Optimized reconstructions of compressively sampled two-dimensional infrared spectra

Area of Science:

• Spectroscopy
• Computational Chemistry
• Data Science

Background:

• Compressive sampling offers significant acceleration for data acquisition in 2D IR spectroscopy.
• The Generic Iteratively Reweighted Annihilating Filter (GIRAF) algorithm was previously developed for 2D IR compressive sampling reconstruction.
• Comparison with the Total Variation (TV) algorithm is essential for evaluating GIRAF's performance.

Purpose of the Study:

• To thoroughly assess the GIRAF reconstruction algorithm for 2D IR compressive sampling.
• To compare GIRAF's performance against the established Total Variation (TV) algorithm.
• To evaluate GIRAF's impact on spectral line shapes and its ability to reconstruct complex multi-oscillator systems.

Main Methods:

• Application of the GIRAF algorithm to 2D IR spectroscopic data acquired using compressive sampling.
• Comparative analysis of spectral line shape distortions caused by GIRAF versus TV algorithms.
• Testing GIRAF's reconstruction capabilities on spectra with single and multiple coupled oscillators, exemplified by rhodium dicarbonyl.

Main Results:

• GIRAF demonstrates distinct advantages over the TV algorithm in 2D IR compressive sampling.
• While line shape impacts are comparable, the nature of these effects differs between GIRAF and TV.
• GIRAF effectively reconstructs spectra with coupled oscillators.
• A significant denoising effect was observed, increasing the signal-to-noise ratio (SNR) by up to 4×.

Conclusions:

• The GIRAF algorithm significantly enhances data collection acceleration in 2D IR spectroscopy through compressive sampling.
• GIRAF offers improved SNR and reconstruction fidelity, surpassing previous methods.
• This advancement enables more challenging experimental measurements and faster data acquisition in 2D IR spectroscopy.