Zobrazeno 1 - 10
of 2 462
pro vyhledávání: '"Erdmann , Martin"'
Autor:
Alshehri, Azzah, Bürger, Jan, Chopra, Saransh, Eich, Niclas, Eppelt, Jonas, Erdmann, Martin, Eschle, Jonas, Fackeldey, Peter, Farkas, Maté, Feickert, Matthew, Fillinger, Tristan, Fischer, Benjamin, Gerlach, Lino Oscar, Hartmann, Nikolai, Heidelbach, Alexander, Held, Alexander, Ivanov, Marian I, Molina, Josué, Nikitenko, Yaroslav, Osborne, Ianna, Padulano, Vincenzo Eduardo, Pivarski, Jim, Praz, Cyrille, Rieger, Marcel, Rodrigues, Eduardo, Shadura, Oksana, Smieško, Juraj, Stark, Giordon Holtsberg, Steinfeld, Judith, Warkentin, Angela
The second PyHEP.dev workshop, part of the "Python in HEP Developers" series organized by the HEP Software Foundation (HSF), took place in Aachen, Germany, from August 26 to 30, 2024. This gathering brought together nearly 30 Python package developer
Externí odkaz:
http://arxiv.org/abs/2410.02112
Autor:
Bruers, Ben, Cruces, Marilyn, Demleitner, Markus, Duckeck, Guenter, Düren, Michael, Eich, Niclas, Enßlin, Torsten, Erdmann, Johannes, Erdmann, Martin, Fackeldey, Peter, Felder, Christian, Fischer, Benjamin, Fröse, Stefan, Funk, Stefan, Gasthuber, Martin, Grimshaw, Andrew, Hadasch, Daniela, Hannemann, Moritz, Kappes, Alexander, Kleinemühl, Raphael, Kozlov, Oleksiy M., Kuhr, Thomas, Lupberger, Michael, Neuhaus, Simon, Niknejadi, Pardis, Reindl, Judith, Schindler, Daniel, Schneidewind, Astrid, Schreiber, Frank, Schumacher, Markus, Schwarz, Kilian, Streit, Achim, von Cube, R. Florian, Walker, Rod, Walther, Cyrus, Wozniewski, Sebastian, Zhou, Kai
Given the urgency to reduce fossil fuel energy production to make climate tipping points less likely, we call for resource-aware knowledge gain in the research areas on Universe and Matter with emphasis on the digital transformation. A portfolio of m
Externí odkaz:
http://arxiv.org/abs/2311.01169
Autor:
Eich, Niclas, Erdmann, Martin, Fackeldey, Peter, Fischer, Benjamin, Noll, Dennis, Rath, Yannik
The development of an LHC physics analysis involves numerous investigations that require the repeated processing of terabytes of data. Thus, a rapid completion of each of these analysis cycles is central to mastering the science project. We present a
Externí odkaz:
http://arxiv.org/abs/2207.08598
The inference of physical parameters from measured distributions constitutes a core task in physics data analyses. Among recent deep learning methods, so-called conditional invertible neural networks provide an elegant approach owing to their probabi
Externí odkaz:
http://arxiv.org/abs/2110.09493
Autor:
Erdmann, Martin1, Fackeldey, Peter1 peter.fackeldey@erumdatahub.de, Fischer, Benjamin1, Fröse, Stefan2, Nowack, Andreas1, Steinfeld, Judith1, Warkentin, Angela1
Publikováno v:
EPJ Web of Conferences. 5/6/2024, Vol. 295, p1-6. 6p.
Autor:
Benato, Lisa, Buhmann, Erik, Erdmann, Martin, Fackeldey, Peter, Glombitza, Jonas, Hartmann, Nikolai, Kasieczka, Gregor, Korcari, William, Kuhr, Thomas, Steinheimer, Jan, Stöcker, Horst, Plehn, Tilman, Zhou, Kai
Publikováno v:
Comput Softw Big Sci 6, 9 (2022)
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning
Externí odkaz:
http://arxiv.org/abs/2107.00656
Publikováno v:
The European Physical Journal C 81 (2021) 794
We present a novel method to search for structures of coherently aligned patterns in ultra-high energy cosmic-ray arrival directions simultaneously across the entire sky. This method can be used to obtain information on the Galactic magnetic field, i
Externí odkaz:
http://arxiv.org/abs/2101.02890
Autor:
Bister, Teresa, Erdmann, Martin, Glombitza, Jonas, Langner, Niklas, Schulte, Josina, Wirtz, Marcus
We present a new approach for the identification of ultra-high energy cosmic rays from sources using dynamic graph convolutional neural networks. These networks are designed to handle sparsely arranged objects and to exploit their short- and long-ran
Externí odkaz:
http://arxiv.org/abs/2003.13038
Publikováno v:
EPJ Web of Conferences, Vol 295, p 06005 (2024)
The usage of Deep Neural Networks (DNNs) as multi-classifiers is widespread in modern HEP analyses. In standard categorisation methods, the high-dimensional output of the DNN is often reduced to a one-dimensional distribution by exclusively passing t
Externí odkaz:
https://doaj.org/article/75254aa1670543908b436be7ab73e709