Open Access
Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
---|---|---|
Article Number | 06016 | |
Number of page(s) | 7 | |
Section | Physics Analysis Tools | |
DOI | https://doi.org/10.1051/epjconf/202429506016 | |
Published online | 06 May 2024 |
- P. Elmer, M. Neubauer, M.D. Sokoloff, Strategic Plan for a Scientific Software Innovation Institute (S2I2) for High Energy Physics (2017), arXiv:1712.06592 [physics.comp-ph] [Google Scholar]
- A. Held, O. Shadura, PoS ICHEP2022, 235 (2022) [Google Scholar]
- A. Held, O. Shadura (organizers), IRIS-HEP AGC Tools 2021 Workshop, https://indico.cern.ch/event/1076231/ (2021) [Google Scholar]
- A. Held, O. Shadura (organizers), IRIS-HEP AGC Tools 2022 Workshop, https://indico.cern.ch/event/1126109/ (2022) [Google Scholar]
- CMS Data preservation and open access group, Getting Started with CMS 2015 Open Data, https://opendata.cern.ch/docs/cms-getting-started-2015 (2022) [Google Scholar]
- G. Petrucciani, A. Rizzi, C. Vuosalo, on behalf of the CMS Collaboration, Journal of Physics: Conference Series 664, 072052 (2015) [CrossRef] [Google Scholar]
- J. Elmsheuser et al., EPJ Web Conf. 245, 06014 (2020) [Google Scholar]
- A. Rizzi, G. Petrucciani, M. Peruzzi (CMS), EPJ Web Conf. 214, 06021 (2019) [CrossRef] [EDP Sciences] [Google Scholar]
- A. Held, O. Shadura, M. Feickert, J. Chakraborty, M. Proffitt, K. Choi, A. Novak, D. Koch, M. Adamec, S. Chopra et al., Analysis Grand Challenge, https://doi. org/10.5281/zenodo.7274936 [Google Scholar]
- G.A. Stewart, P. Elmer, G. Eulisse, L. Gouskos, S. Hageboeck, A.R. Hall, L. Heinrich, A. Held, M. Jouvin, T.J. Khoo et al., HSF IRIS-HEP Second Analysis Ecosystem Workshop Report (2022), https://doi.org/10.5281/zenodo.7003963 [Google Scholar]
- E. Kauffman, A. Held, O. Shadura, Analysis Grand Challenge Documentation, https://agc.readthedocs.io/en/latest/ (2023) [Google Scholar]
- E. Rodrigues et al., EPJ Web Conf. 245, 06028 (2020) [Google Scholar]
- B. Galewsky, R. Gardner, L. Gray, M. Neubauer, J. Pivarski, M. Proffitt, I. Vukotic, G. Watts, M. Weinberg, EPJ Web Conf. 245, 04043 (2020) [Google Scholar]
- M. Proffitt, G. Watts, EPJ Web Conf. 251, 03068 (2021) [Google Scholar]
- L. Gray, N. Smith, B. Tovar, A. Novak, J. Chakraborty, P. Fackeldey, N. Hartmann, G. Watts, D. Thain, G. Stark et al., coffea, https://doi.org/10.5281/zenodo. 3266454 [Google Scholar]
- Dask Development Team, Dask: Library for dynamic task scheduling, https://dask.org (2016) [Google Scholar]
- R. Brun, F. Rademakers, Nucl. Instrum. Meth. A 389, 81 (1997) [Google Scholar]
- J. Pivarski, H. Schreiner, A. Hollands, P. Das, K. Kothari, A. Roy, J. Ling, N. Smith, C. Burr, G. Stark, Uproot, https://doi.org/10.5281/zenodo.4340632 [Google Scholar]
- J. Pivarski, I. Osborne, I. Ifrim, H. Schreiner, A. Hollands, A. Biswas, P. Das, S. Roy Choudhury, N. Smith, M. Goyal, Awkward Array, https://doi.org/10. 5281/zenodo.4341376 [Google Scholar]
- H. Schreiner, H. Dembinski, A. Goel, J. Gohil, S. Liu, J. Eschle, C. Maji, A. Novak, C. Burr, D. Davis et al., boost-histogram, https://doi.org/10.5281/zenodo. 3492034 [Google Scholar]
- H. Schreiner, S. Liu, A. Goel, hist, https://doi.org/10.5281/zenodo.4057112 [Google Scholar]
- N. Smith, Correctionlib (2022), https://doi.org/10.5281/zenodo.7129907 [Google Scholar]
- NVIDIA Corporation, Triton Inference Server: An Optimized Cloud and Edge Inferencing Solution., https://github.com/triton-inference-server/server [Google Scholar]
- A. Held, M. Feickert, H. Schreiner, L. Henkelmann, A. Hollands, E. Kauffman, N. Simpson, R. Mueller, cabinetry, https://doi.org/10.5281/zenodo.4742752 [Google Scholar]
- L. Heinrich, M. Feickert, G. Stark, pyhf, https://doi.org/10.5281/zenodo. 1169739 [Google Scholar]
- L. Heinrich, M. Feickert, G. Stark, K. Cranmer, Journal of Open Source Software 6, 2823 (2021) [CrossRef] [Google Scholar]
- A. Novak et al., mplhep (2022), https://doi.org/10.5281/zenodo.3766157 [Google Scholar]
- T. Chen, C. Guestrin, XGBoost: A Scalable Tree Boosting System, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016), https://doi.org/10.1145/2939672.2939785 [Google Scholar]
- M.A. Zaharia, A. Chen, A. Davidson, A. Ghodsi, S.A. Hong, A. Konwinski, S. Murching, T. Nykodym, P. Ogilvie, M. Parkhe et al., IEEE Data Eng. Bull. 41, 39 (2018) [Google Scholar]
- O. Shadura, A. Held, First performance measurements with the Analysis Grand Challenge, in 21th International Workshop on Advanced Computing and Analysis Techniques in Physics Research: AI meets Reality (2023), https://doi.org/10.48550/ arXiv.2304.05214 [Google Scholar]
- M. Adamec, G. Attebury, K. Bloom, B. Bockelman, C. Lundstedt, O. Shadura, J. Thiltges, EPJ Web Conf. 251, 02061 (2021) [Google Scholar]
- T. Kluyver, B. Ragan-Kelley, F. Pérez, B. Granger, M. Bussonnier, J. Frederic, K. Kelley, J. Hamrick, J. Grout, S. Corlay et al., Jupyter Notebooks - a publishing format for reproducible computational workflows, in Positioning and Power in Academic Publishing: Players, Agents and Agendas (2016), https://dx.doi.org/10.3233/ 978-1-61499-649-1-87 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.