| CXIDB ID 94 | |
| Deposition Summary | |
|---|---|
| Depositor: | Julian Zimmermann |
| Contact: | [email protected] |
| Deposition date: | 2019-02-25 |
| Last modified: | 2019-08-19 |
| DOI: | 10.11577/1496209 |
| Publication Details | |
| Title: | Three-Dimensional Shapes of Spinning Helium Nanodroplets |
| Authors: | Bruno Langbehn et al. |
| Journal: | Physical Review Letters |
| Year: | 2018 |
| DOI: | 10.1103/PhysRevLett.121.255301 |
| Experimental Conditions | |
| Method: | Coherent Diffraction Imaging |
| Sample: | Superfluid Helium Nanodroplets |
| Wavelength: | between 19 eV and 35 eV |
| Lightsource: | FERMI FEL-1 |
| Beamline: | LDM |
Data Files
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| CXI File: | helium_nanodroplets_with_labels_zimmermann.cxi (14.57 GB) |
Description
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 the publication Phys. Rev. E 99, 063309; Zimmermann et al (2019) about the applicability of neural networks within the domain of coherent diffraction imaging. The accompanying Python code for this paper can be found at https://github.com/julian-carpenter/airynet.
