Probabilistic denoising for reliable signal extraction in spectroscopy

TL;DR

This paper presents a probabilistic denoising method that extracts signals and uncertainties from noisy spectroscopy data, enabling reliable scientific analysis and parameter estimation.

Contribution

It introduces a novel probabilistic denoising framework that provides both denoised signals and predictive uncertainties, applicable to spectroscopy and diffraction data.

Findings

Successfully recovers spectral features from Poisson noise at low counts.

Predictive uncertainties enable meaningful error bars in parameter analysis.

Extends applicability from spectroscopy to X-ray diffraction data.

Abstract

While deep learning offers powerful capabilities for scientific research, its application is often hindered by a lack of quantitative reliability. To address this, we introduce a probabilistic denoising framework that simultaneously extracts denoised signals and element-wise predictive uncertainties from noisy data. We demonstrate this approach on three-dimensional angle-resolved photoemission spectroscopy data, showing that the model reliably recovers the spectral features of a cuprate superconductor from Poisson-distributed noise with an average count of only 0.02 electrons per voxel. Crucially, we show that these predicted uncertainties can be propagated into subsequent superconducting gap analyses, enabling quantitative parameter extraction with scientifically meaningful error bars. Furthermore, we validate the broad applicability of our approach by successfully extending it to two-dimensional X-ray diffraction data. Ultimately, this approach establishes uncertainty-aware deep learning not merely as a visualization tool, but as a rigorous framework for scientific data analysis.