New features: More observables for LHEReader.add_observable: Users can use "p_truth" to access particles before smearing, and (at least with XML parsing) there are new global observables "alpha_qcd", "alpha_qed", "scale". LHEReader.add_observable_from_function() now accepts functions that take unsmeared particles as first argument. Bug fixes: Fixed bug in sample_train_ratio() with return_individual_n_effective=True Fixed bug in DelphesReader when no events survive cuts Tutorials and documentation: Removed outdated Docker link from docs Changed morphing basis in particle physics tutorial to work around a weird bug inn the MG-Pythia interface, see #371 Internal changes: Refactored LHE parsing. LHE files are now not read into memory a...
New features: Clean separation between training and validation events: the SampleAugmenter function...
New features: Prototype implementation of joint score computations with finite differences (with Ma...
New features: plot_histograms() can now also visualize observed data / Asimov data. In 2D parameter...
New features: LHEReader.add_observable_from_function() now expects functions with signature observa...
New features: DelphesProcessor supports a "generator truth" analysis. With this option, observables...
New features: Expanded systematics system. Users now declare systematics with MadMiner.add_systemat...
Bug fixes: Fixed particles phi property access after scikit-hep/vector migration (not callable anym...
New features: AsymptoticLimits functions are more memory efficient. New keyword histo_theta_batchsi...
New features: 'tau' object for LHEProcessor observables / cuts. Breaking / API changes: New verbo...
New features: Nuisance parameters to model systematic uncertainties, currently only from PDF / scal...
New features: New keyword order in MadMiner.run(), which can be set to 'nlo' to set the systematics...
New features: New madminer.likelihood class will let the user define more powerful likelihood funct...
New features: ParameterizedRatioEstimator now optionally rescales parameters (theta) to zero mean a...
New features: New Fisher information geometry functionality in the madminer.fisherinformation.Infor...
New features: AsymptoticLimits now supports the SALLINO method, estimating the likelihood with one-...
New features: Clean separation between training and validation events: the SampleAugmenter function...
New features: Prototype implementation of joint score computations with finite differences (with Ma...
New features: plot_histograms() can now also visualize observed data / Asimov data. In 2D parameter...
New features: LHEReader.add_observable_from_function() now expects functions with signature observa...
New features: DelphesProcessor supports a "generator truth" analysis. With this option, observables...
New features: Expanded systematics system. Users now declare systematics with MadMiner.add_systemat...
Bug fixes: Fixed particles phi property access after scikit-hep/vector migration (not callable anym...
New features: AsymptoticLimits functions are more memory efficient. New keyword histo_theta_batchsi...
New features: 'tau' object for LHEProcessor observables / cuts. Breaking / API changes: New verbo...
New features: Nuisance parameters to model systematic uncertainties, currently only from PDF / scal...
New features: New keyword order in MadMiner.run(), which can be set to 'nlo' to set the systematics...
New features: New madminer.likelihood class will let the user define more powerful likelihood funct...
New features: ParameterizedRatioEstimator now optionally rescales parameters (theta) to zero mean a...
New features: New Fisher information geometry functionality in the madminer.fisherinformation.Infor...
New features: AsymptoticLimits now supports the SALLINO method, estimating the likelihood with one-...
New features: Clean separation between training and validation events: the SampleAugmenter function...
New features: Prototype implementation of joint score computations with finite differences (with Ma...
New features: plot_histograms() can now also visualize observed data / Asimov data. In 2D parameter...