Machine-learned exclusion limits without binning
Abstract Machine-learned likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the experimental sensitivity of high-dimensional data sets. We extend the MLL method by including kernel density estimators (KDE) to avoid binning the clas...
Main Authors: | Ernesto Arganda, Andres D. Perez, Martín de los Rios, Rosa María Sandá Seoane |
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Format: | Article |
Language: | English |
Published: |
SpringerOpen
2023-12-01
|
Series: | European Physical Journal C: Particles and Fields |
Online Access: | https://doi.org/10.1140/epjc/s10052-023-12314-z |
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