Adaptable phase retrieval for coherent transition radiation spectroscopy based on differentiable physics information
For researchers using CTR spectroscopy to characterize electron bunches, this provides a flexible, physics-informed framework that can incorporate multi-diagnostic constraints and uncertainty quantification, though it is an incremental improvement over existing methods.
The authors propose a gradient-based phase retrieval method (GD-Phase) for coherent transition radiation spectroscopy that enforces measured spectral amplitude as a hard constraint while optimizing Fourier phase under physical priors. Benchmarking on synthetic spectra shows it matches the fidelity of traditional Gerchberg-Saxton algorithms while enabling seamless inclusion of arbitrary differentiable experimental effects.
Coherent transition radiation (CTR) spectroscopy is a critical diagnostic for characterizing the longitudinal structure of relativistic electron bunches in laser-plasma and conventional accelerators. In practice, recovering the bunch profile from a measured CTR spectrum is an ill-posed phase-retrieval problem. Traditionally, this is addressed using Gerchberg-Saxton (GS)-type iterative algorithms. However, these implementations often rely on explicit inverse propagators, making them difficult to adapt to sophisticated experimental forward models. In this work, we introduce a flexible gradient-based framework for CTR phase retrieval. By leveraging a differentiable forward model, we propose a phase-only gradient descent (GD-Phase) approach that enforces the measured spectral amplitude as a hard constraint while optimizing the Fourier phase under physical real-space priors. Using synthetic CTR spectra spanning multi-peaked and strongly modulated profiles, we benchmark GD-Phase against traditional GS and a real-space amplitude-parametrized gradient descent (GD-Amp) algorithm. Unlike traditional methods, this formulation allows for the seamless inclusion of arbitrary differentiable experimental effects into the reconstruction loop. We demonstrate that this physics-informed approach not only reproduces the fidelity of GS methods but also establishes a robust baseline for incorporating multi-diagnostic constraints and uncertainty quantification. This enables the systematic extension to higher-dimensional, multimodal, and uncertainty-aware diagnostics, facilitating fast and scalable phase retrieval in realistic experimental settings.