Extracting tiny signals from noise is a generic challenge in experimental science. In catalysis, this challenge manifests itself as the need to quantify chemical reactions on nanoscopic surface areas, such as single nanoparticles or even single atoms. Here, we address this challenge by combining the ability of nanofluidic reactors to focus reaction product from tiny catalyst surfaces towards online mass spectrometric analysis with the unrivalled ability of a constrained denoising autoencoder to discern tiny signals from noise. Using CO oxidation on Pd as model reaction, we demonstrate that the catalyst surface area required for online mass spectrometry can be reduced by ≈ 3 orders of magnitude compared to state of the art, down to a single nanoparticle with 0.0072 ± 0.00086 μm2 surface area. These results constitute a new paradigm for online reaction analysis in single particle catalysis and advocate deep learning to improve resolution in mass spectrometry in general.