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High Luminosity LHC data collected by CMS experiment -- an excellent ground for the search of Rare Radiative $B_s^0$ meson decays: A Review

Alibordi Muhammad

Abstract

The High-Luminosity Large Hadron Collider (HL-LHC) provides unprecedented opportunities to study rare radiative decays in the flavor sector, particularly with the CMS experiment. This review focuses on the search for the challenging decay $B_s^0 \to μ^+ μ^- γ$, a flavor-changing neutral current process forbidden at tree level in the Standard Model. These decays probe Wilson coefficients C7, C9, and C10, which connect short-distance electroweak dynamics with long-distance hadronic effects. The main experimental difficulty is reconstructing low-pT photons (2-20 GeV) under extreme pile-up conditions (<mu> ~ 60). We argue that collider data naturally live on curved statistical manifolds shaped by conservation laws, detector geometry, and kinematic constraints. The Fisher-Rao information metric captures this structure, suggesting that curvature-aware analysis methods may enhance sensitivity beyond traditional approaches. We outline a framework that links collider data hierarchies, information geometry, and quantum machine learning as a pathway for future searches for rare radiative B_s^0 decays at the HL-LHC.

High Luminosity LHC data collected by CMS experiment -- an excellent ground for the search of Rare Radiative $B_s^0$ meson decays: A Review

Abstract

The High-Luminosity Large Hadron Collider (HL-LHC) provides unprecedented opportunities to study rare radiative decays in the flavor sector, particularly with the CMS experiment. This review focuses on the search for the challenging decay , a flavor-changing neutral current process forbidden at tree level in the Standard Model. These decays probe Wilson coefficients C7, C9, and C10, which connect short-distance electroweak dynamics with long-distance hadronic effects. The main experimental difficulty is reconstructing low-pT photons (2-20 GeV) under extreme pile-up conditions (<mu> ~ 60). We argue that collider data naturally live on curved statistical manifolds shaped by conservation laws, detector geometry, and kinematic constraints. The Fisher-Rao information metric captures this structure, suggesting that curvature-aware analysis methods may enhance sensitivity beyond traditional approaches. We outline a framework that links collider data hierarchies, information geometry, and quantum machine learning as a pathway for future searches for rare radiative B_s^0 decays at the HL-LHC.

Paper Structure

This paper contains 31 sections, 12 equations, 1 figure, 2 tables.

Table of Contents

  1. Prologue
  2. Theoretical Background
  3. Effective Hamiltonian
  4. Wilson Coefficients and Hadronic Dynamics
  5. Existing numeric
  6. Compact Muon Solenoid
  7. Status
  8. Feasibility Check For Radiative Decays
  9. Hierarchy of Collider Data : Intrinsic Curvature Awareness
  10. From Flat Coordinates to Curved Manifolds
  11. Hierarchies and Curvature in Collider Observables
  12. Analysis Implications: Curvature-Aware Algorithms
  13. Binding Curvature Awareness with Quantum Machine Learning
  14. Information-Geometric Foundations for Rare Decay Analysis
  15. Geometric Decomposition as a Hypothesis for Feature Encoding
  16. ...and 16 more sections

Figures (1)

  • Figure 1: Penguin diagrams responsible for the virtual photon emission and the Wilson coeffcient $C_{7\gamma}$ appear in the Eq. \ref{['ref:eh']} for the $\mathrm{B_s^0\to s\ell^+\ell^-}$ decay rad3.