The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows
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Abstract (arXiv)
We propose the NFLikelihood, an unsupervised version, based on Normalizing Flows, of the DNNLikelihood proposed in Ref.[1]. 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 throught the HEPFit code. We discuss advantages and disadvantages of the unsupervised approach with respect to the supervised one and discuss possible interplays of the two.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 18 Sep 2023.
Builds on
11 of 33 references.
References · 11
- 2017Masked Autoregressive Flow for Density Estimationcited by 684
- 2019Neural Spline Flowscited by 596
- 2019Learning Likelihoods with Conditional Normalizing Flowscited by 172
- 2021A Living Review of Machine Learning for Particle Physicscited by 78
- 2022Continual Repeated Annealed Flow Transport Monte Carlocited by 66
- 2015Neural Adaptive Sequential Monte Carlocited by 34
- 2021Flow-based sampling for multimodal and extended-mode distributions in lattice field theorycited by 25
- 2023Advances in machine-learning-based sampling motivated by lattice quantum chromodynamicscited by 23
- 2016Inference Networks for Sequential Monte Carlo in Graphical Modelscited by 20
- 2021HMC with Normalizing Flowscited by 6
- 2023Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Testscited by 5
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.