Three-Dimensional Shapes of Spinning Helium Nanodroplets

Langbehn, Bruno

doi:10.11577/1496209

Title: 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:: Langbehn, Bruno

Publication Date:: Mon Feb 25 00:00:00 EST 2019

Other Number(s):: CXIDB ID 94

DOE Contract Number:: AC02-05CH11231

Research Org.:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Coherent X-ray Imaging Data Bank; MPI Berlin, FERMI

Sponsoring Org.:: MPI Berlin, FERMI

Keywords:: XFEL; X-ray Free-electorn Lasers; LDM; Coherent Diffraction Imaging; FERMI FEL-1; Superfluid Helium Nanodroplets

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.

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                    Langbehn, Bruno. Three-Dimensional Shapes of Spinning Helium Nanodroplets.  United States.  doi:https://doi.org/10.11577/1496209

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                    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:Mon Feb 25 00:00:00 EST 2019

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@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         = {Mon Feb 25 00:00:00 EST 2019},

  month        = {Mon Feb 25 00:00:00 EST 2019}

}

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DOI: https://doi.org/10.11577/1496209

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