A machine learning route between band mapping and band structure

Xian, R. Patrick; Stimper, Vincent; Zacharias, Marios; Dong, Shuang; Dendzik, Maciej; Beaulieu, Samuel; Schölkopf, Bernhard; Wolf, Martin; Rettig, Laurenz; Carbogno, Christian; Bauer, Sebastian; Ernstorfer, Ralph

doi:10.48550/arxiv.2005.10210

Cited by 5 publications

(5 citation statements)

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“…Determination of the complete (time-resolved) electronic band structure dynamics with the MM bears an enormous potential. Most directly, it allows to track complex momentum-and energydependent scattering phenomena, shines light on quasiparticle lifetimes 60 , and permits benchmark comparison to band structure theory 61 . As the MM measurements are performed at a fixed sample geometry, it allows to investigate higherorder modulation effects of the photoemission intensity, such as orbital interference 62 .…”

Section: Discussionmentioning

confidence: 99%

A quantitative comparison of time-of-flight momentum microscopes and hemispherical analyzers for time- and angle-resolved photoemission spectroscopy experiments

Maklar,

Dong,

Beaulieu

et al. 2020

Preprint

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Time-of-flight-based momentum microscopy has a growing presence in photoemission studies, as it enables parallel energy-and momentum-resolved acquisition of the full photoelectron distribution. Here, we report table-top extreme ultraviolet (XUV) time-and angle-resolved photoemission spectroscopy (trARPES) featuring both a hemispherical analyzer and a momentum microscope within the same setup. We present a systematic comparison of the two detection schemes and quantify experimentally relevant parameters, including pump-and probe-induced space-charge effects, detection efficiency, photoelectron count rates, and depth of focus. We highlight the advantages and limitations of both instruments based on exemplary trARPES measurements of bulk WSe2. Our analysis demonstrates the complementary nature of the two spectrometers for time-resolved ARPES experiments. Their combination in a single experimental apparatus allows us to address a broad range of scientific questions in trARPES.

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Section: Discussionmentioning

confidence: 99%

A quantitative comparison of time-of-flight momentum microscopes and hemispherical analyzers for time- and angle-resolved photoemission spectroscopy experiments

Maklar,

Dong,

Beaulieu

et al. 2020

Preprint

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“…This requires the development of new data handling and data analysis schemes, which enable highly efficient online data screening during experiments as well as in-depth analysis afterwards. Looking on the bright side, the extensive spectroscopic information can pave the way towards yet unexplored data analysis procedures that could be based on pattern recognition algorithms, machine learning or big data analysis schemes [120]. This could potentially revolutionize band structure imaging and set the stage for uncovering novel band structure signatures beyond the conventionally investigated high-symmetry directions of materials in momentum space.…”

Section: Current Challenges and Future Strategiesmentioning

confidence: 99%

The 2021 ultrafast spectroscopic probes of condensed matter roadmap

Lloyd‐Hughes

Oppeneer

Santos

et al. 2021

J. Phys.: Condens. Matter

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In the 60 years since the invention of the laser, the scientific community has developed numerous fields of research based on these bright, coherent light sources, including the areas * Authors to whom any correspondence should be addressed. 19 Guest editors of the roadmap.Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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“…For example, a deep-layer convolutional neural network (ConvNet) has been trained to denoise ARPES data [31]. Afterward, there have been efforts to determine how the band structure calculation is related to the ARPES data [32,33], where the [33] provides an additional feature for obtaining the result even through noisy data (simulated noise). There has been also work in automation of spatial domain assignment with a smaller subset of data over a predetermined area, where subsequent position measurement is calculated with the Gaussian process to give the possible highest amount of information [34].…”

Section: Introductionmentioning

confidence: 99%

Transfer learning application of self-supervised learning in ARPES

Ekahana

Winata

Soh

et al. 2023

Mach. Learn.: Sci. Technol.

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There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lasers, together with focussing beam optics and advanced electron spectrometers, are beginning to enable angle-resolved photoemission spectroscopy (ARPES) in scanning mode with a spatial resolution of near to and below microns, two- to three orders of magnitude smaller than what has been typical for ARPES hitherto. The results are vast data sets inhabiting a five-dimensional subspace of the ten-dimensional space spanned by two scanning dimensions of real space, three of reciprocal space, three of spin-space, time, and energy. In this work, we demonstrate that recent developments in representational learning (self-supervised learning) combined with k-means clustering can help automate the labeling and spatial mapping of dispersion cuts, thus saving precious time relative to manual analysis, albeit with low performance. Finally, we introduce a few-shot learning (k-nearest neighbor or kNN) in representational space where we selectively choose one (k=1) image reference for each known label and subsequently label the rest of the data with respect to the nearest reference image. This last approach demonstrates the strength of self-supervised learning to automate image analysis in ARPES in particular and can be generalized to any scientific image analysis.

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A machine learning route between band mapping and band structure

Cited by 5 publications

References 0 publications

A quantitative comparison of time-of-flight momentum microscopes and hemispherical analyzers for time- and angle-resolved photoemission spectroscopy experiments

A quantitative comparison of time-of-flight momentum microscopes and hemispherical analyzers for time- and angle-resolved photoemission spectroscopy experiments

The 2021 ultrafast spectroscopic probes of condensed matter roadmap

Transfer learning application of self-supervised learning in ARPES

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