Open Access
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
Article Number 06026
Number of page(s) 7
Section 6 - Physics Analysis
Published online 16 November 2020
  1. S. Weinberg, Phys. Rev. Lett. 43, 1566 (1979) [Google Scholar]
  2. W. Buchmuller, D. Wyler, Nucl. Phys. B 268, 621 (1986) [Google Scholar]
  3. B. Grzadkowski, M. Iskrzynski, M. Misiak, J. Rosiek, JHEP 10, 085 (2010), 1008.4884 [CrossRef] [Google Scholar]
  4. G. Cowan, K. Cranmer, E. Gross, O. Vitells, Eur. Phys. J. C 71, 1554 (2011), [Erratum: Eur. Phys. J. C 73, 2501 (2013)], 1007.1727 [CrossRef] [EDP Sciences] [Google Scholar]
  5. J. Brehmer, K. Cranmer, I. Espejo, F. Kling, G. Louppe, J. Pavez, Effective LHC measurements with matrix elements and machine learning, in 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (2019), 1906.01578 [Google Scholar]
  6. K. Cranmer, J. Brehmer, G. Louppe, The frontier of simulation-based inference (2019), 1911.01429 [Google Scholar]
  7. K. Kondo, J. Phys. Soc. Jap. 57, 4126 (1988) [CrossRef] [Google Scholar]
  8. T. Martini, P. Uwer, JHEP 09, 083 (2015), 1506.08798 [CrossRef] [Google Scholar]
  9. J. Brehmer, K. Cranmer, G. Louppe, J. Pavez, Phys. Rev. Lett. 121, 111801 (2018), 1805.00013 [CrossRef] [PubMed] [Google Scholar]
  10. J. Brehmer, K. Cranmer, G. Louppe, J. Pavez, Phys. Rev. D 98, 052004 (2018), 1805.00020 [Google Scholar]
  11. J. Brehmer, G. Louppe, J. Pavez, K. Cranmer, Mining gold from implicit models to improve likelihood-free inference (2018), 1805.12244 [Google Scholar]
  12. M. Stoye, J. Brehmer, G. Louppe, J. Pavez, K. Cranmer, Likelihood-free inference with an improved cross-entropy estimator (2018), 1808.00973 [Google Scholar]
  13. K. Cranmer, J. Pavez, G. Louppe, Approximating Likelihood Ratios with Calibrated Discriminative Classifiers (2015), 1506.02169 [Google Scholar]
  14. J. Brehmer, F. Kling, I. Espejo, K. Cranmer, MadMiner, [Google Scholar]
  15. J. Brehmer, F. Kling, I. Espejo, K. Cranmer, Comput. Softw. Big Sci. 4, 3 (2020), 1907.10621 [CrossRef] [Google Scholar]
  16. J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H.S. Shao, T. Stelzer, P. Torrielli, M. Zaro, JHEP 07, 079 (2014), 1405.0301 [CrossRef] [Google Scholar]
  17. T. Sjostrand, S. Mrenna, P.Z. Skands, Comput. Phys. Commun. 178, 852 (2008), 0710.3820 [Google Scholar]
  18. J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, M. Selvaggi (DELPHES 3), JHEP 02, 057 (2014), 1307.6346 [CrossRef] [Google Scholar]
  19. O. Mattelaer, Eur. Phys. J. C 76, 674 (2016), 1607.00763 [CrossRef] [EDP Sciences] [Google Scholar]
  20. The ATLAS Collaboration, A morphing technique for signal modelling in a multidimensional space of coupling parameters (2015), ATL-PHYS-PUB-2015-047 [Google Scholar]
  21. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., Pytorch: An imperative style, high-performance deep learning library (2019), 1912.01703 [Google Scholar]
  22. P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, D. Whiteson, Eur. Phys. J. C 76, 235 (2016), 1601.07913 [CrossRef] [EDP Sciences] [Google Scholar]
  23. J. Brehmer, S. Dawson, S. Homiller, F. Kling, T. Plehn, JHEP 11, 034 (2019), 1908.06980 [CrossRef] [Google Scholar]
  24. J. Brehmer, S. Mishra-Sharma, J. Hermans, G. Louppe, K. Cranmer, Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning (2019), 1909.02005 [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.