Tackling large datasets and many parameter problems in particle physics

One of the main challenges in particle physics is how to interpret large datasets consisting of many different observables in the context of models with different parameters.

A new article published in EPJ Moreauthored by Ursula Laa from the Institute of Statistics, BOKU University, Vienna, and German Valencia from the School of Physics and Astronomy, Monash University, Clayton, Australia, examines the simplification of a large data set and many parameter problems using tools to divide large parameter spaces into a small number of regions.

“We applied our tools to the so-called B-anomaly problem. In this problem, there are a large number of experimental results and a theory that predicts them in terms of several parameters,” explains Laa. “The problem has received a lot of attention because the parameters preferred to explain the observations do not match those predicted by the Standard Model of particle physics, and as such the results would imply new physics.”

Valencia goes on to explain that the paper shows how the Pandemonium tool can provide an interactive graphical way to study the links between features in observations and regions of parameter space.

“In the problem of anomaly B, for example, we can clearly visualize the tension between two important observables that have been distinguished in the past,” Valencia explains. “We can also see which improved measures would be best to deal with this tension.

“This can be very helpful in prioritizing future experiments to answer unresolved questions.”

Laa expands by explaining that the methods developed and used by the duo are applicable to many other problems, especially for models and observables less well understood than the applications discussed in the article, such as multi Higgs models.

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

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