Výsledky vyhledávání - "Lukas A. Heinrich"

Akademický článek

Scalable Declarative HEP Analysis Workflows for Containerised Compute Clouds

Autor: Tibor Šimko, Lukas Alexander Heinrich, Clemens Lange, Adelina Eleonora Lintuluoto, Danika Marina MacDonell, Audrius Mečionis, Diego Rodríguez Rodríguez, Parth Shandilya, Marco Vidal García

Publikováno v: Frontiers in Big Data, Vol 4 (2021)

We describe a novel approach for experimental High-Energy Physics (HEP) data analyses that is centred around the declarative rather than imperative paradigm when describing analysis computational tasks. The analysis process can be structured in the f

Externí odkaz: https://doaj.org/article/4dab27bdc65944d28fdfd79dd55aded4

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Akademický článek

Reinterpretation of LHC Results for New Physics: Status and recommendations after Run 2

Autor: Waleed Abdallah, Shehu Abdus Salam, Azar Ahmadov, Amine Ahriche, Gaël Alguero, Benjamin C. Allanach, Jack Y. Araz, Alexandre Arbey, Chiara Arina, Peter Athron, Emanuele Bagnaschi, Yang Bai, Michael J. Baker, Csaba Balazs, Daniele Barducci, Philip Bechtle, Aoife Bharucha, Andy Buckley, Jonathan Butterworth, Haiying Cai, Claudio Campagnari, Cari Cesarotti, Marcin Chrzaszcz, Andrea Coccaro, Eric Conte, Jonathan M. Cornell, Louie Dartmoor Corpe, Matthias Danninger, Luc Darmé, Aldo Deandrea, Nishita Desai, Barry Dillon, Caterina Doglioni, Juhi Dutta, John R. Ellis, Sebastian Ellis, Farida Fassi, Matthew Feickert, Nicolas Fernandez, Sylvain Fichet, Jernej F. Kamenik, Thomas Flacke, Benjamin Fuks, Achim Geiser, Marie-Hélène Genest, Akshay Ghalsasi, Tomas Gonzalo, Mark Goodsell, Stefania Gori, Philippe Gras, Admir Greljo, Diego Guadagnoli, Sven Heinemeyer, Lukas A. Heinrich, Jan Heisig, Deog Ki Hong, Tetiana Hryn'ova, Katri Huitu, Philip Ilten, Ahmed Ismail, Adil Jueid, Felix Kahlhoefer, Jan Kalinowski, Deepak Kar, Yevgeny Kats, Charanjit K. Khosa, Valeri Khoze, Tobias Klingl, Pyungwon Ko, Kyoungchul Kong, Wojciech Kotlarski, Michael Krämer, Sabine Kraml, Suchita Kulkarni, Anders Kvellestad, Clemens Lange, Kati Lassila-Perini, Seung J. Lee, Andre Lessa, Zhen Liu, Lara Lloret Iglesias, Jeanette M. Lorenz, Danika MacDonell, Farvah Mahmoudi, Judita Mamuzic, Andrea C. Marini, Pete Markowitz, Pablo Martinez Ruiz del Arbol, David Miller, Vasiliki Mitsou, Stefano Moretti, Marco Nardecchia, Siavash Neshatpour, Dao Thi Nhung, Per Osland, Patrick H. Owen, Orlando Panella, Alexander Pankov, Myeonghun Park, Werner Porod, Darren Price, Harrison Prosper, Are Raklev, Jürgen Reuter, Humberto Reyes-González, Thomas Rizzo, Tania Robens, Juan Rojo, Janusz A. Rosiek, Oleg Ruchayskiy, Veronica Sanz, Kai Schmidt-Hoberg, Pat Scott, Sezen Sekmen, Dipan Sengupta, Elizabeth Sexton-Kennedy, Hua-Sheng Shao, Seodong Shin, Luca Silvestrini, Ritesh Singh, Sukanya Sinha, Jory Sonneveld, Yotam Soreq, Giordon H. Stark, Tim Stefaniak, Jesse Thaler, Riccardo Torre, Emilio Torrente-Lujan, Gokhan Unel, Natascia Vignaroli, Wolfgang Waltenberger, Nicholas Wardle, Graeme Watt, Georg Weiglein, Martin J. White, Sophie L. Williamson, Jonas Wittbrodt, Lei Wu, Stefan Wunsch, Tevong You, Yang Zhang, José Zurita

Publikováno v: SciPost Physics, Vol 9, Iss 2, p 022 (2020)

We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct sea

Externí odkaz: https://doaj.org/article/02ccb01d6e0b497a9278961b7e33cf0c

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Publishing statistical models: Getting the most out of particle physics experiments

Publikováno v: SciPost Physics, Vol 12, Iss 1, p 037 (2022)

The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8866c20eee9650479e702b1399fe4736
http://cds.cern.ch/record/2780990

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The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

Publikováno v: SciPost Physics, Vol 12, Iss 1, p 043 (2022)
SciPost physics 12(1), 043 (2022). doi:10.21468/SciPostPhys.12.1.043

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2217453d53e1dc4ddff87b3c69bcc2b5
http://cds.cern.ch/record/2771263

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