Experimental Evidence-Based Sub-Rayleigh Source Discrimination

TL;DR

This paper introduces a Bayesian inference framework for optical source discrimination, demonstrating that SPADE outperforms direct imaging in superresolution tasks with a robust, data-driven approach.

Contribution

It presents a novel Bayesian evidence-based method for source discrimination that leverages SPADE, improving superresolution analysis without ad hoc statistical assumptions.

Findings

SPADE outperforms direct imaging in resolving incoherent sources.

The Bayesian framework is robust and data-driven, avoiding ad hoc assumptions.

Method generalizes to multiple sources and quantum-enhanced superresolution.

Abstract

We propose a Bayesian evidence-based inference framework based on relative belief ratios and apply it to discriminating between one and two incoherent optical point sources using spatial-mode demultiplexing (SPADE). Unlike the Helstrom measurement, SPADE require no collective detection and its optimal for asymptotically large samples. Our method avoids ad hoc statistical constructs and relies solely on the information contained in the data, with all assumptions entering only through the likelihood model and prior beliefs. Using experimental evidence, we demonstrate the superior resolving performance of SPADE over direct imaging from a new and extensible perspective; one that naturally generalizes to multiple sources and offers a practical robust approach to analyzing quantum-enhanced superresolution.