REDIST is a reweighting method for reinterpreting binned analyses in high-energy physics: it recomputes how an observable’s distribution changes under alterations to the kinematic distributions of a decay channel. It’s built on pyhf for statistical inference, with an optional JAX backend for differentiable likelihood calculations, and integrates with EOS for theoretical predictions. The method is described in arXiv:2402.08417.
Applying the public \(B^+\to K^+\nu\bar\nu\) likelihood to constrain a generic invisible particle \(X\) in \(B^+\to K^+X\), including a heavy dark-sector mediator interpretation.
Three self-contained tutorial notebooks on statistical inference with pyhf: building histogram-based models and including uncertainties, a realistic statistical model for the B+→K+π0 decay, and hypothesis testing. Used for teaching at the Belle II StarterKit, Belle II Physics Week, the Belle...
BibSpire is a Python tool that cleans up bibliography files. It reads a .bib file, searches each entry on INSPIRE-HEP, and replaces the entry with the official INSPIRE citation while keeping the original reference key. It uses title, author, eprint,...
The official Belle II collaboration paper releasing a public, model-agnostic likelihood for the \(B^+\to K^+\nu\bar\nu\) measurement, built with the general method described above. It lets anyone test their own new-physics model against the measurement's data directly, without access to the...