Machine Learning for analysis of speckle dynamics: quantification and outlier detection
X-ray photon correlation spectroscopy (XPCS) provides an understanding of complex dynamics in materials that are tied to their synthesis, properties, and behaviors. Analysis of XPCS data for dynamics that are far from equilibrium is labor intense and often can impede the discovery process, especiall...
| প্রধান লেখক: | , , , , |
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| বিন্যাস: | প্রবন্ধ |
| ভাষা: | English |
| প্রকাশিত: |
American Physical Society
2022-09-01
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| মালা: | Physical Review Research |
| অনলাইন ব্যবহার করুন: | http://doi.org/10.1103/PhysRevResearch.4.033228 |
| _version_ | 1827285370440515584 |
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| author | Tatiana Konstantinova Lutz Wiegart Maksim Rakitin Anthony M. DeGennaro Andi M. Barbour |
| author_facet | Tatiana Konstantinova Lutz Wiegart Maksim Rakitin Anthony M. DeGennaro Andi M. Barbour |
| author_sort | Tatiana Konstantinova |
| collection | DOAJ |
| description | X-ray photon correlation spectroscopy (XPCS) provides an understanding of complex dynamics in materials that are tied to their synthesis, properties, and behaviors. Analysis of XPCS data for dynamics that are far from equilibrium is labor intense and often can impede the discovery process, especially in experiments with high collection rates. Moreover, binning and averaging, involved in the analysis for alleviating poor signal-to-noise ratio, leads to a loss of temporal resolution and the accumulation of systematic error for the parameters quantifying the dynamics. Here, we integrate a denoising autoencoder model into workflows for the analysis of nonequilibrium two-time intensity-intensity correlation functions. Noise reduction allows for extracting the parameters that characterize the sample's dynamics with the temporal resolution limited only by frame rates. Not only does it improve the quantitative usage of the data, but it also creates the potential for automating the analytical workflow, which is a key to high-throughput or autonomous XPCS experiments. Various approaches for the uncertainty quantification and extension of the model for anomalies' detection are discussed. |
| first_indexed | 2024-04-24T10:13:32Z |
| format | Article |
| id | doaj.art-0b3ddf76c2874831866fce2d4aa966a1 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:13:32Z |
| publishDate | 2022-09-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-0b3ddf76c2874831866fce2d4aa966a12024-04-12T17:24:40ZengAmerican Physical SocietyPhysical Review Research2643-15642022-09-014303322810.1103/PhysRevResearch.4.033228Machine Learning for analysis of speckle dynamics: quantification and outlier detectionTatiana KonstantinovaLutz WiegartMaksim RakitinAnthony M. DeGennaroAndi M. BarbourX-ray photon correlation spectroscopy (XPCS) provides an understanding of complex dynamics in materials that are tied to their synthesis, properties, and behaviors. Analysis of XPCS data for dynamics that are far from equilibrium is labor intense and often can impede the discovery process, especially in experiments with high collection rates. Moreover, binning and averaging, involved in the analysis for alleviating poor signal-to-noise ratio, leads to a loss of temporal resolution and the accumulation of systematic error for the parameters quantifying the dynamics. Here, we integrate a denoising autoencoder model into workflows for the analysis of nonequilibrium two-time intensity-intensity correlation functions. Noise reduction allows for extracting the parameters that characterize the sample's dynamics with the temporal resolution limited only by frame rates. Not only does it improve the quantitative usage of the data, but it also creates the potential for automating the analytical workflow, which is a key to high-throughput or autonomous XPCS experiments. Various approaches for the uncertainty quantification and extension of the model for anomalies' detection are discussed.http://doi.org/10.1103/PhysRevResearch.4.033228 |
| spellingShingle | Tatiana Konstantinova Lutz Wiegart Maksim Rakitin Anthony M. DeGennaro Andi M. Barbour Machine Learning for analysis of speckle dynamics: quantification and outlier detection Physical Review Research |
| title | Machine Learning for analysis of speckle dynamics: quantification and outlier detection |
| title_full | Machine Learning for analysis of speckle dynamics: quantification and outlier detection |
| title_fullStr | Machine Learning for analysis of speckle dynamics: quantification and outlier detection |
| title_full_unstemmed | Machine Learning for analysis of speckle dynamics: quantification and outlier detection |
| title_short | Machine Learning for analysis of speckle dynamics: quantification and outlier detection |
| title_sort | machine learning for analysis of speckle dynamics quantification and outlier detection |
| url | http://doi.org/10.1103/PhysRevResearch.4.033228 |
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