Login

PHYSTAT-nu 2019

22–25 Jan 2019
CERN
Europe/Zurich timezone

Contact

Contribution List

85 out of 85 displayed
Export to PDF
  1. 45. Introductory Statistics Course - Part I
    Louis Lyons (Imperial College (GB))
    22/01/2019, 13:30
  2. 46. Introductory Statistics Course - Part II
    Glen Cowan (Royal Holloway, University of London)
    22/01/2019, 14:45
  3. 4. Bayesian techniques
    Prof. James Berger (Duke University)
    22/01/2019, 17:00
  4. 31. Introduction to PHYSTAT-nu
    Olaf Behnke (Deutsches Elektronen-Synchrotron (DE))
    23/01/2019, 09:00
  5. 5. Introduction to neutrino physics
    Alain Blondel (Universite de Geneve (CH))
    23/01/2019, 09:10
  6. 6. Overview of stastistics for neutrino physics
    Yoshi Uchida (Imperial College London)
    23/01/2019, 09:50
  7. 10. Long-baseline experiments
    Alexander Himmel (Fermilab)
    23/01/2019, 11:00
  8. 11. Atmospheric neutrinos
    Christophe Bronner (University of Tokyo)
    23/01/2019, 11:30
  9. 12. Reactor experiments
    Chao Zhang (Brookhaven National Laboratory (US))
    23/01/2019, 12:00
  10. 16. Model building, analysing and treatment of systematic uncertainties in modern HEP experiments
    Wouter Verkerke (Nikhef National institute for subatomic physics (NL))
    23/01/2019, 14:00
  11. 17. Practical experiences with ATLAS/CMS Higgs combine procedures
    Nicholas Wardle (Imperial College (GB))
    23/01/2019, 14:30
  12. 18. The neutrino experiment experience
    Prof. Constantinos Andreopoulos (University of Liverpool and STFC/RAL), Costas Andreopoulos (University of Liverpool & STFC Rutherford Appleton Laboratory)
    23/01/2019, 15:00
  13. 32. Discussion
    23/01/2019, 15:30
  14. 13. Fits to large data sets
    Dr Stefano Gariazzo (IFIC-CSIC/University of Valencia)
    23/01/2019, 16:10
  15. 14. Cosmological neutrinos
    Tim Ruhe (TU Dortmund)
    23/01/2019, 16:40
  16. 15. Direct neutrino mass measurement
    Thierry Lasserre (CEA)
    23/01/2019, 17:10
  17. 29. Summary of the day / discussions
    Tom Junk (Fermi National Accelerator Lab. (US))
    23/01/2019, 17:40
  18. 19. Neutrino-less double-beta decay experiments
    Dr Matteo Agostini (Technical University Munich)
    24/01/2019, 09:00
  19. 80. Supernova neutrino signal detection in the NOvA experiment:
    Andrey Sheshukov (JINR)
    24/01/2019, 09:30
  20. 34. Almost-perfect signal detection
    Prof. Anthony Davison (EPFL )
    24/01/2019, 10:00
  21. 83. Uncertainty in the Reactor Neutrino Spectrum and the Mass Hierarchy
    Emilio Ciuffoli (IMP, CAS)
    24/01/2019, 11:00
  22. 84. Look-elsewhere effect in neutrino oscillation searches:
    Phillip Litchfield (Imperial College, London)
    24/01/2019, 11:15
  23. 95. Reactor Anti-neutrino Data in Global Analyses
    Alvaro Hernandez Cabezudo (Karlsruhe Institute of Technology (KIT))
    24/01/2019, 11:30
  24. 85. Statistical issues on the neutrino Mass Hierarchy determination
    Fatma Helal Sawy (ENHEP Egyptian Network of High Energy Physics (EG)), Fatma Sawy (INFN Padova)
    24/01/2019, 11:45
  25. 82. Short-Baseline experiments
    Prof. Georgia Karagiorgi (CU)
    24/01/2019, 12:00
  26. 96. Introduction to Unfolding
    Mikael Kuusela (Carnegie Mellon University (US))
    24/01/2019, 14:00
  27. 22. The collider experience with unfolding
    Stefan Schmitt (Deutsches Elektronen-Synchrotron (DE))
    24/01/2019, 14:30
  28. 23. The neutrino experiment experience with unfolding
    Philip Andrew Rodrigues (University of Oxford (GB))
    24/01/2019, 15:00
  29. 35. Discussion
    24/01/2019, 15:30
  30. 86. Regularisation for T2K cross-section analyses
    Stephen Dolan (University of Oxford)
    24/01/2019, 16:10
  31. 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
  32. 88. Statistical Models with Uncertain Error Parameters
    Glen Cowan (Royal Holloway, University of London)
    24/01/2019, 17:00
  33. 89. Review of Linear Algebra Applications in Some Recent Neutrino Experiments
    Xin Qian (Brookhaven National Laboratory)
    24/01/2019, 17:25
  34. 30. Summary of the day / discussions
    Tom Junk (Fermi National Accelerator Lab. (US))
    24/01/2019, 18:00
  35. 24. Introduction to machine learning
    Michael Aaron Kagan (SLAC National Accelerator Laboratory (US))
    25/01/2019, 09:00
  36. 25. The collider experience
    Maurizio Pierini (CERN)
    25/01/2019, 09:30
  37. 26. The neutrino experiment experience
    Saul Alonso Monsalve (University Carlos III (ES))
    25/01/2019, 10:00
  38. 38. Theoretical aspects of Machine Learning
    Chad Shafer (CMU)
    25/01/2019, 11:00
  39. 39. Discussion
    25/01/2019, 11:30
  40. 90. Machine Learning methods for JUNO Experiment
    Yu Xu
    25/01/2019, 11:40
  41. 91. Simulating Light in Large Volume Detectors Using Metropolis Light Transport
    Gabriel Collin
    25/01/2019, 11:55
  42. 92. A GPU-based framework for multi-variate analysis in particle physics
    Xuefeng Ding (Gran Sasso Science Insitute (INFN)), Xuefeng Ding (INFN - National Institute for Nuclear Physics)
    25/01/2019, 12:10
  43. 93. Efficient Neutrino Oscillation Parameter Inference with Gaussian Processes
    Bannanje Nitish Nayak (University of California Irvine (US))
    25/01/2019, 14:00
  44. 94. Reflections on the 20th year of Feldman-Cousins: Hypothesis testing of a point null vs a continuous alternative
    Robert Cousins Jr (University of California Los Angeles (US)), Robert Cousins Jr (University of California Los Angeles (US))
    25/01/2019, 14:25
  45. 27. Physicist's summary talk
    Kevin McFarland (University of Rochester)
    25/01/2019, 15:00
  46. 28. Statistician's summary talk
    David van Dyk (Imperial College)
    25/01/2019, 15:35
  47. 47. Goodbye
    Davide Sgalaberna (CERN)
    25/01/2019, 16:10
  48. 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...

    Go to contribution page
  49. 2. A new unified perspective on the problem of limited Monte Carlo for likelihood calculations
    Dr Thorsten Glüsenkamp (University of Erlangen-Nuremberg)

    Parameter estimation in high-energy neutrino/HEP experiments often involves Monte-Carlo simulations to evaluate likelihood functions, a typical use case being forward-folding Poisson likelihood analyses. Limited Monte Carlo samples induce statistical uncertainty into this problem. This is sometimes solved by the classical Ansatz of Barlow/Beeston (1993) - an approach that can be thought of as...

    Go to contribution page
  50. 81. Almost-perfect signal detection
    Prof. Anthony Davison (EPFL )
  51. 53. Analysis review of JUNO Experiment
    Dr Jiajie Ling (Sun Yat-sen University)

    After three neutrino mixing angles and two independent neutrino mass squared differences have been measured, neutrino physics has entered the precision era. Neutrino mass hierarchy and CP violation phase are the two remaining important unknown properties which could be measured by next generation neutrino oscillation experiments. Jiangmen Underground Neutrino Observatory (JUNO) is constructing...

    Go to contribution page
  52. 58. Analytical Multivariate Fit and Sensitivity Studies in the Borexino Solar Neutrino Analysis
    Mr Omer Penek (IKP-2 Forschungszentrum Juelich)

    The Borexino detector, located at the Laboratori Nazionali del Gran Sasso in Italy, is a liquid scintillator detector with a primary goal to measure solar neutrinos. The spectral fit of the energy spectrum has been performed for the first time in the whole energy range from 0.19 up to 2.93 MeV. This approach made it possible to obtain the fluxes of 7Be, pp, and pep solar neutrinos...

    Go to contribution page
  53. 42. Bayesian analysis of multichannel measurements
    Dr Igor Smirnov (Petersburg Nuclear Physics Institute (Russia))

    A typical experiment in high energy physics is considered. The result of the experiment is assumed to be a histogram consisting of bins or channels with numbers of corresponding registered events. The expected background and expected signal shape or acceptance are measured in separate auxiliary experiments, or calculated by the Monte Carlo method with finite sample size, and hence with finite...

    Go to contribution page
  54. 36. Discussion
  55. 50. Efficient Neutrino Oscillation Parameter Inference with Gaussian Process
    Mr Nitish Nayak (University of California-Irvine)

    Neutrinos are tiny sub-atomic particles that carry no electrical charge and interact with matter only through the weak nuclear force, which makes them extremely hard to detect. There are three distinct types of neutrinos, called "flavors": ($\nu_e$, $\nu_\mu$, $\nu_\tau$), each of which can "oscillate" into the other with a detectable probability. Many experiments have been set-up to measure...

    Go to contribution page
  56. 66. ESSnuSB, a new facility to discover CP violation in leptonic sector in Europe.
    Dr Eric Baussan (IPHC)

    The large value of the last mixing angle of the PMNS mixing matrix measured by reactor experiments enable the search for CP violation in leptonic sector with a new generation of neutrino super beams. The ESSnuSB project proposes to use the European Spallation Source (ESS) based in Sweden to elaborate a high intensity neutrino super beam. The LINAC of this facility, under construction, will...

    Go to contribution page
  57. 77. GNA -- high performance fitter for large-scale analyses.
    Mr Konstantin Treskov (Joint Institute for Nuclear Research)

    Statistical data analysis of modern experiments deals with large-scale models with hundreds of nuisance parameters. Direct application on statistical methods such as likelihood profiling, Bayesian methods and especially Feldman-Cousins procedure can lead to extensive numerical computations.
    In order to facilitate the growing need for flexible and high-performance analysis tools to deal...

    Go to contribution page
  58. 54. Implementation of a Feldman-Cousins Framework on Supercomputers and the Impact on NOvA Oscillation Results
    Steven Calvez (Colorado State University)

    The NOvA long-baseline neutrino and anti-neutrino oscillation results, by the nature of the measurements, feature low event counts in both the appearance and disappearance channels. When the low event counts are combined with the form of the fits that are needed to describe PMNS mixing, physical boundaries develop and have the potential to skew the statistical behavior of extracted parameters...

    Go to contribution page
  59. 7. Introduction to unfolding
    Mikael Kuusela (Statistical and Applied Mathematical Sciences Institute (US), University of North Carolina at Chapel Hill (US), Helsinki Institute of Physics (FI), Carnegie Mellon University (US))
  60. 21. Introduction to unfolding
    Mikael Kuusela (Statistical and Applied Mathematical Sciences Institute (US), University of North Carolina at Chapel Hill (US), Helsinki Institute of Physics (FI), Carnegie Mellon University (US))
  61. 72. Look-elsewhere effect in neutrino oscillation searches
    Dr Phillip Litchfield (Imperial College, London)

    The look-elsewhere effect is a familiar issue to collider experiments, and occurs, for example, when a search is performed for a new resonance that is not predicted under the null hypothesis. Many searches for sterile neutrinos employ a similar strategy: looking for evidence of oscillation between two active flavours at an unknown mass-squared splitting. As with collider searches, this ...

    Go to contribution page
  62. 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%...

    Go to contribution page
  63. 52. Non-Parametric Bayesian Event Reconstruction in Super-Kamiokande
    Mr Artur Sztuc (Imperial College London)

    We present an update to a method for non-parametric, Bayesian neutrino event reconstruction for the Super-Kamiokande detector with the existing fiTQun event reconstruction framework, first reported at PhyStat-nu 2016 at the IPMU. Particle properties are determined in a way where the number of Cherenkov rings to be reconstructed, and therefore the number of parameters, is one of the unknowns....

    Go to contribution page
  64. 71. NOvA's $\nu_e + \nu_\mu$ oscillation analysis
    Liudmila Kolupaeva

    NOvA is an experiment devoted to studying neutrino oscillations in the NuMI neutrino beam from FNAL (USA). It is a long-baseline experiment consisting of two functionally identical, finely granulated detectors which are separated by 810 km of Earth crust and sited at 14 mrad off the beam axis. By measuring the transition probabilities P($\nu_\mu \rightarrow \nu_e$) and P($\nu_\mu \rightarrow...

    Go to contribution page
  65. 43. Pseudosignificances as figures of merit: a systematic study
    Dr Pietro Vischia (Universite Catholique de Louvain (UCL) (BE))

    Optimization problems in HEP often involve maximizing a measure of the sensitivity of the analysis to an hypothesis with respect to another; the latter is referred to as null hypothesis and in a frequentist framework is tested against the former, which is referred to as alternative hypothesis.

    In most cases, it is desirable to fully compute the expected frequentist significance, accounting...

    Go to contribution page
  66. 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...

    Go to contribution page
  67. 57. Regularisation for T2K cross-section analyses

    Due to the limited number of events available and finite resolution of detectors, unfolding measurements of neutrino interaction cross sections is a statistically ill-posed problem. To address this, T2K analyses employ several different methods of regularisation. A particular effort has been made to mitigate the potential bias associated with choosing the termination of...

    Go to contribution page
  68. 51. ReMU -- Data sharing in a forward-folding framework
    Lukas Koch (Science and Technology Facilities Council)

    The canonical way to make experimental neutrino-cross-section data available for comparison with different theories and other experiments is to unfold it.
    The aim of this is to remove the detector effects and efficiencies from the data to release distributions of variables of interest in "truth space".
    Depending on the available data, the detector properties, and the variables of interest, it...

    Go to contribution page
  69. 41. ReSyst: a novel technique to Reduce the Systematic uncertainty for precision measurementsReSyst: a novel technique to Reduce the Systematic uncertainty for precision measurements
    Petra Van Mulders (Vrije Universiteit Brussel (BE))

    We are in an era of precision measurements at the Large Hadron Collider. The precision that can be achieved on some of the measurements is limited however due to large systematic uncertainties. This paper introduces a new technique to reduce the systematic uncertainty by quantifying the systematic impact of single events and correlating it with event observables to identify parts of the phase...

    Go to contribution page
  70. 3. Review of Linear Algebra Applications in Some Recent Neutrino Experiments
    Dr Xin Qian (Brookhaven National Laboratory)

    Linear algebra has been widely used in physics analysis of high-energy experiments. In this talk, I am going to review some of its recent usage in detector signal processing, noise filtering, event reconstruction, and data unfolding. In particular, its connections to various numerical and analytical techniques including the Fast Fourier Transformation, the Compressed Sensing, and the...

    Go to contribution page
  71. 55. Searches for New Physics in the Short Baseline Neutrino Program at Fermilab with SBNfit
    Davio Cianci (Columbia University)

    SBNFit is a software framework designed for the purpose of performing simultaneous fits across data from multiple, correlated distributions. Although extremely modular and generic, an ideal application is the searches for new physics at the Short Baseline Neutrino (SBN) experimental program at Fermilab. SBN comprises three experiments: MicroBooNE, SBND, and ICARUS, each of which can perform...

    Go to contribution page
  72. 20. Short-Baseline experiments
    Georgia Karagiorgi (Columbia), Georgia Karagiorgi (University of Manchester), Georgia Karagiorgi (MIT)
  73. 74. Simulating Light in Large Volume Detectors Using Metropolis Light Transport
    Gabriel Collin

    In gigaton scale neutrino detectors, such as the IceCube experiment, interaction products are detected by the Cherenkov radiation emitted by their passage through the detector medium. Simulating this propagation of light is traditionally approached through ray tracing. This is complicated by the sparsity of the detector: the vast majority of light rays are scattered and absorbed by the...

    Go to contribution page
  74. 40. Statistica issues on the neutrino Mass Hierarchy determination
    Luca Stanco (Universita e INFN, Padova (IT))

    The determination of the neutrino Mass Hierarchy (MH) is one of the main goals of the major current and future neutrino experiments. The analysis usually proceeds from the $\Delta\chi^2$ estimator. This estimator may show several draw-backs and concerns, together with a debatable strategy.
    The author will discuss the needs for a clear strategy as well as the related sensitivity evaluation.
    The...

    Go to contribution page
  75. 65. Statistical methods and issues in sterile neutrino searches
    Birgit Neumair (Technical University of Munich (TUM))

    The statistical issues related to the search for sterile neutrinos are reviewed with focus on short-baseline appearance and disappearance experiments. The sensitivities for limit setting and signal discovery are discussed along with their dependency on the experimental parameters, including the signal rate and the spectral shape. Our baseline analysis is built on a profile-likelihood test...

    Go to contribution page
  76. 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...

    Go to contribution page
  77. 33. Statistician talk 1
  78. 37. Status of the MicroBooNE low-energy excess search and systematic uncertainties evaluation
    Mr Stefano Roberto Soleti

    MicroBooNE (the Micro Booster Neutrino Experiment) is a liquid argon time-projection chamber (LArTPC) experiment currently running at Fermilab, and is designed to study short-baseline neutrino physics. It aims to address the nature of the anomalous excess of low-energy electron-neutrino-like events observed by the previous MiniBooNE experiment. We will present the status of the low-energy...

    Go to contribution page
  79. 73. Supernova neutrino signal detection in the NOvA experiment
    Andrey Sheshukov (JINR)

    Detection of galactic supernova neutrinos can provide a valuable information on  both supernova physics and neutrino properties. 
    The large liquid-scintillator detectors of NOvA experiment can be used for such measurement. A dedicated triggering system was designed to perform real-time reconstruction and search for supernova neutrino interaction candidates, extract the time profile of the...

    Go to contribution page
  80. 8. The collider experience with unfolding
  81. 1. Treatment of material radioassay measurements in projecting sensitivity for low-background experiments
    Raymond Hei-Man Tsang (Pacific Northwest National Laboratory)

    Low-background experiments typically perform radioassay on detector construction materials, and use the resulting radioimpurity estimates to project experiment sensitivity. However, as the radiopurity of the materials improves and approaches the detection limit of the radioassay techniques, the radioimpurity concentration of the materials cannot be conclusively determined. Instead, only an...

    Go to contribution page
  82. 76. Tuning the pion production on free nuclei with GENIEv3
    Julia Tena Vidal (University of Liverpool)

    GENIE (http://www.genie-mc.org/) is one of the most used event generator for neutrino experiments. The collaboration has a continuous effort to improve its prediction by adding new models and tune them against data. For the future experiments, the description of single and double pion production is fundamental and yet there is not a single model that describes simultaneously resonant,...

    Go to contribution page
  83. 69. Uncertainty in the Reactor Neutrino Specrum and the Mass Hierarchy Determination
    Prof. Emilio Ciuffoli (IMP, CAS)

    In the next years reactor neutrino experiments, like JUNO or RENO 50, will attempt to determine the mass hierarchy. There are many problems that need to be overcome to accomplish this task, one of them is the theoretical uncertainty in the spectrum. Indeed, as became clear a few years ago with the measurement of the “5 MeV bump”, the theoretical models that predict the reactor neutrino...

    Go to contribution page
  84. 9. Unfolding in neutrino experiments
    Philip Andrew Rodrigues (University of Oxford (GB))
  85. 67. Updated Results for the Search for $\bar{\nu}_{\mu} \rightarrow \bar{\nu}_{e}$ Oscillations from T2K in the 3-flavour Framework
    Mr Francis Bench (The University of Liverpool)

    I report the results of a search for $\bar{\nu}_{\mu} \rightarrow \bar{\nu}_{e}$ oscillations at the T2K experiment in a 3-flavour framework. An exposure of $1.49 \times 10^{21}$ Protons On Target (POT) is used in $\nu$ mode and $1.63 \times 10^{21}$ POT in $\bar{\nu}$ mode; an increase of $46\%$ in the $\bar{\nu}$ exposure compared to results reported in June 2018.

    Results are reported for a...

    Go to contribution page