PhyStat-$\nu$ 2016 at the IPMU: Summary of Discussions

TL;DR

The paper summarizes discussions from the 2016 PhyStat-$ u$ workshop, highlighting statistical challenges and developments in neutrino physics, fostering collaboration between physicists and statisticians to improve data analysis methods.

Contribution

It provides a comprehensive overview of statistical issues in neutrino physics discussed at the workshop, serving as a reference for current and future statistical approaches in the field.

Findings

Identification of key statistical challenges in neutrino data analysis

Discussion on the need for advanced statistical methods for larger datasets

Emphasis on interdisciplinary collaboration for improved data interpretation

Abstract

The presentations, discussions and findings from the inaugural `PhyStat-$\nu$' workshop held at the Kavli Institute for the Physics and Mathematics of the Universe (IPMU) near Tokyo in 2016 are described. PhyStat-$\nu$ was the first workshop to focus solely on statistical issues across the broad range of modern neutrino physics, bringing together physicists who are active in the analysis of neutrino data with experts in statistics to explore statistical issues in the field. It is a goal of PhyStat-$\nu$ to help serve the neutrino physics community by providing a forum within which such statistical issues can be discussed and disseminated broadly. This paper is adapted from a summary document that was initially circulated amongst the participants soon after the workshop. Another PhyStat-$\nu$ workshop is being held at CERN in January 2019, building on the discussions in 2016. Advances in experimental neutrino physics in recent years have led to much larger datasets and more diversity in the properties of neutrinos that are being investigated. The discussions here raised several areas where improved statistical errors and more complicated interpretations of the data require statistical methods to be revisited, as well as topics where broader discussions between experimentalists, phenomenologists and theorists will required, which are summarised here. It is important to record the state of the field as it stands today, as much is expected to change over the coming years, including the emergence of more inter-collaborational studies and increasing sophistication in global parameter fitting and model selection methods. The document is also intended to serve as a reference for pedagogical material for those who are new to the use of modern statistical techniques to describe experimental data, as well as those who are well-versed in these techniques and wish to apply them to new data.