The generation of single isolated attosecond pulses in the extreme ultraviolet (XUV) together with fully synchronized few-cycle infrared (IR) laser pulses allowed to trace electronic processes on the attosecond timescales. A pump/probe technique (attsecond streaking) was used to investigate electron dynamics on surfaces and layered systems with unprecedented resolution. We were able to measure the absolute emission time of electrons upon the photoelectric effect, delays in photoemission of electrons of different species, energy-dependant delays, the influence of the band-structure or wavepacket properties on the emission time in various materials and layered systems.
{"title":"Attosecond chronoscopy on solids","authors":"R. Kienberger","doi":"10.1117/12.2606067","DOIUrl":"https://doi.org/10.1117/12.2606067","url":null,"abstract":"The generation of single isolated attosecond pulses in the extreme ultraviolet (XUV) together with fully synchronized few-cycle infrared (IR) laser pulses allowed to trace electronic processes on the attosecond timescales. A pump/probe technique (attsecond streaking) was used to investigate electron dynamics on surfaces and layered systems with unprecedented resolution. We were able to measure the absolute emission time of electrons upon the photoelectric effect, delays in photoemission of electrons of different species, energy-dependant delays, the influence of the band-structure or wavepacket properties on the emission time in various materials and layered systems.","PeriodicalId":289295,"journal":{"name":"X-Ray Nanoimaging: Instruments and Methods V","volume":"68 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114124564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic X-ray detectors at the National Ignition Facility play a crucial role on High-Energy-Density (HED) experiments. They record data in the form of X-ray spectra, hot spot emission profiles, radiographic images, et cetera. The fast (pico- to nanoseconds) time scales and harsh environments of the HED experiments at the NIF impose tight constraints on the performance of these instruments, both in terms of temporal and spatial resolution, background rejection as well as their survivability. We are constantly striving to improve the quality of the data collected by identifying, implementing, and integrating cutting-edge technology, such as the hybridized CMOS cameras from SNL [1]. Here we provide a summary of the how we utilize these multi-frame nanosecond cameras in our X-ray detectors for HED experiments.
{"title":"Dynamic x-ray detectors for high-energy-density experiments in high density","authors":"S. Nagel","doi":"10.1117/12.2606068","DOIUrl":"https://doi.org/10.1117/12.2606068","url":null,"abstract":"Dynamic X-ray detectors at the National Ignition Facility play a crucial role on High-Energy-Density (HED) experiments. They record data in the form of X-ray spectra, hot spot emission profiles, radiographic images, et cetera. The fast (pico- to nanoseconds) time scales and harsh environments of the HED experiments at the NIF impose tight constraints on the performance of these instruments, both in terms of temporal and spatial resolution, background rejection as well as their survivability.\u0000\u0000We are constantly striving to improve the quality of the data collected by identifying, implementing, and integrating cutting-edge technology, such as the hybridized CMOS cameras from SNL [1]. Here we provide a summary of the how we utilize these multi-frame nanosecond cameras in our X-ray detectors for HED experiments.","PeriodicalId":289295,"journal":{"name":"X-Ray Nanoimaging: Instruments and Methods V","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122824185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Rau, D. Batey, A. Bodey, S. Cipiccia, Peng Li, S. Marathe, M. Storm, G. Das, R. Ziesche
We report about our current capabilities and future plans in multi-scale imaging with high recording speed. For micro-tomographic imaging an automated system is used measuring up to 300 samples per day. For sub-micron and nano measurements the so-called polychromatic ‘pink beam’ is employed. The larger energy bandwidth compared to monochromatic beam permits recording times similar to microtomography. For highest resolution namely ptychography the acquisition time for tomographic scans is currently in the order of hours and below an hour in the near future. The current multi-scale science and the scientific perspective with the Diamond beamline I13L upgrade will be presented.
{"title":"Fast multi-scale x-ray imaging","authors":"C. Rau, D. Batey, A. Bodey, S. Cipiccia, Peng Li, S. Marathe, M. Storm, G. Das, R. Ziesche","doi":"10.1117/12.2596477","DOIUrl":"https://doi.org/10.1117/12.2596477","url":null,"abstract":"We report about our current capabilities and future plans in multi-scale imaging with high recording speed. For micro-tomographic imaging an automated system is used measuring up to 300 samples per day. For sub-micron and nano measurements the so-called polychromatic ‘pink beam’ is employed. The larger energy bandwidth compared to monochromatic beam permits recording times similar to microtomography. For highest resolution namely ptychography the acquisition time for tomographic scans is currently in the order of hours and below an hour in the near future. The current multi-scale science and the scientific perspective with the Diamond beamline I13L upgrade will be presented.","PeriodicalId":289295,"journal":{"name":"X-Ray Nanoimaging: Instruments and Methods V","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114775920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Ohldag, T. Feggeler, D. Shapiro, Y. Kumar, G. Portman, E. Norum, A. Butko
{"title":"Development of a user-friendly time resolved scanning transmission x-ray microscope at the Advanced Light Source","authors":"H. Ohldag, T. Feggeler, D. Shapiro, Y. Kumar, G. Portman, E. Norum, A. Butko","doi":"10.1117/12.2595552","DOIUrl":"https://doi.org/10.1117/12.2595552","url":null,"abstract":"","PeriodicalId":289295,"journal":{"name":"X-Ray Nanoimaging: Instruments and Methods V","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115707211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Pattammattel, R. Tappero, Y. Chu, M. Ge, D. Gavrilov, Xiaojing Huang, Hanfei Yan
Spectromicroscopy techniques allow the study of local chemical states along with morphology information. At the hard X-ray nanoprobe (HXN) beamline at NSLS-II, we developed nanoscale chemical imaging with high chemical state sensitivity and micron-scale penetration depth. In addition to the chemical images, XRF and phase-contrast images collected simultaneously offer multi-modal, correlative image analysis. We also developed a highly interactive, python-based graphical user interface (NSLS-II MIDAS) that allows multi-modal analysis of nano-XANES and XRF images. Advanced supervised and unsupervised learning algorithms enable users to explore the traditional XANES analysis along with standard machine-learning tools
{"title":"Hard x-ray nano-XANES and implementation deep learning tools for multi-modal chemical imaging","authors":"A. Pattammattel, R. Tappero, Y. Chu, M. Ge, D. Gavrilov, Xiaojing Huang, Hanfei Yan","doi":"10.1117/12.2599526","DOIUrl":"https://doi.org/10.1117/12.2599526","url":null,"abstract":"Spectromicroscopy techniques allow the study of local chemical states along with morphology information. At the hard X-ray nanoprobe (HXN) beamline at NSLS-II, we developed nanoscale chemical imaging with high chemical state sensitivity and micron-scale penetration depth. In addition to the chemical images, XRF and phase-contrast images collected simultaneously offer multi-modal, correlative image analysis. We also developed a highly interactive, python-based graphical user interface (NSLS-II MIDAS) that allows multi-modal analysis of nano-XANES and XRF images. Advanced supervised and unsupervised learning algorithms enable users to explore the traditional XANES analysis along with standard machine-learning tools","PeriodicalId":289295,"journal":{"name":"X-Ray Nanoimaging: Instruments and Methods V","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122909331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For a long time in computed tomography (CT), noise and missing wedge have been two significant issues prohibiting researchers from obtaining reliable insights into material's intrinsic structures. Though much work has been done to denoise sinograms or recover the missing information, from traditional algorithms to emerging machine learning (ML) methods, most of them focus on perceptual performance, i.e., better visual consistency of data. This metric is adequate for computer vision applications, yet is insufficient for the scientific community where data fidelity is more critical, e.g., in the medical fields. In this work, we are trying to combine ML methods and the inherent properties of sinograms, aiming to achieve both state-of-the-art perceptual performance and high fidelity of the filled data. Distinguished from existing ML architectures, we propose a two-fold model implemented through neural networks: one using generative adversarial networks (GAN) and autoencoder to denoise/inpaint the missing-wedge sinogram, and the other one using convolutional neural networks (CNN) model to enforce the denoised/inpainted sinogram to obey their inherent properties. These two steps may need iterate to achieve consistent results. The results on both simulated and experimental data have demonstrated that our methods have achieved state-of-the-art perceptual performance and high fidelity. Our work further indicates that it is possible to incorporate physics into scientific ML models to make ML models more robust and accurate, significantly benefiting the scientific research aided by ML methods. This work is supported by the LDRD program at the FXI facility at NSLS-II, Brookhaven National Laboratory (BNL).
{"title":"X-ray tomography reconstruction with machine learning: a study focusing on accuracy and fidelity","authors":"Jiayong Zhang, M. Ge, Thomas Flynn, S. Mittal","doi":"10.1117/12.2594344","DOIUrl":"https://doi.org/10.1117/12.2594344","url":null,"abstract":"For a long time in computed tomography (CT), noise and missing wedge have been two significant issues prohibiting researchers from obtaining reliable insights into material's intrinsic structures. Though much work has been done to denoise sinograms or recover the missing information, from traditional algorithms to emerging machine learning (ML) methods, most of them focus on perceptual performance, i.e., better visual consistency of data. This metric is adequate for computer vision applications, yet is insufficient for the scientific community where data fidelity is more critical, e.g., in the medical fields. In this work, we are trying to combine ML methods and the inherent properties of sinograms, aiming to achieve both state-of-the-art perceptual performance and high fidelity of the filled data. Distinguished from existing ML architectures, we propose a two-fold model implemented through neural networks: one using generative adversarial networks (GAN) and autoencoder to denoise/inpaint the missing-wedge sinogram, and the other one using convolutional neural networks (CNN) model to enforce the denoised/inpainted sinogram to obey their inherent properties. These two steps may need iterate to achieve consistent results. The results on both simulated and experimental data have demonstrated that our methods have achieved state-of-the-art perceptual performance and high fidelity. Our work further indicates that it is possible to incorporate physics into scientific ML models to make ML models more robust and accurate, significantly benefiting the scientific research aided by ML methods. This work is supported by the LDRD program at the FXI facility at NSLS-II, Brookhaven National Laboratory (BNL).","PeriodicalId":289295,"journal":{"name":"X-Ray Nanoimaging: Instruments and Methods V","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122671061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Viktor V. Nikitin, V. Andrade, D. Gursoy, F. Carlo
As X-ray imaging is pushed further into the nanoscale, the sample deformations due to the increased radiation levels or mechanical instabilities of the microscopes become more apparent, leading to challenges in realizing high-resolution microscopy under these conditions. Here we propose a distributed optimization solver for imaging of samples at the nanoscale. Our approach solves the tomography and ptychography problems jointly with projection data alignment, nonrigid sample deformation correction, and regularization. Applicability of the method is demonstrated on experimental data sets from the Transmission X-ray Microscope, and the hard X-ray nanoprobe.
{"title":"Reconstruction with nonrigid alignment in tomography and 3D ptychography","authors":"Viktor V. Nikitin, V. Andrade, D. Gursoy, F. Carlo","doi":"10.1117/12.2594889","DOIUrl":"https://doi.org/10.1117/12.2594889","url":null,"abstract":"As X-ray imaging is pushed further into the nanoscale, the sample deformations due to the increased radiation levels or mechanical instabilities of the microscopes become more apparent, leading to challenges in realizing high-resolution microscopy under these conditions. Here we propose a distributed optimization solver for imaging of samples at the nanoscale. Our approach solves the tomography and ptychography problems jointly with projection data alignment, nonrigid sample deformation correction, and regularization. Applicability of the method is demonstrated on experimental data sets from the Transmission X-ray Microscope, and the hard X-ray nanoprobe.","PeriodicalId":289295,"journal":{"name":"X-Ray Nanoimaging: Instruments and Methods V","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124749745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuto Tanaka, S. Matsuyama, Takato Inoue, Nami Nakamura, J. Yamada, Y. Kohmura, M. Yabashi, K. Omote, T. Ishikawa, K. Yamauchi
A wavefront measurement method in the microscope (magnifying) geometry can help achieve the required high accuracy for deformable mirrors. This study proposes an image-based wavefront measurement method based on a series of images of a small area near the focus. In this method, phase retrieval calculation using multiple images is performed. A proof-of-concept experiment was performed using multilayer AKB mirrors and an FZP to form the small area. Consequently, wavefront aberration was successfully retrieved using 60 images of a 30-nm-diameter area near the focus.
{"title":"Image-based wavefront measurement for full-field x-ray microscopy","authors":"Yuto Tanaka, S. Matsuyama, Takato Inoue, Nami Nakamura, J. Yamada, Y. Kohmura, M. Yabashi, K. Omote, T. Ishikawa, K. Yamauchi","doi":"10.1117/12.2595026","DOIUrl":"https://doi.org/10.1117/12.2595026","url":null,"abstract":"A wavefront measurement method in the microscope (magnifying) geometry can help achieve the required high accuracy for deformable mirrors. This study proposes an image-based wavefront measurement method based on a series of images of a small area near the focus. In this method, phase retrieval calculation using multiple images is performed. A proof-of-concept experiment was performed using multilayer AKB mirrors and an FZP to form the small area. Consequently, wavefront aberration was successfully retrieved using 60 images of a 30-nm-diameter area near the focus.","PeriodicalId":289295,"journal":{"name":"X-Ray Nanoimaging: Instruments and Methods V","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129357285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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