Simple and statistically sound recommendations for analysing physical theories.

Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with statistically unsound ad hoc methods, involving intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these procedures, suggesting where possible simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide any physicists lacking comprehensive statistical training with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden. Our examples can be reproduced with the code publicly available at Zenodo.

Falsifying high-scale leptogenesis at the LHC.

Measuring a nonzero value for the cross section of any lepton number violating (LNV) process would put a strong lower limit on the washout factor for the effective lepton number density in the early Universe at times close to the electroweak phase transition and thus would lead to important constraints on any high-scale model for the generation of the observed baryon asymmetry based on LNV. In particular, for leptogenesis (LG) models with masses of the right-handed neutrinos heavier than the mass scale observed at the LHC, the implied large washout factors would lead to a violation of the out-of-equilibrium condition and exponentially suppress the net lepton number produced in such LG models. We thus demonstrate that the observation of LNV processes at the LHC results in the falsification of high-scale LG models. However, no conclusions about the viability of LG models can be drawn from the nonobservation of LNV processes.