Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2412.15830 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912946569871360 |
|---|---|
| author | Alda, Jorge Mir, Alejandro Penaranda, Siannah |
| author_facet | Alda, Jorge Mir, Alejandro Penaranda, Siannah |
| contents | We present an analysis on flavour anomalies in semileptonic rare $B$-meson decays using an effective field theory approach and assuming that new physics affects only one generation in the interaction basis and non-universal mixing effects are generated by the rotation to the mass basis. A global fit to experimental data is performed, focusing on LFU ratios $R_{D^{(*)}}$ and $R_{J/ψ}$ and branching ratios that exhibit tensions with Standard Model predictions on $B \rightarrow K^{(*)} ν\barν$ decays. In our analysis, we use a Machine Learning Montecarlo algorithm, a framework that emulates the highly non-Gaussian structure of the likelihood landscape with minimal training cost. This method enables the generation of high-resolution confidence regions and detailed correlation analyses. By comparing three different scenarios, we show that the one that introduces only mixing between the second and third quark generations and no mixing in the lepton sector, as well as independent coefficients for the singlet and triplet four fermion effective operators, provides the best fit to the experimental data. A comparison with previous results is performed. We highlight the key strengths of the Machine Learning framework in our analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15830 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Flavour Anomalies: A comparative analysis using a machine learning algorithm Alda, Jorge Mir, Alejandro Penaranda, Siannah High Energy Physics - Phenomenology We present an analysis on flavour anomalies in semileptonic rare $B$-meson decays using an effective field theory approach and assuming that new physics affects only one generation in the interaction basis and non-universal mixing effects are generated by the rotation to the mass basis. A global fit to experimental data is performed, focusing on LFU ratios $R_{D^{(*)}}$ and $R_{J/ψ}$ and branching ratios that exhibit tensions with Standard Model predictions on $B \rightarrow K^{(*)} ν\barν$ decays. In our analysis, we use a Machine Learning Montecarlo algorithm, a framework that emulates the highly non-Gaussian structure of the likelihood landscape with minimal training cost. This method enables the generation of high-resolution confidence regions and detailed correlation analyses. By comparing three different scenarios, we show that the one that introduces only mixing between the second and third quark generations and no mixing in the lepton sector, as well as independent coefficients for the singlet and triplet four fermion effective operators, provides the best fit to the experimental data. A comparison with previous results is performed. We highlight the key strengths of the Machine Learning framework in our analysis. |
| title | Flavour Anomalies: A comparative analysis using a machine learning algorithm |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2412.15830 |