kalderon, Charles William, Kane, Meghan, Karavakis, Edward, Katz, Daniel S., Kcira, Dorian, Keeble, Oliver, Kersevan, Borut Paul, Kirby, Michael, Klimentov, Alexei, Klute, Marku, Komarov, Ilya, Konstantinov, Dmitri, Koppenburg, Patrick, Kowalkowski, Jim, Kreczko, Luke, Kuhr, Thoma, Kutschke, Robert, Kuznetsov, Valentin, Lampl, Walter, Lancon, Eric, Lange, David, Lassnig, Mario, Laycock, Paul, Leggett, Charle, Letts, Jame, Lewendel, Birgit, Li, Teng, Lima, Guilherme, Linacre, Jacob, Linden, Toma, Livny, Miron, Lo Presti, Giuseppe, Lopienski, Sebastian, Love, Peter, Lyon, Adam, Magini, Nicolò, Marshall, Zachary L., Martelli, Edoardo, Martin-Haugh, Stewart, Mato, Pere, Mazumdar, Kajari, McCauley, Thoma, McFayden, Josh, McKee, Shawn, McNab, Andrew, Mehdiyev, Rashid, Meinhard, Helge, Menasce, Dario, Mendez Lorenzo, Patricia, Mete, Alaettin Serhan, Michelotto, Michele, Mitrevski, Jovan, Moneta, Lorenzo, Morgan, Ben, Mount, Richard, Moyse, Edward, Murray, Sean, Nairz, Armin, Neubauer, Mark S., Norman, Andrew, Novaes, Sérgio, Novak, Mihaly, Oyanguren, Arantza, Ozturk, Nurcan, Pacheco Pages, Andre, Paganini, Michela, Pansanel, Jerome, Pascuzzi, Vincent R., Patrick, Glenn, Pearce, Alex, Pearson, Ben, Pedro, Kevin, Perdue, Gabriel, Perez-Calero Yzquierdo, Antonio, Perrozzi, Luca, Petersen, Troel, Petric, Marko, Petzold, Andrea, Piedra, Jónatan, Piilonen, Leo, Piparo, Danilo, Pivarski, Jim, Pokorski, Witold, Polci, Francesco, Potamianos, Karolo, Psihas, Fernanda, Puig Navarro, Albert, Quast, Günter, Raven, Gerhard, Reuter, Jürgen, Ribon, Alberto, Rinaldi, Lorenzo, Ritter, Martin, Robinson, Jame, Rodrigues, Eduardo, Roiser, Stefan, Rousseau, David, Roy, Gareth, Rybkine, Grigori, Sailer, Andre, Sakuma, Tai, Santana, Renato, Sartirana, Andrea, Schellman, Heidi, Schovancová, Jaroslava, Schramm, Steven, Schulz, Marku, Sciabà, Andrea, Seidel, Sally, Sekmen, Sezen, Serfon, Cedric, Severini, Horst, Sexton-Kennedy, Elizabeth, Seymour, Michael, Sgalaberna, Davide, Shapoval, Illya, Shiers, Jamie, Shiu, Jing-Ge, Short, Hannah, Siroli, Gian Piero, Skipsey, Sam, Smith, Tim, Snyder, Scott, Sokoloff, Michael D., Spentzouris, Panagioti, Stadie, Hartmut, Stark, Giordon, Stewart, Gordon, Stewart, Graeme A., Sánchez, Arturo, Sánchez-Hernández, Alberto, Taffard, Anye, Tamponi, Umberto, Templon, Jeff, Tenaglia, Giacomo, Tsulaia, Vakhtang, Tunnell, Christopher, Vaandering, Eric, Valassi, Andrea, Vallecorsa, Sofia, Valsan, Liviu, Van Gemmeren, Peter, Vernet, Renaud, Viren, Brett, Vlimant, Jean-Roch, Voss, Christian, Votava, Margaret, Vuosalo, Carl, Vázquez Sierra, Carlo, Wartel, Romain, Watts, Gordon T., Wenaus, Torre, Wenzel, Sandro, Williams, Mike, Winklmeier, Frank, Wissing, Christoph, Wuerthwein, Frank, Wynne, Benjamin, Xiaomei, Zhang, Yang, Wei, Yazgan, Efe, and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Nuclear and High Energy Physics, HEP, software, computing, big science, HL-LHC, Ciências Naturais::Ciências Físicas, Process (engineering), Computer science, Data management, Ciências Físicas [Ciências Naturais], Other Fields of Physics, FOS: Physical sciences, Software performance testing, Computing & software upgrade, Software performance, 01 natural sciences, programming, High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), White paper, Software, 0103 physical sciences, Machine learning, Computer Science (miscellaneous), [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], [INFO]Computer Science [cs], 010306 general physics, activity report, Complement (set theory), Particle physics, HL-LHC, computing & software upgrade, software performance, machine learning, hep-ex, 010308 nuclear & particles physics, business.industry, Computational Physics (physics.comp-ph), 113 Computer and information sciences, Investment (macroeconomics), Upgrade, physics.comp-ph, data management, ddc:004, business, Software engineering, Physics - Computational Physics, Particle Physics - Experiment
Abstract

Computing and software for big science 3(1), 7 (2019). doi:10.1007/s41781-018-0018-8, Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade., Published by Springer International Publishing, Cham, Switzerland

Published
2019

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