Papers › 'Deep' Dive into b →c Anomalies: Standardized and Future-proof Model Selection Using...
'Deep' Dive into b →c Anomalies: Standardized and Future-proof Model Selection Using Self-normalizing Neural Networks
Srimoy Bhattacharya, Soumitra Nandi, Sunando Kumar Patra, Shantanu Sahoo
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Noting the erroneous proclivity of information-theoretic approaches, like the Akaike information criterion (AIC), to select simpler models while performing model selection with a small sample size, we address the problem of new physics model selection in b→c τν_τ decays in this paper by employing a specific machine learning algorithm (self-normalizing neural networks, a.k.a. SNN) for supervised classification and regression, in a model-independent framework. While the outcomes of the classification with real data-set are compared with AIC, with the SNNs outperforming AIC_c in all aspects of model selection, the regression-outcomes are compared with the results from Bayesian analyses; the obtained parameter spaces differ considerably while keeping maximum posterior (MAP) estimates similar. A few of the two-operator scenarios with a tensor-type interaction are found to be the most probable solution for the data. We also test the effectiveness of our trained networks with the expected, more precise data in Belle-II. The trained networks and associated functionalities are supplied for the use of the community.
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