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Oppliger J, Denner MM, Küspert J, Frison R, Wang Q, Morawietz A, Ivashko O, Dippel AC, Zimmermann MV, Biało I, Martinelli L, Fauqué B, Choi J, Garcia-Fernandez M, Zhou KJ, Christensen NB, Kurosawa T, Momono N, Oda M, Natterer FD, Fischer MH, Neupert T, Chang J. Weak signal extraction enabled by deep neural network denoising of diffraction data. NAT MACH INTELL 2024; 6:180-186. [PMID: 38404481 PMCID: PMC10883886 DOI: 10.1038/s42256-024-00790-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 01/08/2024] [Indexed: 02/27/2024]
Abstract
The removal or cancellation of noise has wide-spread applications in imaging and acoustics. In applications in everyday life, such as image restoration, denoising may even include generative aspects, which are unfaithful to the ground truth. For scientific use, however, denoising must reproduce the ground truth accurately. Denoising scientific data is further challenged by unknown noise profiles. In fact, such data will often include noise from multiple distinct sources, which substantially reduces the applicability of simulation-based approaches. Here we show how scientific data can be denoised by using a deep convolutional neural network such that weak signals appear with quantitative accuracy. In particular, we study X-ray diffraction and resonant X-ray scattering data recorded on crystalline materials. We demonstrate that weak signals stemming from charge ordering, insignificant in the noisy data, become visible and accurate in the denoised data. This success is enabled by supervised training of a deep neural network with pairs of measured low- and high-noise data. We additionally show that using artificial noise does not yield such quantitatively accurate results. Our approach thus illustrates a practical strategy for noise filtering that can be applied to challenging acquisition problems.
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Affiliation(s)
- Jens Oppliger
- Physik-Institut, Universität Zürich, Zurich, Switzerland
| | | | - Julia Küspert
- Physik-Institut, Universität Zürich, Zurich, Switzerland
| | - Ruggero Frison
- Physik-Institut, Universität Zürich, Zurich, Switzerland
| | - Qisi Wang
- Physik-Institut, Universität Zürich, Zurich, Switzerland
- Department of Physics, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Oleh Ivashko
- Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany
| | | | | | - Izabela Biało
- Physik-Institut, Universität Zürich, Zurich, Switzerland
- Faculty of Physics and Applied Computer Science, AGH University of Krakow, Krakow, Poland
| | | | - Benoît Fauqué
- JEIP, USR 3573 CNRS, Collège de France, PSL University, Paris, France
| | | | | | | | | | - Tohru Kurosawa
- Department of Physics, Hokkaido University, Sapporo, Japan
| | - Naoki Momono
- Department of Physics, Hokkaido University, Sapporo, Japan
- Department of Applied Sciences, Muroran Institute of Technology, Muroran, Japan
| | - Migaku Oda
- Department of Physics, Hokkaido University, Sapporo, Japan
| | | | | | - Titus Neupert
- Physik-Institut, Universität Zürich, Zurich, Switzerland
| | - Johan Chang
- Physik-Institut, Universität Zürich, Zurich, Switzerland
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Liu D, Oppliger J, Cahlík A, Witteveen C, von Rohr FO, Natterer FD. A sacrificial magnet concept for field dependent surface science studies. MethodsX 2022; 10:101964. [PMID: 36578290 PMCID: PMC9791577 DOI: 10.1016/j.mex.2022.101964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
We demonstrate a straightforward approach to integrating a magnetic field into a low-temperature scanning tunneling microscope (STM) by adhering an NdFeB permanent magnet to a magnetizable sample plate. To render our magnet concept compatible with high-temperature sample cleaning procedures, we make the irreversible demagnetization of the magnet a central part of our preparation cycle. After sacrificing the magnet by heating it above its Curie temperature, we use a transfer tool to attach a new magnet in-situ prior to transferring the sample into the STM. We characterize the magnetic field created by the magnet using the Abrikosov vortex lattice of superconducting NbSe2. Excellent agreement between the distance dependent magnetic fields from experiments and simulations allows us to predict the magnitude and orientation of magnetic flux at any location with respect to the magnet and the sample plate. Our concept is an accessible solution for field-dependent surface science studies that require fields in the range of up to 400 mT and otherwise detrimental heating procedures.•Accessible magnetic field generation.•Selectable field strength and orientation.•Compatible with high-temperature sample preparation.
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Affiliation(s)
- Danyang Liu
- Department of Physics, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Jens Oppliger
- Department of Physics, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Aleš Cahlík
- Department of Physics, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Catherine Witteveen
- Department of Physics, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland,Department of Quantum Matter Physics, University of Geneva, 24 Quai Ernest-Ansermet, CH-1211 Geneva, Switzerland
| | - Fabian O. von Rohr
- Department of Quantum Matter Physics, University of Geneva, 24 Quai Ernest-Ansermet, CH-1211 Geneva, Switzerland
| | - Fabian Donat Natterer
- Department of Physics, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland,Corresponding Author.
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Oppliger J, Zengin B, Liu D, Hauser K, Witteveen C, von Rohr F, Natterer FD. Adaptive sparse sampling for quasiparticle interference imaging. MethodsX 2022; 9:101784. [PMID: 35898613 PMCID: PMC9309409 DOI: 10.1016/j.mex.2022.101784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/07/2022] [Indexed: 11/15/2022] Open
Abstract
Quasiparticle interference imaging (QPI) offers insight into the band structure of quantum materials from the Fourier transform of local density of states (LDOS) maps. Their acquisition with a scanning tunneling microscope is traditionally tedious due to the large number of required measurements that may take several days to complete. The recent demonstration of sparse sampling for QPI imaging showed how the effective measurement time could be fundamentally reduced by only sampling a small and random subset of the total LDOS. However, the amount of required sub-sampling to faithfully recover the QPI image remained a recurring question. Here we introduce an adaptive sparse sampling (ASS) approach in which we gradually accumulate sparsely sampled LDOS measurements until a desired quality level is achieved via compressive sensing recovery. The iteratively measured random subset of the LDOS can be interleaved with regular topographic images that are used for image registry and drift correction. These reference topographies also allow to resume interrupted measurements to further improve the QPI quality. Our ASS approach is a convenient extension to quasiparticle interference imaging that should remove further hesitation in the implementation of sparse sampling mapping schemes. • Accumulative sampling for unknown degree of sparsity • Controllably interrupt and resume QPI measurements • Scattering wave conserving background subtractions.
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Affiliation(s)
- Jens Oppliger
- Department of Physics, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
| | - Berk Zengin
- Department of Physics, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
| | - Danyang Liu
- Department of Physics, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
| | - Kevin Hauser
- Department of Physics, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland.,Department of Physics, Harvard University, 17 Oxford Street Cambridge, MA 02138, United States of America
| | - Catherine Witteveen
- Department of Physics, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland.,Department of Quantum Matter Physics, University of Geneva, 24 Quai Ernest-Ansermet, Geneva CH-1211, Switzerland
| | - Fabian von Rohr
- Department of Quantum Matter Physics, University of Geneva, 24 Quai Ernest-Ansermet, Geneva CH-1211, Switzerland
| | - Fabian Donat Natterer
- Department of Physics, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
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