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Noting the erroneous proclivity of information-theoretic approaches, like theAkaike information criterion (AIC), to select simpler models while performingmodel selection with a small sample size, we address the problem of new physicsmodel selection in $b\to c \tau \nu_{\tau}$ decays in this paper by employing aspecific machine learning algorithm (self-normalizing neural networks, a.k.a.SNN) for supervised classification and regression, in a model-independentframework. While the outcomes of the classification with real data-set arecompared with AIC, with the SNNs outperforming AIC$_c$ in all aspects of modelselection, the regression-outcomes are compared with the results from Bayesiananalyses; the obtained parameter spaces differ considerably while keepingmaximum posterior (MAP) estimates similar. A few of the two-operator scenarioswith a tensor-type interaction are found to be the most probable solution forthe data. We also test the effectiveness of our trained networks with theexpected, more precise data in Belle-II. The trained networks and associatedfunctionalities are supplied for the use of the community.

'Deep' Dive into $b \to c$ Anomalies: Standardized and Future-proof Model Selection Using Self-normalizing Neural Networks