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The DNNLikelihood: enhancing likelihood distribution with Deep Learning · Around

2019

The DNNLikelihood: enhancing likelihood distribution with Deep Learning

Coccaro, Andrea, Pierini, Maurizio, Silvestrini, Luca et al.

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We introduce the DNNLikelihood, a novel framework to easily encode, through Deep Neural Networks (DNN), the full experimental information contained in complicated likelihood functions (LFs).

We show how to efficiently parametrise the LF, treated as a multivariate function of parameters and nuisance parameters with high dimensionality, as an interpolating function in the form of a DNN predictor.
We do not use any Gaussian approximation or dimensionality reduction, such as marginalisation or profiling over nuisance parameters, so that the full experimental information is retained.
The procedure applies to both binned and unbinned LFs, and allows for an efficient distribution to multiple software platforms, e.g.

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