Inverse Modeling for Artifact Removal in Photonic Data: A Computational Physics and Transfer Learning-Based Approach

Area of Science:

• Spectroscopy
• Computational Physics
• Deep Learning

Background:

• Etaloning artifacts cause significant distortions in spectroscopic data.
• These distortions complicate data analysis and interpretation.
• Accurate spectral data is crucial for scientific discovery.

Purpose of the Study:

• To develop an inverse modeling framework to correct etaloning artifacts.
• To improve the accuracy and interpretability of spectroscopic data.
• To leverage deep learning and computational physics for spectral data correction.

Main Methods:

• An inverse modeling framework integrating computational physics and deep learning was developed.
• A two-phase transfer learning strategy was employed: pretraining on simulated spectra and fine-tuning on experimental data.
• Over 30,000 simulated spectra were generated using the transfer matrix method for pretraining.

Main Results:

• The transfer learning approach reduced etaloning-induced distortions by up to 70%.
• The model demonstrated enhanced generalization across different sensor designs.
• Cross-validation across multiple devices confirmed significant improvements in spectral accuracy and interpretability.

Conclusions:

• The developed framework effectively corrects etaloning artifacts in spectroscopic data.
• This approach sets a new standard for achieving reliable spectral data.
• Combining correction procedures with physics simulations enhances spectral data quality.