High-order spectral deconvolution of complex X-ray distributions using machine learning
High-repetition-rate (10 Hz) laser-plasma experiments require rapid and reliable reconstruction of X-ray spectra from scintillator-based linear absorption spectrometer measurements. The inverse problem is intrinsically ill-posed and complicated by practical detector non-idealities such as digitisation, saturation, and noise. In this work, we present a machine learning-based reconstruction framework that enables rapid spectral inference under realistic detector conditions, without a priori assumptions on spectral shape during inference. The approach separates spectral shape from flux, allowing an ensemble of neural networks to infer spectral shape from normalised detector measurements, while flux is recovered in a subsequent step. Training data comprises a collection of physically interpretable, multi-component spectra formed from mixtures of Boltzmann and skewed Gaussian components, enabling generalisation beyond simple parametric forms. Models are trained on synthetic data incorporating detector non-idealities. Comparison is performed with existing multi-parameter deconvolution method, demonstrating comparable performance in the presence of detector non-idealities, with approximately two orders of magnitude faster inference. A comprehensive evaluation is performed examining the role of training distribution, measurement-space data augmentation, and detector response. Results demonstrate robust reconstruction across diverse spectral morphologies and flux levels, with millisecond-level inference times suitable for high-repetition-rate experiments.