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Jonas Eschle

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2020 – today

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2023
[j4]
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  - journals/csbs/EschleGGGHHAKLMMBPSSSSV23
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  - https://dblp.org/rec/journals/csbs/EschleGGGHHAKLMMBPSSSSV23
Jonas Eschle, Tamás Gál, Mosè Giordano, Philippe Gras, Benedikt Hegner, Lukas Heinrich, Uwe Hernandez Acosta, Stefan Kluth, Jerry (Jiahong) Ling, Pere Mato, Mikhail Mikhasenko, Alexander Moreno Briceño, Jim Pivarski, Konstantinos Samaras-Tsakiris, Oliver Schulz, Graeme Andrew Stewart, Jan Strube, Vassil Vassilev:
Potential of the Julia Programming Language for High Energy Physics Computing. Comput. Softw. Big Sci. 7(1): 10 (2023)
[j3]
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Julián García Pardiñas, Marta Calvi, Jonas Eschle, Andrea Mauri, Simone Meloni, Martina Mozzanica, Nicola Serra:
GNN for Deep Full Event Interpretation and Hierarchical Reconstruction of Heavy-Hadron Decays in Proton-Proton Collisions. Comput. Softw. Big Sci. 7(1): 12 (2023)
[i2]
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  - journals/corr/abs-2306-03675
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Jonas Eschle, Tamás Gál, Mosè Giordano, Philippe Gras, Benedikt Hegner, Lukas Heinrich, Uwe Hernandez Acosta, Stefan Kluth, Jerry (Jiahong) Ling, Pere Mato, Mikhail Mikhasenko, Alexander Moreno Briceño, Jim Pivarski, Konstantinos Samaras-Tsakiris, Oliver Schulz, Graeme A. Stewart, Jan Strube, Vassil Vassilev:
Potential of the Julia programming language for high energy physics computing. CoRR abs/2306.03675 (2023)
2020
[j2]
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Jonas Eschle, Albert Navarro, Rafael Silva Coutinho, Nicola Serra:
zfit: Scalable pythonic fitting. SoftwareX 11: 100508 (2020)

2010 – 2019

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2019
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Albert Navarro, Jonas Eschle:
phasespace: n-body phase space generation in Python. J. Open Source Softw. 4(42): 1570 (2019)
2018
[i1]
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  - journals/corr/abs-1807-02876
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Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier M. Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir V. Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemyslaw Karpinski, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark S. Neubauer, Harvey B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Andrew Stewart, Bob Stienen, Ian Stockdale, Giles Chatham Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata:
Machine Learning in High Energy Physics Community White Paper. CoRR abs/1807.02876 (2018)

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