New Physics Learning Machine (NPLM) is a novel machine-learning based strategy to detect data departures from a Reference model, with no prior bias on the nature of the physical effect responsible it [1]. The main idea behind the method is to build the log-likelihood-ratio hypothesis test by translating the problem of maximizing the log-likelihood-ratio into the minimization of a loss function.
NPLM has been recently extended in order to deal with the uncertainties of the Standard Model predictions [2]. The new formulation directly builds on the specific maximum-likelihood-ratio treatment of uncertainties as nuisance parameters, that is routinely employed in high-energy physics for hypothesis testing.
After outlining the conceptual foundations of the algorithm and the procedure to account for systematic uncertainties, we show a step-by-step implemented of the strategy in a one-dimensional toy model.
[1] https://link.aps.org/doi/10.1103/PhysRevD.99.015014
[2] https://link.springer.com/article/10.1140/epjc/s10052-022-10226-y
