Multivariate Discrimination in Quantum Target Detection

Multivariate Discrimination in Quantum Target Detection

Update July 2020: now published as Appl. Phys. Lett. 117, 044001 (2020);

[Submitted on 1 May 2020]

Peter Svihra, Yingwen Zhang, Paul Hockett, Steven Ferrante, Benjamin Sussman, Duncan England, Andrei Nomerotski

We describe a simple multivariate technique of likelihood ratios for improved discrimination of signal and background in multi-dimensional quantum target detection. The technique combines two independent variables, time difference and summed energy, of a photon pair from the spontaneous parametric down-conversion source into an optimal discriminant. The discriminant performance was studied in experimental data and in Monte-Carlo modelling with clear improvement shown compared to previous techniques. As novel detectors become available, we expect this type of multivariate analysis to become increasingly important in multi-dimensional quantum optics.


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