Retrieval of Raman signals from broadband CARS intensity measurements using an autoencoder trained with a prior excitation function

Published

July 31, 2023

Author(s)

ryan muddiman, Kevin O' Dwyer, Charles Camp, Bryan Hennelly

Abstract

Broadband coherent anti-Stokes Raman scattering (BCARS) is capable of producing high-quality Raman spectra spanning broad bandwidths, 400–4000 cm−1, with millisecond acquisition times. Raw BCARS spectra, however, are a coherent combination of vibrationally resonant (Raman) and non-resonant (electronic) components that may challenge or degrade chemical analyses. Recently, we demonstrated a deep convolutional autoencoder network, trained on pairs of simulated BCARS-Raman datasets, which could retrieve the Raman signal with high quality under ideal conditions. In this work, we present a new computational system that incorporates experimental measurements of the laser system spectral and temporal properties, combined with simulated susceptibilities. Thus, the neural network learns the mapping between the susceptibility and the measured response for a specific BCARS system. The network is tested on simulated and measured experimental results taken with our BCARS system.

Citation

Analyst

Volume

15

Pub Type

Journals

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Keywords

Raman, BCARS, Machine Learning, Deep Learning, Phase Retrieval

Citation

Muddiman, R. , O' Dwyer, K. , Camp, C. and Hennelly, B. (2023), Retrieval of Raman signals from broadband CARS intensity measurements using an autoencoder trained with a prior excitation function, Analyst, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936436 (Accessed October 29, 2025)