Coincidence detection for photon triplet sources

TL;DR

This paper develops a probabilistic model for photon triplet detection, addressing the challenge of characterizing sources with noise and limited detection rates, using Bayesian inference to determine the minimum detectable coincidence rate.

Contribution

It introduces a Bayesian probability framework to model coincidence detection in photon triplet sources, enabling better source characterization amidst noise and instrument limitations.

Findings

Triplet generation rate of 1-100 Hz needed for characterization

Source characterization feasible over 1-72 hours with superconducting detectors

Bayesian model effectively identifies detection limits

Abstract

Photon triplet generation based on third-order spontaneous parametric down-conversion remains as an experimental challenge. The challenge stems from the trade-offs between source brightness and instrument noise. This work presents a probability theory of coincidence detection to address the detection limit in source characterization. We use Bayes' theorem to model instruments as a noisy communication channel and apply statistical inference to identify the minimum detectable coincidence rate. A triplet generation rate of 1-100 Hz is required for source characterization performed over 1-72 hours using superconducting nanowire single-photon detectors.