• Tackling large data sets and many parame

    From ScienceDaily@1:317/3 to All on Tue Mar 22 22:30:44 2022
    Tackling large data sets and many parameter problems in particle physics


    Date:
    March 22, 2022
    Source:
    Springer
    Summary:
    A new tool to break down and segment large data set problems and
    problems with many parameters in particle physics could have a
    wide range of applications.



    FULL STORY ==========================================================================
    One of the major challenges in particle physics is how to interpret large
    data sets that consist of many different observables in the context of
    models with different parameters.


    ==========================================================================
    A new paper published in EPJ Plus, authored by Ursula Laa from the
    Institute of Statistics at BOKU University, Vienna, and German Valencia
    from the School of Physics and Astronomy, Monash University, Clayton, Australia, looks at the simplification of large data set and many
    parameter problems using tools to split large parameter spaces into a
    small number of regions.

    "We applied our tools to the so-called B-anomaly problem. In this
    problem there is a large number of experimental results and a theory that predicts them in terms of several parameters," Laa says. "The problem has received much attention because the preferred parameters to explain the observations do not correspond to those predicted by the standard model
    of particle physics, and as such the results would imply new physics."
    Valencia continues by explaining the paper shows how the Pandemonium tool
    can provide an interactive graphical way to study the connections between characteristics in the observations and regions of parameter space.

    "In the B-anomaly problem, for example, we can clearly visualise the
    tension between two important observables that have been singled out in
    the past," Valencia says. "We can also see which improved measurements
    would be best to address that tension.

    "This can be most helpful in prioritising future experiments to address unresolved questions." Laa elaborates by explaining that the methods
    developed and used by the duo are applicable to many other problems,
    in particular for models and observables that are less well understood
    than the applications discussed in the paper, such as multi Higgs models.

    "A challenge is the visualization of multidimensional parameter spaces,
    the current interface only allows the user to visualise high dimensional
    data spaces interactively," Laa concludes. "The challenge is to automate
    this, which will be addressed in future work, using techniques from
    dimension reduction."

    ========================================================================== Story Source: Materials provided by Springer. Note: Content may be edited
    for style and length.


    ========================================================================== Journal Reference:
    1. Ursula Laa, German Valencia. Pandemonium: a clustering tool to
    partition
    parameter space--application to the B anomalies. The
    European Physical Journal Plus, 2022; 137 (1) DOI:
    10.1140/epjp/s13360-021-02310-1 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/03/220322133058.htm

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