Abstract

A new nonlinear signal processing algorithm for recovery of narrow and high-contrast spectral features for fast chirping tunable laser spectroscopy is described. The prior linear spectral feature recovery algorithm used only linear signal processing techniques. The new algorithm uses nonlinear signal processing to improve the linear recovery algorithm by estimating and removing nonlinearities resulting from the combination of chirping faster than the conventional spectroscopic limit, large absorption depth, and high-contrast spectral features. In this context, “nonlinearities” refers to the difference between the feature-size-dependent recovered spectral features and a properly scaled version of the actual spectral features. The new nonlinear algorithm was tested on a set of spectrally narrow holes burned into the inhomogeneous line, both theoretically and experimentally. Experimental data included multiple sets of spectral holes of different depths and widths <1  MHz, which were spectrally burned into cryogenic Tm3+:YAG and readout using a chirp rate of 11.88 MHz/μs. This technique improves the qualitative and quantitative spectral estimates made by spectral-holeburning-based RF spectrum analyzers, but can be applied to any tunable laser spectroscopy applications that require fast measurement of multiple high-contrast and narrow spectral features.

© 2018 Optical Society of America

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