ArticleNature communications2017
Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 97 citations in OpenAlex.
- Multi-facility virtual diagnostic for longitudinal phase space predictions.Scientific reports · 2026Article
- Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning.Scientific reports · 2024Article
- Prediction on X-ray output of free electron laser based on artificial neural networks.Nature communications · 2023Article
- Machine learning for laser-induced electron diffraction imaging of molecular structures.Communications chemistry · 2021Article
- Machine Learning for Electronically Excited States of Molecules.Chemical reviews · 2021Article
- Physically inspired deep learning of molecular excitations and photoemission spectra.Chemical science · 2021Article
- Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning.Scientific reports · 2021Article
- Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics.Scientific reports · 2021Article
- Pulse-to-pulse wavefront sensing at free-electron lasers using ptychography.Journal of applied crystallography · 2020Article
- Development of ultrafast capabilities for X-ray free-electron lasers at the linac coherent light source.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
- Quantitative characterization of bovine serum albumin thin-films using terahertz spectroscopy and machine learning methods.Biomedical optics express · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
43 authors at 13 institutions in 6 countries.
Funding
Abstract
Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy, we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. This opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.