Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy
Abstract We develop several inference methods to estimate the position of dislocations from images generated using dark-field X-ray microscopy (DFXM)—achieving superresolution accuracy and principled uncertainty quantification. Using the framework of Bayesian inference, we incorporate...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer US
2022
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| Online Access: | https://hdl.handle.net/1721.1/144366 |
| _version_ | 1826204482446295040 |
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| author | Brennan, Michael C. Howard, Marylesa Marzouk, Youssef Dresselhaus-Marais, Leora E. |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Brennan, Michael C. Howard, Marylesa Marzouk, Youssef Dresselhaus-Marais, Leora E. |
| author_sort | Brennan, Michael C. |
| collection | MIT |
| description | Abstract
We develop several inference methods to estimate the position of dislocations from images generated using dark-field X-ray microscopy (DFXM)—achieving superresolution accuracy and principled uncertainty quantification. Using the framework of Bayesian inference, we incorporate models of the DFXM contrast mechanism and detector measurement noise, along with initial position estimates, into a statistical model coupling DFXM images with the dislocation position of interest. We motivate several position estimation and uncertainty quantification algorithms based on this model. We then demonstrate the accuracy of our primary estimation algorithm on synthetic realistic DFXM images of edge dislocations in single-crystal aluminum. We conclude with a discussion of our methods’ impact on future dislocation studies and possible future research avenues. |
| first_indexed | 2024-09-23T12:56:17Z |
| format | Article |
| id | mit-1721.1/144366 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:56:17Z |
| publishDate | 2022 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1443662023-08-03T04:36:44Z Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy Brennan, Michael C. Howard, Marylesa Marzouk, Youssef Dresselhaus-Marais, Leora E. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Abstract We develop several inference methods to estimate the position of dislocations from images generated using dark-field X-ray microscopy (DFXM)—achieving superresolution accuracy and principled uncertainty quantification. Using the framework of Bayesian inference, we incorporate models of the DFXM contrast mechanism and detector measurement noise, along with initial position estimates, into a statistical model coupling DFXM images with the dislocation position of interest. We motivate several position estimation and uncertainty quantification algorithms based on this model. We then demonstrate the accuracy of our primary estimation algorithm on synthetic realistic DFXM images of edge dislocations in single-crystal aluminum. We conclude with a discussion of our methods’ impact on future dislocation studies and possible future research avenues. 2022-08-19T12:59:19Z 2022-08-19T12:59:19Z 2022-08-02 2022-08-18T03:35:59Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/144366 Brennan, Michael C., Howard, Marylesa, Marzouk, Youssef and Dresselhaus-Marais, Leora E. 2022. "Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy." en https://doi.org/10.1007/s10853-022-07465-5 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature application/pdf Springer US Springer US |
| spellingShingle | Brennan, Michael C. Howard, Marylesa Marzouk, Youssef Dresselhaus-Marais, Leora E. Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy |
| title | Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy |
| title_full | Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy |
| title_fullStr | Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy |
| title_full_unstemmed | Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy |
| title_short | Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy |
| title_sort | analytical methods for superresolution dislocation identification in dark field x ray microscopy |
| url | https://hdl.handle.net/1721.1/144366 |
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