U.S. Department of EnergyOffice of Scientific and Technical Information
Three-Dimensional Shapes of Spinning Helium Nanodroplets
Abstract
This repository contains data from an experiment at the LDM end station at FERMI FEL-1. The experimental details are described in Phys. Rev. Lett. 121, 255301; Langbehn et al (2018). In addition to the scattering data, the data file contains labels for a supervised machine learning task. These labels are subject of an upcoming publication about the applicability of neural networks within the domain of coherent diffraction imaging. The accompanying Python code for this paper can already be found at https://github.com/julian-carpenter/airynet.
- Authors:
-
- CXIDB
- Publication Date:
- Other Number(s):
- CXIDB ID 94
- DOE Contract Number:
- AC02-05CH11231
- Research Org.:
- Coherent X-ray Imaging Data Bank (Lawrence Berkeley National Laboratory); MPI Berlin, FERMI
- Sponsoring Org.:
- MPI Berlin, FERMI
- Subject:
- Coherent Diffraction Imaging; FERMI FEL-1; LDM; Superfluid Helium Nanodroplets; X-ray Free-electorn Lasers; XFEL
- OSTI Identifier:
- 1496209
- DOI:
- https://doi.org/10.11577/1496209
Citation Formats
Langbehn, Bruno. Three-Dimensional Shapes of Spinning Helium Nanodroplets. United States: N. p., 2019.
Web. doi:10.11577/1496209.
Langbehn, Bruno. Three-Dimensional Shapes of Spinning Helium Nanodroplets. United States. doi:https://doi.org/10.11577/1496209
Langbehn, Bruno. 2019.
"Three-Dimensional Shapes of Spinning Helium Nanodroplets". United States. doi:https://doi.org/10.11577/1496209. https://www.osti.gov/servlets/purl/1496209. Pub date:Sun Feb 24 23:00:00 EST 2019
@article{osti_1496209,
title = {Three-Dimensional Shapes of Spinning Helium Nanodroplets},
author = {Langbehn, Bruno},
abstractNote = {This repository contains data from an experiment at the LDM end station at FERMI FEL-1. The experimental details are described in Phys. Rev. Lett. 121, 255301; Langbehn et al (2018). In addition to the scattering data, the data file contains labels for a supervised machine learning task. These labels are subject of an upcoming publication about the applicability of neural networks within the domain of coherent diffraction imaging. The accompanying Python code for this paper can already be found at https://github.com/julian-carpenter/airynet.},
doi = {10.11577/1496209},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Feb 24 23:00:00 EST 2019},
month = {Sun Feb 24 23:00:00 EST 2019}
}
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