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1. SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?

Author

Yang, John, Jimenez, Carlos E., Zhang, Alex L., Lieret, Kilian, Yang, Joyce, Wu, Xindi, Press, Ori, Muennighoff, Niklas, Synnaeve, Gabriel, Narasimhan, Karthik R., Yang, Diyi, Wang, Sida I., and Press, Ofir

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Software Engineering
Abstract

Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual elements such as images. This limited coverage motivates our inquiry into how existing systems might perform on unrepresented software engineering domains (e.g., front-end, game development, DevOps), which use different programming languages and paradigms. Therefore, we propose SWE-bench Multimodal (SWE-bench M), to evaluate systems on their ability to fix bugs in visual, user-facing JavaScript software. SWE-bench M features 617 task instances collected from 17 JavaScript libraries used for web interface design, diagramming, data visualization, syntax highlighting, and interactive mapping. Each SWE-bench M task instance contains at least one image in its problem statement or unit tests. Our analysis finds that top-performing SWE-bench systems struggle with SWE-bench M, revealing limitations in visual problem-solving and cross-language generalization. Lastly, we show that SWE-agent's flexible language-agnostic features enable it to substantially outperform alternatives on SWE-bench M, resolving 12% of task instances compared to 6% for the next best system.

Published
2024

2. EnIGMA: Enhanced Interactive Generative Model Agent for CTF Challenges

Author

Abramovich, Talor, Udeshi, Meet, Shao, Minghao, Lieret, Kilian, Xi, Haoran, Milner, Kimberly, Jancheska, Sofija, Yang, John, Jimenez, Carlos E., Khorrami, Farshad, Krishnamurthy, Prashanth, Dolan-Gavitt, Brendan, Shafique, Muhammad, Narasimhan, Karthik, Karri, Ramesh, and Press, Ofir

Subjects
Computer Science - Artificial Intelligence
Abstract

Although language model (LM) agents are demonstrating growing potential in many domains, their success in cybersecurity has been limited due to simplistic design and the lack of fundamental features for this domain. We present EnIGMA, an LM agent for autonomously solving Capture The Flag (CTF) challenges. EnIGMA introduces new Agent-Computer Interfaces (ACIs) to improve the success rate on CTF challenges. We establish the novel Interactive Agent Tool concept, which enables LM agents to run interactive command-line utilities essential for these challenges. Empirical analysis of EnIGMA on over 350 CTF challenges from three different benchmarks indicates that providing a robust set of new tools with demonstration of their usage helps the LM solve complex problems and achieves state-of-the-art results on the NYU CTF and Intercode-CTF benchmarks. Finally, we discuss insights on ACI design and agent behavior on cybersecurity tasks that highlight the need to adapt real-world tools for LM agents.

Published
2024

3. SciCode: A Research Coding Benchmark Curated by Scientists

Author

Tian, Minyang, Gao, Luyu, Zhang, Shizhuo Dylan, Chen, Xinan, Fan, Cunwei, Guo, Xuefei, Haas, Roland, Ji, Pan, Krongchon, Kittithat, Li, Yao, Liu, Shengyan, Luo, Di, Ma, Yutao, Tong, Hao, Trinh, Kha, Tian, Chenyu, Wang, Zihan, Wu, Bohao, Xiong, Yanyu, Yin, Shengzhu, Zhu, Minhui, Lieret, Kilian, Lu, Yanxin, Liu, Genglin, Du, Yufeng, Tao, Tianhua, Press, Ofir, Callan, Jamie, Huerta, Eliu, and Peng, Hao

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Since language models (LMs) now outperform average humans on many challenging tasks, it has become increasingly difficult to develop challenging, high-quality, and realistic evaluations. We address this issue by examining LMs' capabilities to generate code for solving real scientific research problems. Incorporating input from scientists and AI researchers in 16 diverse natural science sub-fields, including mathematics, physics, chemistry, biology, and materials science, we created a scientist-curated coding benchmark, SciCode. The problems in SciCode naturally factorize into multiple subproblems, each involving knowledge recall, reasoning, and code synthesis. In total, SciCode contains 338 subproblems decomposed from 80 challenging main problems. It offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. Claude3.5-Sonnet, the best-performing model among those tested, can solve only 4.6% of the problems in the most realistic setting. We believe that SciCode demonstrates both contemporary LMs' progress towards becoming helpful scientific assistants and sheds light on the development and evaluation of scientific AI in the future., Comment: 25 pages, 9 figures, 7 tables

Published
2024

4. SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

Author

Yang, John, Jimenez, Carlos E., Wettig, Alexander, Lieret, Kilian, Yao, Shunyu, Narasimhan, Karthik, and Press, Ofir

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Language model (LM) agents are increasingly being used to automate complicated tasks in digital environments. Just as humans benefit from powerful software applications, such as integrated development environments, for complex tasks like software engineering, we posit that LM agents represent a new category of end users with their own needs and abilities, and would benefit from specially-built interfaces to the software they use. We investigate how interface design affects the performance of language model agents. As a result of this exploration, we introduce SWE-agent: a system that facilitates LM agents to autonomously use computers to solve software engineering tasks. SWE-agent's custom agent-computer interface (ACI) significantly enhances an agent's ability to create and edit code files, navigate entire repositories, and execute tests and other programs. We evaluate SWE-agent on SWE-bench and HumanEvalFix, achieving state-of-the-art performance on both with a pass@1 rate of 12.5% and 87.7%, respectively, far exceeding the previous state-of-the-art achieved with non-interactive LMs. Finally, we provide insight on how the design of the ACI can impact agents' behavior and performance., Comment: Code, data, and demo available at https://swe-agent.com

Published
2024

5. High Pileup Particle Tracking with Object Condensation

Author

Lieret, Kilian, DeZoort, Gage, Chatterjee, Devdoot, Park, Jian, Miao, Siqi, and Li, Pan

Subjects
Physics - Data Analysis, Statistics and Probability, Computer Science - Machine Learning, High Energy Physics - Experiment
Abstract

Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking while improving scalability to meet the computing challenges posed by the HL-LHC. Most GNN tracking algorithms are based on edge classification and identify tracks as connected components from an initial graph containing spurious connections. In this talk, we consider an alternative based on object condensation (OC), a multi-objective learning framework designed to cluster points (hits) belonging to an arbitrary number of objects (tracks) and regress the properties of each object. Building on our previous results, we present a streamlined model and show progress toward a one-shot OC tracking algorithm in a high-pileup environment., Comment: 8 pages, 6 figures, 8th International Connecting The Dots Workshop (Toulouse 2023)

Published
2023

6. An Object Condensation Pipeline for Charged Particle Tracking at the High Luminosity LHC

Author

Lieret, Kilian and DeZoort, Gage

Subjects
Physics - Data Analysis, Statistics and Probability, High Energy Physics - Experiment
Abstract

Recent work has demonstrated that graph neural networks (GNNs) trained for charged particle tracking can match the performance of traditional algorithms while improving scalability to prepare for the High Luminosity LHC experiment. Most approaches are based on the edge classification (EC) paradigm, wherein tracker hits are connected by edges, and a GNN is trained to prune edges, resulting in a collection of connected components representing tracks. These connected components are usually collected by a clustering algorithm and the resulting hit clusters are passed to downstream modules that may assess track quality or fit track parameters. In this work, we consider an alternative approach based on object condensation (OC), a multi-objective learning framework designed to cluster points belonging to an arbitrary number of objects, in this context tracks, and regress the properties of each object. We demonstrate that OC shows very promising results when applied to the pixel detector of the trackML dataset and can, in some cases, recover tracks that are not reconstructable when relying on the output of an EC alone. The results have been obtained with a modular and extensible open-source implementation that allows us to efficiently train and evaluate the performance of various OC architectures and related approaches., Comment: 8 pages, 5 figures. 26th International Conference on Computing in High Energy & Nuclear Physics (CHEP23)

Published
2023

7. Second Analysis Ecosystem Workshop Report

Author

Aly, Mohamed, Burzynski, Jackson, Cardwell, Bryan, Craik, Daniel C., van Daalen, Tal, Dado, Tomas, Das, Ayanabha, Peris, Antonio Delgado, Doglioni, Caterina, Elmer, Peter, Eren, Engin, Eriksen, Martin B., Eschle, Jonas, Eulisse, Giulio, Fitzpatrick, Conor, Molina, José Flix, Forti, Alessandra, Galewsky, Ben, Gasiorowski, Sean, Goel, Aman, Gouskos, Loukas, Guiraud, Enrico, Gupta, Kanhaiya, Hageboeck, Stephan, Hall, Allison Reinsvold, Heinrich, Lukas, Held, Alexander, Hernández, José M., Villanueva, Michel Hernández, Hrivnac, Julius, Jouvin, Michel, Khoo, Teng Jian, Kreczko, Luke, Krumnack, Nils, Kuhr, Thomas, Kundu, Baidyanath, Lancon, Eric, Lange, Johannes, Laycock, Paul, Lieret, Kilian, Manganelli, Nicholas J., Villa, Pere Mato, Novak, Andrzej, Yzquierdo, Antonio Perez-Calero, Pivarski, Jim, Proffitt, Mason, Rembser, Jonas, Rodrigues, Eduardo, Rybkin, Grigori, Schaarschmidt, Jana, Schreiner, Henry F., Schulz, Markus, Sciabà, Andrea, Sekmen, Sezen, Sexton-Kennedy, Elizabeth, Shadura, Oksana, Simko, Tibor, Simpson, Nathan, Singh, Jaydip, Skidmore, Nicola, Smith, Nicholas, Sokoloff, Michael, Stewart, Graeme A., Strong, Giles C., Unel, Gokhan, Vassilev, Vassil, Waterlaat, Mark, Watts, Gordon, and Yazgan, Efe

Subjects
High Energy Physics - Experiment
Abstract

The second workshop on the HEP Analysis Ecosystem took place 23-25 May 2022 at IJCLab in Orsay, to look at progress and continuing challenges in scaling up HEP analysis to meet the needs of HL-LHC and DUNE, as well as the very pressing needs of LHC Run 3 analysis. The workshop was themed around six particular topics, which were felt to capture key questions, opportunities and challenges. Each topic arranged a plenary session introduction, often with speakers summarising the state-of-the art and the next steps for analysis. This was then followed by parallel sessions, which were much more discussion focused, and where attendees could grapple with the challenges and propose solutions that could be tried. Where there was significant overlap between topics, a joint discussion between them was arranged. In the weeks following the workshop the session conveners wrote this document, which is a summary of the main discussions, the key points raised and the conclusions and outcomes. The document was circulated amongst the participants for comments before being finalised here.

Published
2022
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8. Software Training in HEP

Author

Malik, Sudhir, Meehan, Samuel, Lieret, Kilian, Evans, Meirin Oan, Villanueva, Michel H., Katz, Daniel S., Stewart, Graeme A., Elmer, Peter, Aziz, Sizar, Bellis, Matthew, Bianchi, Riccardo Maria, Bianco, Gianluca, Bonilla, Johan Sebastian, Burger, Angela, Burzynski, Jackson, Chamont, David, Feickert, Matthew, Gadow, Philipp, Gruber, Bernhard Manfred, Guest, Daniel, Hageboeck, Stephan, Heinrich, Lukas, Horzela, Maximilian M., Huwiler, Marc, Lange, Clemens, Lehmann, Konstantin, Li, Ke, Majumder, Devdatta, Mamužić, Judita, Nelson, Kevin, Newhouse, Robin, Nibigira, Emery, Norberg, Scarlet, Pineda, Arturo Sánchez, Proffitt, Mason, Regnery, Brendan, Roepe, Amber, Roiser, Stefan, Schreiner, Henry, Shadura, Oksana, Stark, Giordon, Swatman, Stephen Nicholas, Thais, Savannah, Valassi, Andrea, Wunsch, Stefan, Yakobovitch, David, and Yuan, Siqi

Subjects
High Energy Physics - Experiment, Physics - Physics Education, HEP, software, training
Abstract

Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020s this will only become increasingly relevant throughout this decade. Meeting this sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g. Unix, version control,C++, continuous integration). The second is knowledge of domain specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving more specialized techniques. These include parallel programming, machine learning and data science tools, and techniques to preserve software projects at all scales. This paper dis-cusses the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP). The program equips participants with an array of software skills that serve as ingredients from which solutions to the computing challenges of HEP can be formed. Beyond serving the community by ensuring that members are able to pursue research goals, this program serves individuals by providing intellectual capital and transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP, Comment: For CHEP 2021 conference,sent for publication to CSBS Springer

Published
2021
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9. Software Sustainability & High Energy Physics

Author

Katz, Daniel S., Malik, Sudhir, Neubauer, Mark S., Stewart, Graeme A., Assamagan, Kétévi A., Becker, Erin A., Hong, Neil P. Chue, Cosden, Ian A., Meehan, Samuel, Moyse, Edward J. W., Price-Whelan, Adrian M., Sexton-Kennedy, Elizabeth, Evans, Meirin Oan, Feickert, Matthew, Lange, Clemens, Lieret, Kilian, Quick, Rob, Pineda, Arturo Sánchez, and Tunnell, Christopher

Subjects
High Energy Physics - Experiment, Computer Science - Software Engineering
Abstract

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)., Comment: A report from the "Sustainable Software in HEP" IRIS-HEP blueprint workshop: https://indico.cern.ch/event/930127/

Published
2020
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10. HL-LHC Computing Review: Common Tools and Community Software

Author

Foundation, HEP Software, Aarrestad, Thea, Amoroso, Simone, Atkinson, Markus Julian, Bendavid, Joshua, Boccali, Tommaso, Bocci, Andrea, Buckley, Andy, Cacciari, Matteo, Calafiura, Paolo, Canal, Philippe, Carminati, Federico, Childers, Taylor, Ciulli, Vitaliano, Corti, Gloria, Costanzo, Davide, Dezoort, Justin Gage, Doglioni, Caterina, Duarte, Javier Mauricio, Dziurda, Agnieszka, Elmer, Peter, Elsing, Markus, Elvira, V. Daniel, Eulisse, Giulio, Menendez, Javier Fernandez, Fitzpatrick, Conor, Frederix, Rikkert, Frixione, Stefano, Genser, Krzysztof L, Gheata, Andrei, Giuli, Francesco, Gligorov, Vladimir V., Grasland, Hadrien Benjamin, Gray, Heather, Gray, Lindsey, Grohsjean, Alexander, Gütschow, Christian, Hageboeck, Stephan, Harris, Philip Coleman, Hegner, Benedikt, Heinrich, Lukas, Holzman, Burt, Hopkins, Walter, Hsu, Shih-Chieh, Höche, Stefan, Ilten, Philip James, Ivantchenko, Vladimir, Jones, Chris, Jouvin, Michel, Khoo, Teng Jian, Kisel, Ivan, Knoepfel, Kyle, Konstantinov, Dmitri, Krasznahorkay, Attila, Krauss, Frank, Krikler, Benjamin Edward, Lange, David, Laycock, Paul, Li, Qiang, Lieret, Kilian, Liu, Miaoyuan, Loncar, Vladimir, Lönnblad, Leif, Maltoni, Fabio, Mangano, Michelangelo, Marshall, Zachary Louis, Mato, Pere, Mattelaer, Olivier, McFayden, Joshua Angus, Meehan, Samuel, Mete, Alaettin Serhan, Morgan, Ben, Mrenna, Stephen, Muralidharan, Servesh, Nachman, Ben, Neubauer, Mark S., Neumann, Tobias, Ngadiuba, Jennifer, Ojalvo, Isobel, Pedro, Kevin, Perini, Maurizio, Piparo, Danilo, Pivarski, Jim, Plätzer, Simon, Pokorski, Witold, Pol, Adrian Alan, Prestel, Stefan, Ribon, Alberto, Ritter, Martin, Rizzi, Andrea, Rodrigues, Eduardo, Roiser, Stefan, Schulz, Holger, Schulz, Markus, Schönherr, Marek, Sexton-Kennedy, Elizabeth, Siegert, Frank, Siódmok, Andrzej, Stewart, Graeme A, Sudhir, Malik, Summers, Sioni Paris, Thais, Savannah Jennifer, Tran, Nhan Viet, Valassi, Andrea, Verderi, Marc, Bruch, Dorothea Vom, Watts, Gordon T., Wenaus, Torre, and Yazgan, Efe

Subjects
Physics - Computational Physics, High Energy Physics - Experiment
Abstract

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful., Comment: 40 pages contribution to Snowmass 2021

Published
2020
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11. Clustering of $\bar B\to D^{(*)}\tau^-\bar\nu_\tau$ kinematic distributions with ClusterKinG

Author

Aebischer, Jason, Kuhr, Thomas, and Lieret, Kilian

Subjects
High Energy Physics - Phenomenology, High Energy Physics - Experiment
Abstract

New Physics can manifest itself in kinematic distributions of particle decays. The parameter space defining the shape of such distributions can be large which is challenging for both theoretical and experimental studies. Using clustering algorithms, the parameter space can however be dissected into subsets (clusters) which correspond to similar kinematic distributions. Clusters can then be represented by benchmark points, which allow for less involved studies and a concise presentation of the results. We demonstrate this concept using the Python package ClusterKinG, an easy to use framework for the clustering of distributions that particularly aims to make these techniques more accessible in a High Energy Physics context. As an example we consider $\bar B\to D^{(*)}\tau^-\bar\nu_\tau$ distributions and discuss various clustering methods and possible implications for future experimental analyses., Comment: 25 pages, 10 figures, 2 tables; corrected implementation of chi2 metric with correlated random variables and corrected number of degrees of freedom; added toy study to validate statistical treatment

Published
2019
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12. Train to Sustain

Author

Malik Sudhir, Lieret Kilian, Elmer Peter, Hernandez Villanueva Michel, and Roiser Stefan

Subjects
Physics, QC1-999
Abstract

The HSF/IRIS-HEP Software Training group provides software training skills to new researchers in High Energy Physics (HEP) and related communities. These skills are essential to produce high-quality and sustainable software needed to do the research. Given the thousands of users in the community, sustainability, though challenging, is the centerpiece of its approach. The training modules are open source and collaborative. Different tools and platforms, like GitHub, enable technical continuity, collaboration and nurture the sense to develop software that is reproducible and reusable. This contribution describes these efforts and its broader impacts.

Published
2024
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13. An Object Condensation Pipeline for Charged Particle Tracking at the High Luminosity LHC

Author

Lieret Kilian and DeZoort Gage

Subjects
Physics, QC1-999
Abstract

Recent work has demonstrated that graph neural networks (GNNs) trained for charged particle tracking can match the performance of traditional algorithms while improving scalability to prepare for the High Luminosity LHC experiment. Most approaches are based on the edge classification (EC) paradigm, wherein tracker hits are connected by edges, and a GNN is trained to prune edges, resulting in a collection of connected components representing tracks. These connected components are usually collected by a clustering algorithm and the resulting hit clusters are passed to downstream modules that may assess track quality or fit track parameters. In this work, we consider an alternative approach based on object condensation (OC), a multi-objective learning framework designed to cluster points belonging to an arbitrary number of objects, in this context tracks, and regress the properties of each object. We demonstrate that OC shows very promising results when applied to the pixel detector of the trackML dataset and can, in some cases, recover tracks that are not reconstructable when relying on the output of an EC alone. The results have been obtained with a modular and extensible open-source implementation that allows us to efficiently train and evaluate the performance of various OC architectures and related approaches.

Published
2024
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16. A new Software Training Model at Belle II

Author

Lieret, Kilian, primary, Luca Pinna Angioni, Gian, additional, Bauer, Moritz, additional, Bertemes, Michel, additional, Bilokin, Sviatoslav, additional, Di Canto, Angelo, additional, Casarosa, Giulia, additional, Cheaib, Racha, additional, Cunliffe, Sam, additional, Eberlein, Nathalie, additional, Eliachevitch, Michael, additional, Garcia, Marcela, additional, Grace, Phil, additional, Greenwald, Daniel, additional, Hartbrich, Oskar, additional, Hsu, Chia-Ling, additional, Komarov, Ilya, additional, Kuhr, Thomas, additional, Kulii, Yaroslav, additional, Meier, Frank, additional, Milesi, Marco, additional, Mora, Alejandro, additional, De Pietro, Giacomo, additional, Prim, Markus, additional, Ritter, Martin, additional, Schmolz, Pascal, additional, Tamponi, Umberto, additional, Tenchini, Francesco, additional, Villanueva, Michel, additional, Marie Wakeling, Hannah, additional, and Zhou, Xing-Yu, additional

Published
2023
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17. IRIS-HEP Strategic Plan for the Next Phase of Software Upgrades for HL-LHC Physics

Author

Bockelman, Brian, Elmer, Peter, Watts, Gordon, Cranmer, Kyle, Feickert, Matthew, Gardner, Rob, Gray, Heather, Held, Alexander, Katz, Daniel S., Lange, David, Lieret, Kilian, Neubauer, Mark, Shadura, Oksana, Sokoloff, Mike, Tuck, Robert, Wuerthwein, Frank, Adam, Wolfgang, Bauerdick, Lothar, Bose, Tulika, Bloom, Kenneth, Calafiura, Paolo, Di Girolamo, Alessandro, Gardner, Robert, Gutsche, Oliver, Hildreth, Michael, Koppenburg, Patrick, Letts, James, Maltzahn, Carlos, Marshall, Zachary, Martinez Outschoorn, Verena, Mcbride, Patricia, Mckee, Shawn, Neubauer, Mark S., Osborne, Ianna, Piparo, Danilo, Rodrigues, Eduardo, Sexton-Kennedy, Elizabeth, Silvestris, Lucia, Sokoloff, Michael D., Tadel, Matevz, Tompkins, Lauren, Vassilev, Vassil, Weitzel, Derek, Williams, Mike, and Peter Wittich

Subjects
Accelerator Physics (physics.acc-ph), Physics - Instrumentation and Detectors, hep-ex, Other Fields of Physics, FOS: Physical sciences, Instrumentation and Detectors (physics.ins-det), Computational Physics (physics.comp-ph), Accelerators and Storage Rings, High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), physics.comp-ph, Physics - Accelerator Physics, Detectors and Experimental Techniques, Physics - Computational Physics, physics.ins-det, Particle Physics - Experiment, physics.acc-ph
Abstract

The quest to understand the fundamental building blocks of nature and their interactions is one of the oldest and most ambitious of human scientific endeavors. CERN's Large Hadron Collider (LHC) represents a huge step forward in this quest. The discovery of the Higgs boson, the observation of exceedingly rare decays of $B$ mesons, and stringent constraints on many viable theories of physics beyond the Standard Model (SM) demonstrate the great scientific value of the LHC physics program. The next phase of this global scientific project will be the High-Luminosity LHC (HL-LHC) which will collect data starting circa 2029 and continue through the 2030s. The primary science goal is to search for physics beyond the SM and, should it be discovered, to study its implications. In the HL-LHC era, the ATLAS and CMS experiments will record around 100 times as many collisions as were used to discover the Higgs boson (and at twice the energy). Both NSF and DOE are making large detector upgrade investments so the HL-LHC can operate in this high-rate environment. Similar investment in software R&D for acquiring, managing, processing and analyzing HL-LHC data is critical to maximize the return-on-investment in the upgraded accelerator and detectors. This report presents a strategic plan for a possible second 5-year funded phase (2023 through 2028) for the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) which will close remaining software and computing gaps to deliver HL-LHC science.

Published
2023

18. Measurement of the $\Omega_c^0$ lifetime at Belle II

Author

Abudinen, Fernando Jesus, Adachi, Ichiro, Aggarwal, Latika, Ahmed, Hossain, Aihara, Hiroaki, Akopov, Norayr, Aloisio, Alberto, Nguyen, Anh-Ky, Asner, David Mark, Aushev, Tagir, Aushev, Volodymyr Ye, Bae, Hanwook, Bambade, Philip, Banerjee, Sw., Banerjee, Swagato, Baudot, Jerome, Bauer, Moritz, Beaubien, Alexandre, Becker, Juergen, Behera, Prafulla Kumar, Bennett, Jake, Bernieri, Enrico, Bernlochner, Florian, Bertacchi, Valerio, Bertemes, Michel, Bertholet, Emilie, Bessner, Martin, Bettarini, Stefano, Bhuyan, Bipul, Bianchi, Fabrizio, Bilka, Tadeas, Biswas, Diptaparna, Bodrov, Denis, Bolz, Arthur, Borah, Jyotirmoi, Bozek, Andrzej, Bracko, Marko, Branchini, Paolo, Briere, R. A., Browder, Thomas Earl, Budano, Antonio, Bussino, Severino, Campajola, Marcello, Cao, Lu, Casarosa, Giulia, Cecchi, Claudia, Chang, Ming-Chuan, Cheema, Priyanka, Chekelian, Vladimir, Chen, Yongqing, Chilikin, Kirill, Chirapatpimol, Khem, Cho, HanEol, Cho, Kihyeon, Cho, Sungjin, Choi, Sookyung, Choudhury, Seema, Cinabro, David, Corona, Luigi, Cunliffe, Samuel Thomas, Das, Sanjeeda Bharati, Dattola, Filippo, De La Cruz Burelo, Eduard, De La Motte, Shanette Anne, de Marino, Gaetano, De Nardo, Guglielmo, De Nuccio, Michael, De Pietro, Giacomo, de Sangro, Riccardo, Destefanis, Marco, Dey, Sourav, De Yta Hernandez, Alejandro, Dhamija, Rashmi, Di Canto, Angelo, Di Capua, Francesco, Dingfelder, Jochen, Dolezal, Zdenek, Dominguez Jimenez, Isabel, Dong, Thanh Van, Dorigo, Mirco, Dort, Katharina, Dreyer, Sascha, Dubey, Shawn, Dujany, Giulio, Ecker, Patrick, Eliachevitch, Michael, Epifanov, Denis Alexandrovich, Feichtinger, Paul, Ferber, Torben, Ferlewicz, Daniel, Fillinger, Tristan, Finocchiaro, Giuseppe, Fodor, Andrea, Forti, Francesco, Fulsom, Bryan Gregory, Ganiev, Eldar, Gaur, Vipin, Gaz, Alessandro, Gellrich, Andreas, Ghevondyan, Gayane, Giordano, Raffaele, Giri, Anjan, Glazov, Alexander, Gobbo, Benigno, Godang, Romulus, Goldenzweig, Pablo, Gradl, Wolfgang, Granderath, Svenja Katharina, Graziani, Enrico, Greenwald, Daniel, Gu, Tianping, Guan, Yinghui, Gudkova, Kristina, Guilliams, Justin, Hayasaka, Kiyoshi, Hayashii, Hisaki, Hazra, Sagar, Hearty, Christopher, Heredia de la Cruz, Ivan, Hernandez Villanueva, Michel, Hershenhorn, Alon David, Higuchi, Takeo, Hill, Ewan Chin, Hirata, Hikari, Hohmann, Marcel, Hsu, Chia-Ling, Iijima, Toru, Inami, Kenji, Inguglia, Gianluca, Ipsita, N. Sushree, Ishikawa, Akimasa, Ito, Shintaro, Itoh, Ryosuke, Iwasaki, Masako, Jackson, Paul Douglas, Jacobs, William Wescott, Jaffe, David, Jang, Eunji, Jia, Sen, Jin, Yifan, Joo, Kyung Kwang, Junkerkalefeld, Henrik, Kaliyar, Abdul Basith, Kang, Kookhyun, Karl, Robert, Karyan, Gevorg, Kiesling, Christian, Kim, Cheolhun, Kim, Doris Yangsoo, Kim, Kyungho, Kim, Yongkyu, Kindo, Haruki, Kinoshita, Kay, Kodys, Peter, Koga, Taichiro, Kohani, Shahab, Kojima, Kazuki, Konno, Tomoyuki, Korobov, Alexandr, Korpar, Samo, Kovalenko, Evgeniy, Kowalewski, Robert, Kraetzschmar, Thomas Michael Gerd, Krizan, Peter, Krokovny, Pavel, Kumar, Jitendra, Kumara, Kospalage Janaka, Kunigo, Takuto, Kuzmin, Alexander, Kwon, Youngjoon, Lacaprara, Stefano, Lam, Tommy, Lanceri, Livio, Lange, Jens Soeren, Laurenza, Martina, Lautenbach, Klemens, Leboucher, Robin, Li, Chunhua, Li, Longke, Libby, James Frederick, Lieret, Kilian Adriano, Liptak, Zachary John, Liu, Qingyuan, Liventsev, Dmitri, Longo, Savino, Lozar, Andrej, Lueck, Thomas, Lyu, Chaoyi, Maggiora, Marco, Maiti, Rajesh Kumar, Manfredi, Riccardo, Manoni, Elisa, Marcello, Simonetta, Marinas, Carlos, Martini, Alberto, Martinov, Tommy, Massaccesi, Ludovico, Masuda, Masataka, Maurya, Shubhangi Krishan, McKenna, Janis Anne, Meier, Frank, Merola, Mario, Metzner, Felix, Milesi, Marco, Miller, Caleb, Miyabayashi, Kenkichi, Mizuk, Roman, Mohanty, G. B., Molina-Gonzalez, N., Molina, Norman Wilfrido, Moneta, Stefano, Moser, Hans-Guenther, Mrvar, Manca, Mussa, Roberto, Nakamura, Isamu, Nakao, Mikihiko, Nakayama, Hiroyuki, Nakazawa, Yu, Narimani Charan, Abtin, Naruki, Megumi, Natkaniec, Zbigniew Marian, Natochii, Andrii, Nayak, Lopamudra, Nayak, Minakshi, Nazaryan, Gevorg, Nellikunnummel, Nisar, Ogawa, Satoru, Ono, Hiroaki, Onuki, Yoshiyuki, Oxford, Emma Rollinson, Paladino, Antonio, Panta, Anil, Paoloni, Eugenio, Pardi, Silvio, Park, Hwanbae, Park, Seokhee, Passeri, Antonio, Paul, Stephan Martin, Pedlar, Todd Kristofer, Peruzzi, Ida Marena, Peschke, Richard, Pestotnik, Rok, Piccolo, Marcello, Piilonen, L. E., Podesta lerma, Pedro Luis Manuel, Podobnik, Tomaz, Pokharel, Saroj, Polat, Leonard, Praz, Cyrille, Prell, Soeren, Prencipe, Elisabetta, Prim, Markus Tobias, Purwar, Harsh, Rad, Navid Khandan, Rados, Petar, Raiz, Sebastiano, Ramirez Morales, Andres, Reif, Markus, Reiter, Simon, Remnev, Mikhail, Ripp-Baudot, Isabelle, Rizzo, Giuliana, Robertson, Steven H., Roney, John Michael, Rostomyan, Armine, Rout, Niharika, Russo, Guido, Sanders, David Alan, Sandilya, Saurabh, Sangal, Aman, Sato, Yo, Savinov, Vladimir, Scavino, Bianca, Schueler, Jeff, Schwanda, Christoph, Schwartz, Alan Jay, Schwenker, Benjamin, Seino, Yoshiaki, Selce, Andrea, Senyo, Katsumi, Serrano, Justine, Sevior, Martin Edmund, Sfienti, Concettina, Shen, Chengping, Shi, Xiaodong, Shillington, Trevor, Sibidanov, Alexei, Singh, Jasbir, Skorupa, Justin, Sobie, Randall, Soffer, Abner, Solovieva, Elena, Spataro, Stefano, Spruck, Bjoern, Staric, Marko, Stefkova, Slavomira, Stottler, Zachary Shaun, Stroili, Roberto, Strube, Jan Fridolf, Sumihama, Mizuki, Sumisawa, Kazutaka, Sutcliffe, William, Suzuki, Soh Yamagata, Svidras, Henrikas, Takizawa, Makoto, Tamponi, Umberto, Tanida, Kiyoshi, Tanigawa, Hikaru, Tenchini, Francesco, Thaller, Arthur, Tiwary, Rahul, Tonelli, Diego, Torassa, Ezio, Toutounji, Nadia Ejlal, Trabelsi, Karim, Uchida, Makoto, Ueda, Ikuo, Uematsu, Yuma, Uglov, Timofey, Unger, Kai Lukas, Unno, Yuji, Uno, Kenta, Uno, Shoji, Ushiroda, Yutaka, Vahsen, Sven Einar, van Tonder, Raynette, Varner, Gary, Varvell, Kevin Ernest, Vinokurova, Anna, Vitale, Lorenzo, Vobbilisetti, Vidya Sagar, Wakeling, Hannah Marie, Wang, Erfei, Wang, Min-Zu, Wang, Xiaolong, Warburton, Andreas, Watanabe, Minori, Watanuki, Shun, Welsch, Maximilian, Wessel, Christian, Won, Eunil, Yabsley, Bruce Donald, Yamada, Satoru, Yan, Wenbiao, Yang, Seongbae, Ye, Hua, Yelton, John, Yin, Junhao, Yook, Youngmin, Yoshihara, Keisuke, Yuan, Changzheng, Zani, Laura, Zhang, Yi, Zhilich, Victor, Zhou, Qi-Dong, Zhou, Xing-Yu, Zhukova, Valentina, Zlebcik, Radek, Belle-II Collaboration, Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut Pluridisciplinaire Hubert Curien (IPHC), 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), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Belle-II, and HEP, INSPIRE

Subjects
lifetime, Omega, [PHYS.HEXP] Physics [physics]/High Energy Physics - Experiment [hep-ex], hadronic decay [Omega/c0], BELLE, annihilation [electron positron], electron positron: colliding beams, Omega/c0: hadronic decay, electron positron: annihilation, pi+, High Energy Physics - Experiment, KEK-B, Omega/c0: lifetime, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], colliding beams [electron positron], ddc:530, charmed baryon, +Omega-+pi%2B%22">Omega/c0 --> Omega- pi+, lifetime [Omega/c0], experimental results
Abstract

Physical review / D 107, L031103 (2023). doi:10.1103/PhysRevD.107.L031103, We report on a measurement of the $\Omega_c^0$ lifetime using $\Omega_c^0 \to \Omega^-\pi^+$ decays reconstructed in $e^+e^-\to c\bar{c}$ data collected by the Belle II experiment and corresponding to $207~{\rm fb^{-1}}$ of integrated luminosity. The result, $\rm\tau(\Omega_c^0)=243\pm48( stat)\pm11(syst)~fs$, agrees with recent measurements indicating that the $\Omega_c^0$ is not the shortest-lived weakly decaying charmed baryon., Published by Inst., Woodbury, NY

Published
2023
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19. Selective background Monte Carlo simulation at Belle II

Author

Kahn James, Dorigatti Emilio, Lieret Kilian, Lindner Andreas, and Kuhr Thomas

Subjects
Physics, QC1-999
Abstract

The large volume of data expected to be produced by the Belle II experiment presents the opportunity for studies of rare, previously inaccessible processes. Investigating such rare processes in a high data volume environment necessitates a correspondingly high volume of Monte Carlo simulations to prepare analyses and gain a deep understanding of the contributing physics processes to each individual study. This resulting challenge, in terms of computing resource requirements, calls for more intelligent methods of simulation, in particular for processes with very high background rejection rates. This work presents a method of predicting in the early stages of the simulation process the likelihood of relevancy of an individual event to the target study using graph neural networks. The results show a robust training that is integrated natively into the existing Belle II analysis software framework.

Published
2020
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20. HSF IRIS-HEP Second Analysis Ecosystem Workshop Report

Author

Stewart, Graeme A, Elmer, Peter, Eulisse, Giulio, Gouskos, Loukas, Hageboeck, Stephan, Hall, Allison Reinsvold, Heinrich, Lukas, Held, Alexander, Jouvin, Michel, Khoo, Teng Jian, Laycock, Paul, Mato Vila, Pere, Pivarski, Jim, Rembser, Jonas, Rodrigues, Eduardo, Schaarschmidt, Jana, Sexton-Kennedy, Elizabeth, Shadura, Oksana, Simpson, Nathan, Skidmore, Nicola, Sokoloff, Michael, Watts, Gordon, Mohamed, Aly, Burzynski, Jackson, Cardwell, Bryan, Craik, Daniel C, Dado, Tomas, Delgado Peris, Antonio, Doglioni, Caterina, Eren, Engin, Eriksen, Martin B, Eschle, Jonas, Fitzpatrick, Conor, Flix Molina, José, Gasiorowski, Sean, Goel, Aman, Guiraud, Enrico, Gupta, Kanhaiya, Hernández Villanueva, Michael, Hernández, José M, Hrivnac, Julius, Lieret, Kilian, Kreczko, Luke, Krumnack, Nils, Kuhr, Thomas, Kundu, Baidyanath, Lancon, Eric, Lange, Johannes, Manganelli, Nicholas J, Novak, Andrzej, Perez-Calero Yzquierdo, Antonio, Proffitt, Mason, Rybkin, Grigori, Schreiner, Henry F, Schulz, Markus, Sciabà, Andrea, Sekmen, Sezen, van Daalen, Tal, Simko, Tibor, Singh, Jaydip, Smith, Nicholas, Strong, Giles C, Unel, Gokhan, Vassilev, Vassil, Waterlaat, Mark, Yazgan, Efe, Das, Ayanabha, and Galewsky, Ben

Subjects
High-Energy Physics, Analysis, Software
Abstract

The second workshop on the HEP Analysis Ecosystem took place 23-25 May 2022 at IJCLab in Orsay, to look at progress and continuing challenges in scaling up HEP analysis to meet the needs of HL-LHC and DUNE, as well as the very pressing needs of LHC Run 3 analysis. The workshop was themed around six particular topics, which were felt to capture key questions, opportunities and challenges. Each topic arranged a plenary session introduction, often with speakers summarising the state-of-the art and the next steps for analysis. This was then followed by parallel sessions, which were much more discussion focused, and where attendees could grapple with the challenges and propose solutions that could be tried. Where there was significant overlap between topics, a joint discussion between them was arranged. In the weeks following the workshop the session convenors wrote this document, which is a summary of the main discussions, the key points raised and the conclusions and outcomes. The document was circulated amongst the participants for comments before being finalised here.

Published
2022
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21. Calibration of machine learning based hadronic tagging in preparation for a Vcb measurement and clustering of kinematic distributions

Author

Lieret, Kilian

Subjects
Belle, Belle II, Machine Learning, Clustering, Calibration, FOS: Physical sciences
Abstract

Measurements of the decay rate ratios R(D(*)) of B -> D(*) tau nu and B -> D(*) ℓ nu decays (ℓ=e, mu) show a substantial tension with the predictions of the Standard Model of particle physics. This thesis explores multiple topics that can facilitate a better understanding of these so-called flavor anomalies. These flavor anomalies may point to contributions of physics beyond the Standard Model, however the large, unexplored parameter space defining such contributions makes exploratory physics studies challenging. Clustering algorithms can divide this parameter space into subsets featuring similar decay kinematics, which helps to simplify studies. I present an open-source software package that makes such techniques accessible and demonstrate its application on the B -> D(*) tau nu decay, which is speculated to be responsible for the R(D(*)) anomalies. Furthermore, I show preparations for a study of the decay e-e+ -> Upsilon(4S) -> Btag (-> hadrons) Bsig (-> D* ℓ nu) with the dataset of the Belle experiment. Besides being of interest as the normalization channel of the R(D*) ratio, this decay also allows for the measurement of the CKM matrix element Vcb and for fits to hadronic form factors. One of the significant improvements of this study over previous studies at Belle is the use of the Full Event Interpretation (FEI), a machine learning algorithm that is able to reconstruct several thousand possible Btag decays. However, this algorithm is sensitive to inaccuracies in the modeling of the Monte Carlo simulation used throughout the analysis, leading to different efficiencies on simulated and recorded data. To correct these efficiency differences, I reconstruct the decay e-e+ -> Upsilon(4S) -> Btag (-> hadrons) Bsig (-> X ℓ nu). Assuming that the reconstruction efficiencies of the X ℓ nu decay are well-understood, any efficiency difference between data and Monte Carlo simulation can be attributed to the FEI and hence used for its calibration. Along with its effect on the overall reconstruction efficiency, the calibration also affects important observables used for background subtraction in Vcb and R(D(*)) analyses. I validate the calibration with a sample of B -> D ℓ nu decays and perform additional studies to confirm the validity of core assumptions of the calibration procedure. The calibration of Btag mesons with correctly and incorrectly reconstructed flavors are found to differ, which was not accounted for in previous analyses. I successfully explore several correction strategies on a preliminary dataset. As the FEI is used heavily at both Belle and Belle II, this result has significant implications for many past and upcoming analyses. The success of the FEI also highlights the importance of using state-of-the-art software technologies in modern measurements. To deliver the best possible science, large experimental collaborations are increasingly focusing on software education. I present recent developments in the software training activities that I have coordinated at Belle II and at the High Energy Physics Software Foundation (HSF)., Messungen der Verhältnisse R(D(*)) der Zerfallsraten von B -> D(*) tau nu und B -> D(*) ℓ nu (ℓ=e, mu) zeigen erhebliche Abweichungen zu den Vorhersagen des Standardmodells der Teilchenphysik. Die vorliegende Arbeit präsentiert verschiedene Ergebnisse, die zu einem verbesserten Verständnis dieser Anomalien beitragen können. Die Abweichungen können als Effekte von Physik jenseits des Standardmodells interpretiert werden. Der Parameterraum, der diese Effekte beschreibt, ist aufgrund seiner Größe jedoch eine Herausforderung für theoretische wie experimentelle Studien. Clusteranalysen können diesen Parameterraum in Teilmengen mit ähnlichen Zerfallskinematiken unterteilen, um entsprechende Untersuchungen zu vereinfachen. Ich stelle ein Open-Source-Softwarepaket vor, das derartige Methoden implementiert und wende es auf den B -> D(*) tau nu-Zerfall an, der für die R(D(*))-Anomalien verantwortlich gemacht wird. Weiterhin zeige ich Vorbereitungen für eine Messung des Zerfalls e-e+ -> Upsilon(4S) -> Btag (-> Hadronen) Bsig (-> D* ℓ nu) mit dem Datensatz des Belle-Experiments. Neben seiner Bedeutung als Normalisierungskanal der R(D*)-Observable erlaubt dieser Prozess die Messung des CKM-Matrix-Elements Vcb und die Extrapolation hadronischer Formfaktoren. Eine der entscheidenden Verbesserungen dieser Messung gegenüber Vorgängerstudien ist die Verwendung der Full Event Interpretation (FEI). Dieser Machine-Learning-Algorithmus rekonstruiert Btag-Mesonen in mehreren tausend Zerfallskanälen. Allerdings führen Ungenauigkeiten in der für die Analyse essenziellen Monte-Carlo-Simulation zu Effizienzunterschieden der FEI zwischen simulierten und gemessenen Daten. Um diese Effizienzunterschiede zu korrigieren, rekonstruiere ich den Zerfall e-e+ -> Upsilon(4S) -> Btag (-> Hadronen) Bsig (-> X ℓ nu). Unter der Annahme, dass die Rekonstruktionseffizienzen von X ℓ nu ausreichend verstanden sind, lassen sich die Effizienzunterschiede zwischen Daten und Monte-Carlo-Simulation der FEI zuordnen, sodass eine Kalibrierung vorgenommen werden kann. Neben der Korrektur der Gesamteffizienz beeinflusst die Kalibrierung auch wichtige Observablen für die Signalextraktion in Vcb- und R(D(*))-Analysen. Zur Validierung der Kalibrierung betrachte ich B -> D ℓ nu-Zerfälle und führe weitere Studien zur Bestätigung zentraler Annahmen der Kalibrierungsmethodik durch. Es zeigt sich, dass sich die Kalibrierung für Btag-Mesonen mit korrekt und inkorrekt rekonstruiertem flavor wesentlich unterscheidet, was in früheren Analysen nicht berücksichtigt wurde. Für eine Korrektur dieses Effekts präsentiere ich mehrere Strategien, die ich erfolgreich auf einen vorläufigen Datensatz anwende. Aufgrund der zentralen Rolle der FEI für eine Vielzahl von Studien am Belle- und Belle II-Experiment sind diese Resultate von weitreichender Bedeutung. Der Erfolg der FEI verdeutlicht auch die Bedeutung neuester Softwaretechniken für moderne Messungen. Um das volle Potenzial der gemessenen Daten auszuschöpfen, investieren große Kollaborationen zunehmend in die Softwarekenntnisse ihrer Mitglieder. Ich zeige von mir koordinierte Aktivitäten am Belle II-Experiment und in der High Energy Physics Software Foundation (HSF).

Published
2022
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22. Measurement of the $B^+/B^0$ production ratio in $e^+e^-$ collisions at the $\Upsilon(4S)$ resonance using $B \rightarrow J/\psi(\ell\ell) K$ decays at Belle

Author

Adachi, Ichirou, Aihara, Hiroaki, Asner, David, Atmacan, Hulya, Aushev, Tagir, Ayad, Rachid, Babu, Varghese, Bahinipati, Seema, Behera, Prafulla Kumar, Belous, Konstantin, Bennett, Jake, Bessner, Martin, Bhardwaj, Vishal, Bhuyan, Bipul, Bilka, Tadeas, Bodrov, Denis, Bonvicini, Giovanni, Borah, Jyotirmoi, Bozek, Andrzej, Bracko, Marko, Branchini, Paolo, Browder, Thomas, Budano, Antonio, Campajola, Marcello, Cervenkov, Daniel, Chang, Ming-Chuan, Chen, Chunhui, Cheon, Byunggu, Chilikin, Kirill, Cho, Haneol, Cho, Kihyeon, Cho, Sungjin, Choi, Sookyung, Choi, Youngil, Choudhury, Seema, Cinabro, David, Das, Sanjeeda Bharati, de Pietro, Giacomo, Di Capua, Francesco, Dingfelder, Jochen, Dolezal, Zdenek, Dong, Thanh Van, Epifanov, Denis Alexandrovich, Ferlewicz, Daniel, Fulsom, Bryan Gregory, Garg, Renu, Gaur, Vipin, Gabyshev, Nikolay, Giri, Anjan, Goldenzweig, Pablo, Graziani, Enrico, Gu, Tianping, Gudkova, Kristina, Hadjivasiliou, Christos, Hara, Takanori, Hayasaka, Kiyoshi, Hayashii, Hisaki, Hedges, Michael Thomas, Herrmann, Daniel Ricalde, Hou, George Wei-Shu, Hsu, Chia-Ling, Inami, Kenji, Ipsita, N. Sushree, Ishikawa, Akimasa, Itoh, Ryosuke, Iwasaki, Masako, Jacobs, William, Jang, Eunji, Ji, Qingping, Jia, Sen, Jin, Yifan, Joo, Kyung Kwang, Kang, Kook Hyun, Kawasaki, Takeo, Kiesling, Christian, Kim, Cheolhun, Kim, Doris Yangsoo, Kim, Kyungho, Kim, Yongkyu, Kindo, Haruki, Kinoshita, Kay, Kodys, Peter, Konno, Tomoyuki, Korobov, Alexander, Korpar, Samo, Kovalenko, Evgeniy, Krizan, Peter, Krokovny, Pavel, Kumar, Manish, Kumar, Rajeev, Kumara, Kospalage Janaka, Kwon, Young-Joon, Lam, Tommy, Lange, Jens Soeren, Laurenza, Martina, Lee, Seungcheol, Lewis, Peter, Li, Jin, Li, Longke, Li, Yubo, Li Gioi, Luigi, Libby, James Frederick, Lieret, Kilian Adriano, Liventsev, Dmitri, Luo, Tao, Masuda, Masataka, Matsuda, Tatsuro, Matvienko, Dmitry, Maurya, Shubhangi Krishan, Meier, Frank, Merola, Mario, Metzner, Felix, Miyabayashi, Kenkichi, Mizuk, Roman, Mohanty, Gagan Bihari, Nakamura, Isamu, Nakao, Mikihiko, Natkaniec, Zbigniew, Natochii, Andrii, Nayak, Lopamudra, Nayak, Minakshi, Nellikunnummel, Nisar, Nishida, Shohei, Ogawa, Satoru, Ono, Hiroaki, Oskin, Pavel, Pakhlov, Pavel, Pakhlova, Galina, Pardi, Silvio, Park, Seokhee, Passeri, Antonio, Patra, Sourav, Paul, Stephan Martin, Pedlar, Todd Kristofer, Pestotnik, Rok, Piilonen, Leo, Podobnik, Tomaz, Prell, Soeren, Prencipe, Elisabetta, Prim, Markus, Rout, Niharika, Russo, Guido, Sakai, Yoshihide, Sandilya, Saurabh, Sangal, Aman, Santelj, Luka, Savinov, Vladimir, Schueler, Jeff, Schwanda, Christoph, Schwartz, Alan Jay, Seino, Yoshiaki, Senyo, Katsumi, Sevior, Martin Edmund, Shapkin, Mikhail, Sharma, Chanchal, Shen, Chengping, Shiu, Jing-Ge, Singh, Jasbir, Sokolov, Anatoly, Solovieva, Elena, Staric, Marko, Stottler, Zachary S., Strube, Jan, Sumihama, Mizuki, Sumiyoshi, Takayuki, Takizawa, Makoto, Tanida, Kiyoshi, Tenchini, Francesco, Trabelsi, Karim, Uchida, Makoto, Uglov, Timofey, Unno, Yuuji, Uno, Shoji, Urquijo, Phillip, Ushiroda, Yutaka, Usov, Yuri, van Tonder, Raynette, Varner, Gary, Varvell, Kevin Ernest, Vinokurova, Anna, Vossen, Anselm Georg, Waheed, Eiasha, Wang, Erfei, Wang, Min-Zu, Wang, Xiaolong, Watanabe, Minori, Watanuki, Shun, Won, Eunil, Xu, Xinping, Yabsley, Bruce, Yang, Seongbae, Yelton, John, Yin, Junhao, Yuan, Changzheng, Yusa, Yosuke, Zhai, Yunxiao, Zhang, Ziping, Zhilich, Victor, Zhukova, Valentina, and HEP, INSPIRE

Subjects
+electron+positron%22">J/psi(3100) --> electron positron, B0: hadronic decay, Upsilon(10570): electroproduction, +J%2Fpsi%283100%29+K%2B%22">B+ --> J/psi(3100) K+, [PHYS.HEXP] Physics [physics]/High Energy Physics - Experiment [hep-ex], +B0+anti-B0%22">Upsilon(10570) --> B0 anti-B0, measure: ratio, BELLE, electron positron: colliding beams, branching ratio: ratio, electron positron: annihilation, +B%2B+B%22">Upsilon(10570) --> B+ B, decay rate: ratio, electron: pair production, muon: pair production, J/psi(3100): leptonic decay, Upsilon(10570): branching ratio, error: statistical, KEK-B, +muon%2B+muon%22">J/psi(3100) --> muon+ muon, +J%2Fpsi%283100%29+K0%22">B0 --> J/psi(3100) K0, isospin: symmetry, B+: hadronic decay, experimental results
Abstract

We measure the ratio of branching fractions for the $\Upsilon (4S)$ decays to $B^+B^-$ and $B^0\bar{B}{}^0$ using $B^+ \rightarrow J/\psi(\ell\ell) K^+$ and $B^0 \rightarrow J/\psi(\ell\ell) K^0$ samples, where $J/\psi(\ell\ell)$ stands for $J/\psi \to \ell^+\ell^-$ ($\ell = e$ or $\mu$), with $711$ fb$^{-1}$ of data collected at the $\Upsilon(4S)$ resonance with the Belle detector. We find the decay rate ratio of $\Upsilon(4S) \rightarrow B^+B^-$ over $\Upsilon(4S) \rightarrow B^0\bar{B}{}^0$ to be $1.065\pm0.012\pm 0.019 \pm 0.047$, which is the most precise measurement to date. The first and second uncertainties are statistical and systematic, respectively, and the third uncertainty is systematic due to the assumption of isospin symmetry in $B \to J/\psi(\ell\ell) K$.

Published
2022

23. Software Training in HEP

Author

Malik, Sudhir, primary, Meehan, Samuel, additional, Lieret, Kilian, additional, Oan Evans, Meirin, additional, Villanueva, Michel H., additional, Katz, Daniel S., additional, Stewart, Graeme A., additional, Elmer, Peter, additional, Aziz, Sizar, additional, Bellis, Matthew, additional, Bianchi, Riccardo Maria, additional, Bianco, Gianluca, additional, Bonilla, Johan Sebastian, additional, Burger, Angela, additional, Burzynski, Jackson, additional, Chamont, David, additional, Feickert, Matthew, additional, Gadow, Philipp, additional, Gruber, Bernhard Manfred, additional, Guest, Daniel, additional, Hageboeck, Stephan, additional, Heinrich, Lukas, additional, Horzela, Maximilian M., additional, Huwiler, Marc, additional, Lange, Clemens, additional, Lehmann, Konstantin, additional, Li, Ke, additional, Majumder, Devdatta, additional, Mamužić, Judita, additional, Nelson, Kevin, additional, Newhouse, Robin, additional, Nibigira, Emery, additional, Norberg, Scarlet, additional, Pineda, Arturo Sánchez, additional, Proffitt, Mason, additional, Regnery, Brendan, additional, Roepe, Amber, additional, Roiser, Stefan, additional, Schreiner, Henry, additional, Shadura, Oksana, additional, Stark, Giordon, additional, Swatman, Stephen Nicholas, additional, Thais, Savannah, additional, Valassi, Andrea, additional, Wunsch, Stefan, additional, Yakobovitch, David, additional, and Yuan, Siqi, additional

Published
2021
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24. Erratum to: Clustering of <math> <mover> <mi>B</mi> <mo>¯</mo> </mover> <mo>→</mo> <msup> <mi>D</mi> <mfenced> <mo>∗</mo> </mfenced> </msup> <msup> <mi>τ</mi> <mo>−</mo> </msup> <msub> <mover> <mi>v</mi> <mo>¯</mo> </mover> <mi>τ</mi> </msub> </math> $$ \overline{B}\to {D}^{\left(\ast \right)}{\tau}^{-}{\overline{v}}_{\tau } $$ kinematic distributions with ClusterKinG

Author

Aebischer, Jason, Kuhr, Thomas, and Lieret, Kilian

Abstract

However, this changes the interpretation of our results but slightly, because the exact probability distribution of the metric is only important for the choice and interpretation of the stopping criterium of the clustering.

Published
2021

26. HL-LHC Computing Review: Common Tools and Community Software

Author

Aarrestad, Thea, Amoroso, Simone, Carminati, Federico, Thais, Savannah Jennifer, Tran, Nhan Viet, Valassi, Andrea, Verderi, Marc, Vom Bruch, Dorothea, Watts, Gordon T., Wenaus, Torre, Yazgan, Efe, Foundation, HEP Software, Childers, Taylor, Ciulli, Vitaliano, Corti, Gloria, Costanzo, Davide, Dezoort, Justin Gage, Doglioni, Caterina, Duarte, Javier Mauricio, Dziurda, Agnieszka, Elmer, Peter, Atkinson, Markus Julian, Elsing, Markus, Elvira, V. Daniel, Eulisse, Giulio, Fernandez Menendez, Javier, Fitzpatrick, Conor, Frederix, Rikkert, Frixione, Stefano, Genser, Krzysztof L, Gheata, Andrei, Giuli, Francesco, Bendavid, Joshua, Gligorov, Vladimir, V., Grasland, Hadrien Benjamin, Gray, Heather, Gray, Lindsey, Grohsjean, Alexander, Gütschow, Christian, Hageboeck, Stephan, Harris, Philip Coleman, Hegner, Benedikt, Heinrich, Lukas, Boccali, Tommaso, Holzman, Burt, Hopkins, Walter, Hsu, Shih-Chieh, Höche, Stefan, Ilten, Philip James, Ivantchenko, Vladimir, Jones, Chris, Jouvin, Michel, Khoo, Teng Jian, Kisel, Ivan, Bocci, Andrea, Knoepfel, Kyle, Konstantinov, Dmitri, Krasznahorkay, Attila, Krauss, Frank, Krikler, Benjamin Edward, Lange, David, Laycock, Paul, Li, Qiang, Lieret, Kilian, Liu, Miaoyuan, Buckley, Andy, Loncar, Vladimir, Lönnblad, Leif, Maltoni, Fabio, Mangano, Michelangelo, Marshall, Zachary Louis, Mato, Pere, Mattelaer, Olivier, McFayden, Joshua Angus, Meehan, Samuel, Mete, Alaettin Serhan, Cacciari, Matteo, Morgan, Ben, Mrenna, Stephen, Muralidharan, Servesh, Nachman, Ben, Neubauer, Mark S., Neumann, Tobias, Ngadiuba, Jennifer, Ojalvo, Isobel, Pedro, Kevin, Perini, Maurizio, Calafiura, Paolo, Piparo, Danilo, Pivarski, Jim, Plätzer, Simon, Pokorski, Witold, Pol, Adrian Alan, Prestel, Stefan, Ribon, Alberto, Ritter, Martin, Rizzi, Andrea, Rodrigues, Eduardo, Canal, Philippe, Roiser, Stefan, Schulz, Holger, Schulz, Markus, Schönherr, Marek, Sexton-Kennedy, Elizabeth, Siegert, Frank, Siódmok, Andrzej, Stewart, Graeme A, Sudhir, Malik, Summers, Sioni Paris, 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 Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire Leprince-Ringuet (LLR), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), HEP Software Foundation, and HEP, INSPIRE

Subjects
[PHYS.HEXP] Physics [physics]/High Energy Physics - Experiment [hep-ex], Physics::Instrumentation and Detectors, Other Fields of Physics, FOS: Physical sciences, Physics::Data Analysis, Statistics and Probability, [INFO] Computer Science [cs], Computer Science::Digital Libraries, 01 natural sciences, programming, High Energy Physics - Experiment, Software, HL-LHC, High Energy Physics - Experiment (hep-ex), 0103 physical sciences, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], [INFO]Computer Science [cs], 010306 general physics, numerical calculations, ComputingMilieux_MISCELLANEOUS, Computer Science::Distributed, Parallel, and Cluster Computing, activity report, 010308 nuclear & particles physics, CMS, hep-ex, Computer Science::Software Engineering, Computational Physics (physics.comp-ph), ATLAS, CERN LHC Coll, physics.comp-ph, upgrade, Physics - Computational Physics, Particle Physics - Experiment
Abstract

Zenodo 40 pp. (2020). doi:10.5281/zenodo.4009114, Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful., Published by Zenodo

Published
2020
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27. Clustering of <math> <mover> <mi>B</mi> <mo>¯</mo> </mover> <mo>→</mo> <msup> <mi>D</mi> <mfenced> <mo>∗</mo> </mfenced> </msup> <msup> <mi>τ</mi> <mo>−</mo> </msup> <msub> <mover> <mi>ν</mi> <mo>¯</mo> </mover> <mi>τ</mi> </msub> </math> $$ \overline{B}\to {D}^{\left(\ast \right)}{\tau}^{-}{\overline{\nu}}_{\tau } $$ kinematic distributions with ClusterKinG

Author

Aebischer, Jason, Kuhr, Thomas, and Lieret, Kilian

Abstract

New Physics can manifest itself in kinematic distributions of particle decays. The parameter space defining the shape of such distributions can be large which is chalenging for both theoretical and experimental studies. Using clustering algorithms, the parameter space can however be dissected into subsets (clusters) which correspond to similar kinematic distributions. Clusters can then be represented by benchmark points, which allow for less involved studies and a concise presentation of the results. We demonstrate this concept using the Python package ClusterKinG, an easy to use framework for the clustering of distributions that particularly aims to make these techniques more accessible in a High Energy Physics context. As an example we consider B ¯ → D ∗ τ − ν ¯ τ $$ \overline{B}\to {D}^{\left(\ast \right)}{\tau}^{-}{\overline{\nu}}_{\tau } $$ distributions and discuss various clustering methods and possible implications for future experimental analyses.

Published
2020

29. B to D(*) l nu at Belle/Belle II

Author

Lieret, Kilian

Abstract

This contribution reviews measurements of |V_cb| using hadronically tagged and untagged exclusive analyses of B to D(*) l nu decays at Belle. The influence of different form factor parametrizations on |V_cb| is discussed, in particular in light of a very recent untagged analysis of B to D* l nu. Finally, prospects for similar measurements at Belle II are considered.

Published
2019
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30. Selective background Monte Carlo simulation at Belle II.

Author

Doglioni, C., Kim, D., Stewart, G.A., Silvestris, L., Jackson, P., Kamleh, W., Kahn, James, Dorigatti, Emilio, Lieret, Kilian, Lindner, Andreas, and Kuhr, Thomas

Subjects
COMPUTER software, MONTE Carlo method, COMPUTER simulation, ROBUST control, DATA analysis
Abstract

The large volume of data expected to be produced by the Belle II experiment presents the opportunity for studies of rare, previously inaccessible processes. Investigating such rare processes in a high data volume environment necessitates a correspondingly high volume of Monte Carlo simulations to prepare analyses and gain a deep understanding of the contributing physics processes to each individual study. This resulting challenge, in terms of computing resource requirements, calls for more intelligent methods of simulation, in particular for processes with very high background rejection rates. This work presents a method of predicting in the early stages of the simulation process the likelihood of relevancy of an individual event to the target study using graph neural networks. The results show a robust training that is integrated natively into the existing Belle II analysis software framework. [ABSTRACT FROM AUTHOR]

Published
2020
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31. Software Sustainability & High Energy Physics

Author

Katz, Daniel S., Malik, Sudhir, Neubauer, Mark S., Stewart, Graeme A., Assamagan, Kétévi A., Becker, Erin A., Chue Hong, Neil P., Cosden, Ian A., Meehan, Samuel, Moyse, Edward J. W., Price-Whelan, Adrian M., Sexton-Kennedy, Elizabeth, Evans, Meirin Oan, Feickert, Matthew, Lange, Clemens, Lieret, Kilian, Quick, Rob, Sánchez Pineda, Arturo, and Tunnell, Christopher

Subjects
13. Climate action, 11. Sustainability, 7. Clean energy, high energy physics, software sustainability, education, training, 12. Responsible consumption
Abstract

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).

32. Software Training in HEP

Author

Malik, Sudhir, Meehan, Samuel, Lieret, Kilian, Oan Evans, Meirin, Villanueva, Michel H., Katz, Daniel S., Stewart, Graeme A., Elmer, Peter, Aziz, Sizar, Bellis, Matthew, Bianchi, Riccardo Maria, Bianco, Gianluca, Bonilla, Johan Sebastian, Burger, Angela, Burzynski, Jackson, Chamont, David, Feickert, Matthew, Gadow, Philipp, Gruber, Bernhard Manfred, Malik, Sudhir, Meehan, Samuel, Lieret, Kilian, Oan Evans, Meirin, Villanueva, Michel H., Katz, Daniel S., Stewart, Graeme A., Elmer, Peter, Aziz, Sizar, Bellis, Matthew, Bianchi, Riccardo Maria, Bianco, Gianluca, Bonilla, Johan Sebastian, Burger, Angela, Burzynski, Jackson, Chamont, David, Feickert, Matthew, Gadow, Philipp, and Gruber, Bernhard Manfred

33. Software Training in HEP

Author

Malik, Sudhir, Meehan, Samuel, Lieret, Kilian, Oan Evans, Meirin, Villanueva, Michel H., Katz, Daniel S., Stewart, Graeme A., Elmer, Peter, Aziz, Sizar, Bellis, Matthew, Bianchi, Riccardo Maria, Bianco, Gianluca, Bonilla, Johan Sebastian, Burger, Angela, Burzynski, Jackson, Chamont, David, Feickert, Matthew, Gadow, Philipp, Gruber, Bernhard Manfred, Malik, Sudhir, Meehan, Samuel, Lieret, Kilian, Oan Evans, Meirin, Villanueva, Michel H., Katz, Daniel S., Stewart, Graeme A., Elmer, Peter, Aziz, Sizar, Bellis, Matthew, Bianchi, Riccardo Maria, Bianco, Gianluca, Bonilla, Johan Sebastian, Burger, Angela, Burzynski, Jackson, Chamont, David, Feickert, Matthew, Gadow, Philipp, and Gruber, Bernhard Manfred

34. Software Training in HEP

Author

Malik, Sudhir, Meehan, Samuel, Lieret, Kilian, Oan Evans, Meirin, Villanueva, Michel H., Katz, Daniel S., Stewart, Graeme A., Elmer, Peter, Aziz, Sizar, Bellis, Matthew, Bianchi, Riccardo Maria, Bianco, Gianluca, Bonilla, Johan Sebastian, Burger, Angela, Burzynski, Jackson, Chamont, David, Feickert, Matthew, Gadow, Philipp, Gruber, Bernhard Manfred, Malik, Sudhir, Meehan, Samuel, Lieret, Kilian, Oan Evans, Meirin, Villanueva, Michel H., Katz, Daniel S., Stewart, Graeme A., Elmer, Peter, Aziz, Sizar, Bellis, Matthew, Bianchi, Riccardo Maria, Bianco, Gianluca, Bonilla, Johan Sebastian, Burger, Angela, Burzynski, Jackson, Chamont, David, Feickert, Matthew, Gadow, Philipp, and Gruber, Bernhard Manfred

35. Software Training in HEP

Author

Malik, Sudhir, Meehan, Samuel, Lieret, Kilian, Oan Evans, Meirin, Villanueva, Michel H., Katz, Daniel S., Stewart, Graeme A., Elmer, Peter, Aziz, Sizar, Bellis, Matthew, Bianchi, Riccardo Maria, Bianco, Gianluca, Bonilla, Johan Sebastian, Burger, Angela, Burzynski, Jackson, Chamont, David, Feickert, Matthew, Gadow, Philipp, Gruber, Bernhard Manfred, Malik, Sudhir, Meehan, Samuel, Lieret, Kilian, Oan Evans, Meirin, Villanueva, Michel H., Katz, Daniel S., Stewart, Graeme A., Elmer, Peter, Aziz, Sizar, Bellis, Matthew, Bianchi, Riccardo Maria, Bianco, Gianluca, Bonilla, Johan Sebastian, Burger, Angela, Burzynski, Jackson, Chamont, David, Feickert, Matthew, Gadow, Philipp, and Gruber, Bernhard Manfred

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