Cross-exposure transferability and failure boundaries of self-supervised DNN denoising for AR-HAXPES depth profiling

Laboratory-source angle-resolved hard X-ray photoelectron spectroscopy (AR-HAXPES) is photon-starved atthespatialresolution required formapping,wheretheframe averaging needed to recover a clean depth profile at every position drives total acquisition time into a prohibitive regime. Self-supervised neural denoising offers an alternative to acquisition-time scaling, and a recent companion study 1 has systematically benchmarked its performance against alternative strategies; however, its operational envelope across exposures, its cross-exposure transferability, and its failure modes in the photon-starved regime remain uncharacterised. Here we evaluate self-supervised denoising atthe smallestpossibletargetwindow across three exposures spanning a 25-fold range of per-frame photon flux on a model C / Al 2 O 3 / TiO 2 / Si multilayer. We identify an empirical directional rule for cross-exposure denoiser deployment — train S/N ≥ inference S/N —, characterise the failure boundary in the photon-starved regime where upstream denoiser collapse propagates into constraint-saturated inversion solutions, and locate the asymptotic reach of alltested denoising strategies (frame averaging, Self-DNN, Cross-DNN) relative to a methodological floor set by the reproducibility of the reference. Within the success region of this rule, cross-exposure transfer enables up to a 48-fold equivalent acquisition-time reduction on the depth-RMSE axis (14-fold on the Wasserstein-1 interface- localization axis) at the most photon-starved 4.8 s/frame condition. These results frame self- supervised AR-HAXPES denoising as a characterisable engineering operation with quantifiable failure boundaries and recovery routes, rather than as an empirical post-processing recipe.