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Title: Building and steering binned template fits with cabinetry
The cabinetry library provides a Python-based solution for building and steering binned template fits. It tightly integrates with the pythonic High Energy Physics ecosystem, and in particular with pyhf for statistical inference. cabinetry uses a declarative approach for building statistical models, with a JSON schema describing possible configuration choices. Model building instructions can additionally be provided via custom code, which is automatically executed when applicable at key steps of the workflow. The library implements interfaces for performing maximum likelihood fitting, upper parameter limit determination, and discovery significance calculation. cabinetry also provides a range of utilities to study and disseminate fit results. These include visualizations of the fit model and data, visualizations of template histograms and fit results, ranking of nuisance parameters by their impact, a goodness-of-fit calculation, and likelihood scans. The library takes a modular approach, allowing users to include some or all of its functionality in their workflow.  more » « less
Award ID(s):
1836650
PAR ID:
10354358
Author(s) / Creator(s):
;
Editor(s):
Biscarat, C.; Campana, S.; Hegner, B.; Roiser, S.; Rovelli, C.I.; Stewart, G.A.
Date Published:
Journal Name:
EPJ Web of Conferences
Volume:
251
ISSN:
2100-014X
Page Range / eLocation ID:
03067
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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