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$Interference and Diffraction$ 02:18

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Related Experiment Video

Updated: May 9, 2026

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

Published on: April 4, 2017

Cramer-Rao bounds for intensity interferometry measurements.

Richard Holmes¹, Brandoch Calef, D Gerwe

¹Boeing Laser Technical Services, 535 Lipoa Parkway, Suite 200, Kihei, Hawaii 96753, USA. richard.b.holmes@boeing.com

|July 23, 2013

Summary

This summary is machine-generated.

Innovations in signal processing can improve signal-to-noise ratio (SNR) in intensity interferometry. Using object shape knowledge offers potential gains, but constraints can introduce biases that impact overall error.

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Last Updated: May 9, 2026

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Area of Science:

Astronomy and Astrophysics
Signal Processing
Statistical Inference

Background:

Signal-to-noise ratio (SNR) in intensity interferometry is crucial for astronomical observations.
Recent advancements in signal processing offer potential improvements to SNR.

Purpose of the Study:

To investigate the potential SNR gains from using positivity and object shape knowledge in intensity interferometry.
To analyze these gains using Cramer-Rao lower bounds (CRLB).

Main Methods:

Application of Cramer-Rao lower bounds (CRLB) to evaluate estimators.
Analysis of positivity-constrained maximum likelihood (ML) estimators.
Investigation of the impact of prior knowledge on estimator variance.

Main Results:

Positivity constraints can reduce estimator variance by up to 75% compared to unconstrained estimators.
Positivity-constrained ML estimators offer a best-case variance reduction of 34.1%.
Prior knowledge gains are dependent on information quality; biases can offset variance reduction.

Conclusions:

Positivity and object shape knowledge can enhance SNR in intensity interferometry.
CRLB provides a framework to quantify gains and understand limitations, including induced biases.
Careful consideration of constraints and prior information is necessary to optimize total error reduction.