Statistical Insight into the Correlation of Geometry and Spectral Emission in Network Lasers

TL;DR

This paper uses statistical analysis based on laser theory to explore how the geometry of network lasers influences their emission spectra, aiming to improve design and control.

Contribution

It introduces a statistical framework linking network geometry features to emission properties, advancing predictive understanding of network laser behavior.

Findings

Edge crowding affects modal intensity uniformity.

Statistical analysis reveals correlations between network geometry and emission spectra.

Framework aids in designing network-based photonic devices.

Abstract

Optically active networks show feature-rich emission that depends on the fine details of their geometry, and find diverse applications in random lasers, sensing devices and photonics processors. In these and other systems, a thorough and predictive characterization of how the network geometry correlates with the resulting emission spectrum would be highly important, however such outright description is still lacking. In this work, we take a step toward filling this gap, by using the well-known Steady-State ab Initio Laser Theory equations to carry out an extensive set of statistical analyses and establish connections between the random network geometry and their ultimate emission spectrum. Our results show that edge crowding (abundance of short edges in the network) is key to tune the uniformity of the modal intensity distribution of the emission spectrum. A statistical framework for the comprehensive understanding of the network statistical properties is highly significant to establish precise design rules for network-based photonic devices and intelligent systems.