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Deep learning-based statistical noise reduction for multidimensional spectral data. | LitMetric

AI Article Synopsis

  • Spectroscopic experiments, especially in multi-dimensional phase space like ARPES, often require long acquisition times, posing challenges for data collection.
  • A new deep learning-based denoising method has been developed to enhance the quality of spectral data while significantly reducing the time needed for data acquisition.
  • This neural network efficiently removes noise without losing key information, enabling detailed analysis with up to 100 times less data collection time and has potential applications for various multidimensional spectral data prone to noise.

Article Abstract

In spectroscopic experiments, data acquisition in multi-dimensional phase space may require long acquisition time, owing to the large phase space volume to be covered. In such a case, the limited time available for data acquisition can be a serious constraint for experiments in which multidimensional spectral data are acquired. Here, taking angle-resolved photoemission spectroscopy (ARPES) as an example, we demonstrate a denoising method that utilizes deep learning as an intelligent way to overcome the constraint. With readily available ARPES data and random generation of training datasets, we successfully trained the denoising neural network without overfitting. The denoising neural network can remove the noise in the data while preserving its intrinsic information. We show that the denoising neural network allows us to perform a similar level of second-derivative and line shape analysis on data taken with two orders of magnitude less acquisition time. The importance of our method lies in its applicability to any multidimensional spectral data that are susceptible to statistical noise.

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Source
http://dx.doi.org/10.1063/5.0054920DOI Listing

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