Removing Non-Resonant Background from CARS spectra via Deep Learning

Broadband Coherent Anti-Stokes Raman Scattering (B-CARS) is a powerful label-free nonlinear spectroscopy technique allowing one to measure the full vibrational spectrum of molecules and solids [1] - [2] . B-CARS spectra, however, suffer from the presence of a spurious signal, called non-resonant background (NRB), which interferes with the resonant one, distorting the line shapes and degrading the chemical information. While several numerical techniques [3] are available to remove this unwanted contribution and extract the resonant vibrational signal of interest, they all require the user’s intervention and sensitively depend on the spectral shape of the NRB, which needs to be measured independently.