TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.7.3.048
TI  - The NFLikelihood: An unsupervised DNNLikelihood from normalizing flows
PY  - 2024/07/31
UR  - https://www.scipost.org/SciPostPhysCore.7.3.048
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 7
IS  - 3
SP  - 048
A1  - Reyes-González, Humberto
AU  - Torre, Riccardo
AB  - We propose the NFLikelihood, an unsupervised version, based on Normalizing Flows, of the DNNLikelihood proposed in [Eur. Phys. J. C 80, 664 (2020)]. We show, through realistic examples, how Autoregressive Flows, based on affine and rational quadratic spline bijectors, are able to learn complicated high-dimensional Likelihoods arising in High Energy Physics (HEP) analyses. We focus on a toy LHC analysis example already considered in the literature and on two Effective Field Theory fits of flavor and electroweak observables, whose samples have been obtained through the HEPFit code. We discuss advantages and disadvantages of the unsupervised approach with respect to the supervised one and discuss a possible interplay between the two.
ER  -

@Article{10.21468/SciPostPhysCore.7.3.048,
	title={{The NFLikelihood: An unsupervised DNNLikelihood from normalizing flows}},
	author={Humberto Reyes-González and Riccardo Torre},
	journal={SciPost Phys. Core},
	volume={7},
	pages={048},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.7.3.048},
	url={https://scipost.org/10.21468/SciPostPhysCore.7.3.048},
}