abcd-pyhf is a standalone implementation that does not make any assumptions about the underlying analysis and can thus be used or adapted in any analysis using the ABCD method. Although there is no such thing as a perfectly. ABCD method for writing learning outcomes. Part of the reason this technique works so well is that its. methods and materials) and desired goal state (learning outcome).
The likelihood-based version of the ABCD method, also referred to as the “modified ABCD method”, can be used even when there may be significant contamination of the control regions by signal events. The proposed segmentation was performed by means of the Clustering Method using Gaussian mixtures the feature extraction was performed based on the ABCD rule. The ABCDE method is a prioritization strategy invented by productivity expert, Brian Tracy. The regions are defined such that there is a search region, where most signal events are expected to be, and three control regions.
The ABCD method is a common background estimation method used by many physics searches in particle collider experiments and involves defining four regions based on two uncorrelated observables. Abcd-pyhf is an implementation of the likelihood-based version of the ABCD method that utilizes pyhf for hypothesis testing and limit setting.
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