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Binned efficiency maps are however strongly limited by statistics. We propose a neural network approach to learn ratios of local densities to estimate in an optimal fashion efficiencies as a function of a set of parameters. Graph neural network techniques are used to account for the high dimensional correlations between different physics objects in the event. We show in a specific toy model how this method is applicable to produce accurate multidimensional efficiency maps for heavy-flavor tagging classifiers in HEP experiments, including for processes on which it was not trained.<\/jats:p>","DOI":"10.1007\/s41781-021-00059-x","type":"journal-article","created":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T08:09:23Z","timestamp":1622189363000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Efficiency Parameterization with Neural Networks"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9870-2021","authenticated-orcid":false,"given":"Francesco Armando","family":"Di Bello","sequence":"first","affiliation":[]},{"given":"Jonathan","family":"Shlomi","sequence":"additional","affiliation":[]},{"given":"Chiara","family":"Badiali","sequence":"additional","affiliation":[]},{"given":"Guglielmo","family":"Frattari","sequence":"additional","affiliation":[]},{"given":"Eilam","family":"Gross","sequence":"additional","affiliation":[]},{"given":"Valerio","family":"Ippolito","sequence":"additional","affiliation":[]},{"given":"Marumi","family":"Kado","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,28]]},"reference":[{"key":"59_CR1","unstructured":"The ATLAS Collaboration (2019) ATLAS b-jet identification performance and efficiency measurement with $$t {\\bar{t}}$$ events in pp collisions at $${\\sqrt{s}} = {13}{TeV}$$. 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