One of the major challenges in particle physics is interpreting large data sets with many different observables in the context of models with different parameters.
A new paper published in EPJ Plus by Ursula Laa from the Institute of Statistics at BOKU University in Vienna and German Valencia from the School of Physics and Astronomy at Monash University in Clayton, Australia, looks at the simplification of large data sets and many parameter problems utilizing tools to split large parameter spaces into a small number of regions.
We applied our tools to the supposed B-anomaly problem. There are a large number of experimental results and a theory that predicts them in terms of several parameters in this problem Laa explains.
The problem has obtained a lot of attention since the preferred parameters for explaining the observations do not correspond to those predicted by the standard model of particle physics, and as a result, the results would imply new physics.
Valencia goes on to say that the paper demonstrates how the Pandemonium tool can provide an interactive graphical way to investigate the relationships between characteristics in observations and regions of parameter space.
For instance, in the B-anomaly problem, we can visualize the tension between two important observables that have been singled out in the past, Valencia says. We can also determine which improved measurements would be most effective in addressing that tension.
This can be extremely useful in prioritizing future experiments to address unanswered questions.
Laa goes on to say that the duo’s methods apply to a wide range of other problems, specifically models and observables that are less well understood than the applications considered in the paper, like multi Higgs models.
A challenge is the visualization of multidimensional parameter spaces; the current interface only allows the user to interactively visualize high dimensional data spaces, Laa concludes. The challenge lies in automating this, which will be addressed in future work utilizing dimension reduction techniques.
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