This series of notebooks contain all the code to reproduce the data in Chapter 7 of my PhD thesis, which is adapted from 2109.07405
- 01: Fits to Scenarios I and II and their errors. Pulls in Scenario II. Leptoquark couplings.
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02: Plots for
$\alpha^l$ vs$\beta^\ell$ and$\alpha^q$ vs$\beta^q$ in Scenario II. - 03: Plots for $R_{K^{()}}$ and $R_{D^{()}}$ observables in Scenario I and II.
- 04: Preparation of the datasets to train the Machine Learning algorithm.
- 05: Training of the ML algorithm. Regression, histogram and SHAP values.
- 06: Correlation matrices for Wilson Coefficients and for observables in the dataset generated by the ML algorithm.
- Hessian ellipse data for:
- Likelihood plots for:
- $R_{K^{()}}$ and $R_{D^{()}}$ observables:
- Confidence plots for observables:
- Plot of the pulls for every observable in Scenario II: (pdf) | (pgf)
- List of the pulls for every observable in Scenario II: (pdf) | (tex)
- Evolution of the likelihood changing one parameter:
- Regression plot for the training of the Machine Learning algorithm
- Histogram of the ML-generated points: (pdf) | (pgf)
- SHAP summary plot
- SHAP values for the fit parameters:
- Correlation matrices:
- Plot for $R_D$ and $\mathrm{B\to K\nu\nu}$