Probabilistic denoising for reliable signal extraction in spectroscopy
The model uses a probabilistic framework to simultaneously denoise signals and predict uncertainty from noisy data. It successfully recovers spectral features of cuprate superconductors from very sparse Poisson noise, providing scientifically meaningful error bars for subsequent analyses. This establishes uncertainty-aware deep learning as a rigorous tool for scientific data analysis.