Tools for High Energy Physics and Cosmology

Session

Collider

1

3 Nov 2020, 09:00

IP2I, Lyon

55. Review on Feynman rules and matrix elements calculators

Celine Catherine A Degrande (CERN)

03/11/2020, 09:00

57. Review on Monte Carlo based programs

Nishita Desai (Tata Institute of Fundamental Research)

03/11/2020, 09:30

58. Review on collider recasting tools

Benjamin Fuks (Centre National de la Recherche Scientifique (FR))

03/11/2020, 10:00

59. xsec

Jeriek Van den Abeele (University of Oslo)

03/11/2020, 10:30

60. Generative Networks for LHC events

Anja Butter

03/11/2020, 10:50

61. FAST-HEP

Benjamin Krikler (University of Bristol (GB))

03/11/2020, 11:10

62. pyhf: pure-Python implementation of HistFactory with tensors and automatic differentiation

Matthew Feickert (Univ. Illinois at Urbana Champaign (US))

03/11/2020, 11:30

35. Generative Networks for LHC events

Anja Butter

Higgs and colliders

LHC physics crucially relies on our ability to simulate events efficiently from first principles. Modern machine learning, specifically generative networks, will help us tackle simulation challenges for the coming LHC runs. Such networks can be employed within established simulation tools or as part of a new framework. Since neural networks can be inverted, they also open new avenues in LHC analyses.

25. pyhf: pure-Python implementation of HistFactory with tensors and automatic differentiation

Matthew Feickert (Univ. Illinois at Urbana Champaign (US))

Higgs and colliders

The HistFactory p.d.f. template [CERN-OPEN-2012-016] is per-se independent of its implementation in ROOT and it is useful to be able to run statistical analysis outside of the ROOT, RooFit, RooStats framework. pyhf is a pure-python implementation of that statistical model for multi-bin histogram-based analysis and its interval estimation is based on the...