PHYSTAT-nu 2019

Session

Contributed talks session

24 Jan 2019, 16:10

CERN

86. Regularisation for T2K cross-section analyses

Stephen Dolan (University of Oxford)

24/01/2019, 16:10

87. A new unified perspective on the problem of limited Monte Carlo for likelihood calculations

Thorsten Glüsenkamp (Universität Erlangen-Nürnberg)

24/01/2019, 16:35

88. Statistical Models with Uncertain Error Parameters

Glen Cowan (Royal Holloway, University of London)

24/01/2019, 17:00

89. Review of Linear Algebra Applications in Some Recent Neutrino Experiments

Xin Qian (Brookhaven National Laboratory)

24/01/2019, 17:25

63. \texttt{GooStats}: A GPU-based framework for multi-variate analysis in particle physics

Xuefeng Ding (Gran Sasso Science Insitute (INFN))

\texttt{GooStats} is a software framework that provides a flexible environment and common tools to implement multi-variate statistical analysis. The framework is based on C++11, CERN ROOT, MINUIT, and \texttt{GooFit}, a popular minimization engine that can run on general purpose graphics processing units. Running a multi-variate analysis in parallel on graphics processing units yields a huge...

56. Machine Learning methods for JUNO Experiment

Mr Yu Xu (IKP2 FZJ)

Jiangmen Underground Neutrino Observatory (JUNO) experiment is a multipurpose neutrino experiment, aiming to determine the unknown neutrino mass ordering and precisely measure the neutrino oscillation parameters. JUNO consists of a central detector with 20 kt liquid-scintillator target and muon veto detectors. 18,000 20-inch PMTs will be installed in central detector, in order to achieve 3%...

75. Reactor Anti-neutrino Data in Global Analyses

Alvaro Hernandez Cabezudo

I will explain the role of reactor anti-neutrino data in the global analyses performed for standard neutrino oscillations (within the NuFit collaboration) and in scenarios involving sterile neutrinos.

The modern medium baseline reactor experiments, L~Km, can determine the neutrino mixing angle \theta_{13} and give a complementary determination to the long baseline experiments of the...

64. Statistical Models with Uncertain Error Parameters

Glen Cowan (Royal Holloway, University of London)

In a statistical analysis in Particle Physics, nuisance parameters can be introduced to take into account various types of systematic uncertainties. The best estimate of such a parameter is often modeled as a Gaussian distributed variable with a given standard deviation (the corresponding "systematic error"). Although the assigned systematic errors are usually treated as constants, in...