Search for the decay Bc to tau nu
Contact
Clement Helsens Clement.Helsens@cern.ch
Donal Hill Donal.Hill@cern.ch
Pre-print submitted May 28, 2021
Talks
- First studies of Bc+ → τ+ ντ with EDM4hep and FCCAnalyses Donal Hill, talk at the Physics Performance meeting, Dec 14, 2020.
- Analysis of Bc / B+ to tau nu Clement Helsens, talk at the Physics Performance meeting, Apr 19, 2021.
- Updates on the Bc / B+ to tau nu analysis Donal Hill, talk at the Physics Performance meeting, May 17, 2021.
- Prospects for 𝐵+𝑐→𝜏+𝜈𝜏 at FCC-ee Clement Helsens, talk at the general FCC-ee physics meeting, May 31, 2021.
Bibliography
- The CEPC feasibility study in the leptonic channels: their Snowmass LOI and the preprint Analysis of Bc→τντ at CEPC, Taifan Zheng et al, arXiv:2007.08234
Analysis code summary
The following scripts are used to run the variois steps of the offline analysis:
user_config.py: paths and common variables.decay_mode_xs.py: definitions of the branching ratios and cross-sections for the exclusive B-hadron modes considered as background.process_sig_bkg_samples_for_xgb.py: awkward array conversion of ROOT files to pandas data frames used in stage 1 xgboost training. Output stored in pickle files.train_xgb.py: train first stage BDT.train_xgb_stage2.py: train second stage BDT.fit_MVA_dists.py: fit the two MVA distributions above 0.95 in a summed sample of exclusive background. This is used to create cubic splines for accurate cut efficiency determination in the optimisation.estimate_purity.py: run the 2D cut optimisation procedure to determine best purity for a given set of cuts, and the signal and background yield at those cuts. Persists output to dictionary in JSON.make_selected_samples_for_templates.py: create files for signal and background passing tight BDT cuts, which are then used to make templates for the fit. Files are written as pandas dataframes in pickle.template_fit.py: run toy fits to measure the signal yield for a given number of Z’s. Can run with one toy to plot, or with many for toy studies of signal yield precision and bias.analyse_toys.py: gather toy fit results and look at the distribution of fitted signal yields. Use this to determine the overall signal yield uncertainty expected.calc_BFs.py: calculate branching ratios and their precisions for different number of Z’s. Plot the trends in signal yield, branching fraction ratio, and branching fraction vs. number of Z’s.
A few scripts are also used to generate plots and tables for the paper:
plot_xgb.py: plot the BDT distributions from first stage training and the efficiency profilesplot_xgb_stage2.py: plot the BDT distributions from second stage training and the efficiency profilesexclusive_bkg_summary_table.py: summarise the exclusive background statistics and efficiencies for the paper in a table.plot_max_hem_E.py: plot charged vs neutral maximum hemisphere energies in signal and background, which are shown in paper.make_signal_yield_table.py: make a table for the paper showing signal yields and uncertainties for different number of Z’s.make_yield_BF_summary_tables.py: make summary tables of yield and branching fraction precision as a function of number of Z’s.