Software Open Access

Johann Brehmer; Felix Kling; Irina Espejo; Kyle Cranmer; alexander-held; Zubair

New features:

• Smarter sampling: MadMiner now keeps track of which events where generated (sampled) from which benchmark point (at the MadGraph stage). The new keyword sample_only_from_closest_benchmark in the SampleAugmenter functions and plot_distributions() then allows the user to restrict the unweighting / resampling at some parameter point to events from the closest benchmark point. This can significantly reduce the weights of individual events and thus reduce the variance.
API / breaking changes:
• k-factors are now automatically added when there are subsamples generated at different benchmarks. For instance, if we add a sample with 30k events generated at theta0 and a sample with 70k events generated at theta1, and calculate cross sections from the full sample, MadMiner will automatically apply a k-factor of 0.3 and 0.7 to the two samples.
Bug fixes:
• Various small bug fixes, mostly related to nuisance parameters
Internal changes:

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