Speaker
Description
OmniFold gives you per-event weights instead of a histogram, so you can build whatever distribution you want afterward. But that makes results hard to share. A weight file is just a few hundred unlabeled columns, and readers are left guessing which one is the main result, which columns belong to the same uncertainty estimate, how to combine them, and whether the rows even still match the original data.
omnifold_publication is our attempt at a standard for publishing unbinned results. It stores the data and weights in a standard table format, plus a metadata file that says which weights go together, how to combine them, the binning convention, normalization, row alignment, and a checksum. The package also reads and analyzes results published this way: histograms, uncertainty estimates, correctness checks, and export for publication. We want unbinned measurements to be as easy to reuse as a histogram, without every analysis making up its own format.
