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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