Use and Citations

Citation

The preferred BibTeX entry for citation of pyhf is

@software{pyhf,
  author = "{Heinrich, Lukas and Feickert, Matthew and Stark, Giordon}",
  title = "{pyhf: v0.5.3}",
  version = {0.5.3},
  doi = {10.5281/zenodo.1169739},
  url = {https://github.com/scikit-hep/pyhf},
}

Use in Publications

Updating list of citations and use cases of pyhf:

  • Waleed Abdallah and others. Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2. 2020. arXiv:2003.07868.

  • Gaël Alguero, Sabine Kraml, and Wolfgang Waltenberger. A SModelS interface for pyhf likelihoods. Sep 2020. arXiv:2009.01809.

  • J. Alison and others. Higgs Boson Pair Production at Colliders: Status and Perspectives. In B. Di Micco, M. Gouzevitch, J. Mazzitelli, and C. Vernieri, editors, Double Higgs Production at Colliders. 9 2019. arXiv:1910.00012.

  • B.C. Allanach, Tyler Corbett, and Maeve Madigan. Sensitivity of Future Hadron Colliders to Leptoquark Pair Production in the Di-Muon Di-Jets Channel. Eur. Phys. J. C, 80(2):170, 2020. arXiv:1911.04455, doi:10.1140/epjc/s10052-020-7722-3.

  • Andrei Angelescu, Darius A. Faroughy, and Olcyr Sumensari. Lepton Flavor Violation and Dilepton Tails at the LHC. Eur. Phys. J. C, 80(7):641, 2020. arXiv:2002.05684, doi:10.1140/epjc/s10052-020-8210-5.

  • Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer. MadMiner: Machine learning-based inference for particle physics. Comput. Softw. Big Sci., 4(1):3, 2020. arXiv:1907.10621, doi:10.1007/s41781-020-0035-2.

  • G. Brooijmans and others. Les Houches 2019 Physics at TeV Colliders: New Physics Working Group Report. In 2020. arXiv:2002.12220.

  • Lukas Heinrich, Holger Schulz, Jessica Turner, and Ye-Ling Zhou. Constraining A₄ Leptonic Flavour Model Parameters at Colliders and Beyond. 2018. arXiv:1810.05648.

  • Charanjit K. Khosa, Sabine Kraml, Andre Lessa, Philipp Neuhuber, and Wolfgang Waltenberger. SModelS database update v1.2.3. LHEP, 158:2020, 5 2020. arXiv:2005.00555, doi:10.31526/lhep.2020.158.

  • Jeffrey Krupa and others. GPU coprocessors as a service for deep learning inference in high energy physics. 7 2020. arXiv:2007.10359.

  • ATLAS Collaboration. Reproducing searches for new physics with the ATLAS experiment through publication of full statistical likelihoods. Geneva, Aug 2019. URL: https://cds.cern.ch/record/2684863.

Published Likelihoods

Updating list of HEPData entries for publications using HistFactory JSON likelihoods: