Giuseppe Fumero, Giovanni Batignani, Edoardo Cassetta, Carino Ferrante, Stefano Giagu, Tullio Scopigno
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Retrieving genuine nonlinear Raman responses in ultrafast spectroscopy via deep learning
Noise manifests ubiquitously in nonlinear spectroscopy, where multiple sources contribute to experimental signals generating interrelated unwanted components, from random point-wise fluctuations to structured baseline signals. Mitigating strategies are usually heuristic, depending on subjective biases such as the setting of parameters in data analysis algorithms and the removal order of the unwanted components. We propose a data-driven frequency-domain denoiser based on a convolutional neural network to extract authentic vibrational features from a nonlinear background in noisy spectroscopic raw data. The different spectral scales in the problem are treated in parallel by means of filters with multiple kernel sizes, which allow the receptive field of the network to adapt to the informative features in the spectra. We test our approach by retrieving asymmetric peaks in stimulated Raman spectroscopy, an ideal test-bed due to its intrinsic complex spectral features combined with a strong background signal. By using a theoretical perturbative toolbox, we efficiently train the network with simulated datasets resembling the statistical properties and lineshapes of the experimental spectra. The developed algorithm is successfully applied to experimental data to obtain noise- and background-free stimulated Raman spectra of organic molecules and prototypical heme proteins.
APL PhotonicsPhysics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
10.30
自引率
3.60%
发文量
107
审稿时长
19 weeks
期刊介绍:
APL Photonics is the new dedicated home for open access multidisciplinary research from and for the photonics community. The journal publishes fundamental and applied results that significantly advance the knowledge in photonics across physics, chemistry, biology and materials science.