Recent studies explored a novel storage ring light source using steady-state microbunching (SSMB). Existing investigations predominantly focused on single-particle and pure-optics phenomena. Many SSMB schemes employ laser modulators, comprising an undulator and copropagating laser beam, to manipulate electron longitudinal bunch length. Electron bunch traversing the undulator emits coherent undulator radiation near the resonant wavelength. Laser beams may form a closed path to become a laser enhancement cavity. We developed a model* analyzing laser-electron-radiation interactions in laser modulator cavities, considering mirror-induced losses, externally injected laser power compensation, and coherent undulator radiation dynamics on multiple turns. Our approach integrates beamline transfer matrices with a low-gain FEL oscillator model, enabling quick estimation of the dynamic effects. In this work we examine three SSMB scenarios, amplifier, frequency-beating, and harmonic, accounting for laser-electron-radiation interactions. Under preliminary design parameters, our analysis suggests feasibility for the three scenarios. A potential self-seeding SSMB scheme is also investigated.

The microbunching instability (MBI) has long been a persistent issue in high-brightness electron beam transport. The dogleg structure, a dispersive configuration composed of two quadrupole magnets and dipole magnets, has drawn attention in recent studies. It has been pointed out that the Landau damping effect can be enhanced to effectively suppress the microbunching instability by adjusting the strength of two quadrupole magnets preceding the dogleg structure. In this work, we derive an analytical formula for the CSR-induced microbunching gain in a dogleg lattice based on the iterative approach. The formulas have been benchmarked against semi-analytical Vlasov calculations. The analytical formulas obtained in this paper can be used to explore the influence of the strength of the quadrupole magnets in front of the dogleg lattice on the final microbunching instability, and also to verify the effectiveness of suppressing MBI in the dispersive region where the dogleg is located.

Achieving precise and real-time diagnostics of electron beam characteristics is critical for enhancing the performance of ultrafast electron diffraction (UED) and electron microscopy (UEM) techniques. Key parameters such as bunch size, emittance, energy spread, and spatial pointing jitter directly influence the quality and accuracy of experimental results. Traditional diagnostic methods often lack the ability to provide continuous, real-time, and non-intrusive monitoring, limiting their effectiveness. This work presents a machine learning (ML)-based approach that utilizes a small dataset of known beam parameters in combination with real-time diffraction image data recorded during experiments to predict electron beam characteristics for each run. This approach enables continuous optimization of beam stability without interfering with the experiment and facilitates real-time updates to UED parameters during data collection. As a result, it significantly improves the precision, reliability, and overall performance of UED and UEM experiments.