smartscan

Smartscan is a tool that allows autonomous control of nanoARPES experiments. It uses Gaussian Process regression to estimate the the most promising next point to measure in an n-dimensional parameter space. The tool is designed to be used with the SGM4 endstation at the ASTRID2 synchrotron in Aarhus, Denmark, but can be adapted to other endstations.

The Gaussian Process regression is implemented using the GPcam package (v7.4.10), description of which can be found here and the related paper can be found here.

Installation

To install the package, clone the repository and run the following command in the root directory:

pip install -e .

Smartscan is built using Python 3.10 and has been tested on Windows only. It should work on other platforms as well, but this has not been tested. The package has the following dependencies:

GPcam:

• gpcam == 7.4.10
• fvgp == 3.3.8
• hgdl == 2.0.7

Other: numpy, scipy, h5py, matplotlib, xarray, tqdm.

We strongly recommend using a virtual environment to install the package.

If installing using the command above, no additional dependencies are needed, as they are installed automatically. However, if you want to install the dependencies manually, the required packages are listed in the env.yml file. A manual installation of the dependencies using conda can be done by running the following command:

conda env create -f env.yml

which will create a new environment called smartscan with all the necessary dependencies.

Usage

Here we describe how to use the smartscan tool to run a scan on the SGM4 endstation. The tool can be used to run a scan on the real endstation, or to simulate a scan using pre-measured data.

SGM4 @ ASTRID2

To start a new scan, the SGM4 end station needs to be set up for a remote autonomous scan. Once that is running and listening on the TCP port, the GP driven scan can be started with

python -m smartscan

When no options are provided, the scan will be set up uning the settings from the config.json. These settings can be changed to any other file using the --config option.

python -m smartscan --config path/to/config.json

The scan will run until the specified number of measurements have been taken, or until the scan is stopped manually using Ctrl+C.

Simulation

To simulate running a scan without actually controlling any end-station, a dummy end-station can be started with

python -m smartscan.simulator

This dummy end-station will respond to the commands from the smartscan script as if it was the real end-station. The data returned by the simulator is picked from a pre-measured raster map. The file used for this is selected either in the configuration file or by providing the --data option.

python -m smartscan.simulator --data path/to/data.h5

Also here, the default settings for the simulator can be changed by providing a custom configuration file

python -m smartscan.simulator --config path/to/config.json

Once the simulator is running, a regular scan can be started as described above.

Data for Simulation

The data used for the simulation is stored in an HDF5 file format designed for the SGM4 endstation. Example datasets can can be found in the zenodo repository

Citation

If you use this tool in your research, please cite the following paper:

S.Y. Ágústsson, A. J. H. Jones, D. Curcio, S. Ulstrup, J. Miwa, D. Mottin, P. Karras, P. Hofmann, Autonomous micro-focus angle-resolved photoemission spectroscopy. Rev. Sci. Instrum. 95 055106 (2024)

DOI: https://doi.org/10.1063/5.0204663

Bibtex:

@article{10.1063/5.0204663,
    author = {Ágústsson, Steinn Ýmir and Jones, Alfred J. H. and Curcio, Davide and Ulstrup, Søren and Miwa, Jill and Mottin, Davide and Karras, Panagiotis and Hofmann, Philip},
    title = "{Autonomous micro-focus angle-resolved photoemission spectroscopy}",
    journal = {Review of Scientific Instruments},
    volume = {95},
    number = {5},
    pages = {055106},
    year = {2024},
    month = {05},
    abstract = "{Angle-resolved photoemission spectroscopy (ARPES) is a technique used to map the occupied electronic structure of solids. Recent progress in x-ray focusing optics has led to the development of ARPES into a microscopic tool, permitting the electronic structure to be spatially mapped across the surface of a sample. This comes at the expense of a time-consuming scanning process to cover not only a three-dimensional energy-momentum (E, kx, ky) space but also the two-dimensional surface area. Here, we implement a protocol to autonomously search both k- and real-space in order to find positions of particular interest, either because of their high photoemission intensity or because of sharp spectral features. The search is based on the use of Gaussian process regression and can easily be expanded to include additional parameters or optimization criteria. This autonomous experimental control is implemented on the SGM4 micro-focus beamline of the synchrotron radiation source ASTRID2.}",
    issn = {0034-6748},
    doi = {10.1063/5.0204663},
    url = {https://doi.org/10.1063/5.0204663},
    eprint = {https://pubs.aip.org/aip/rsi/article-pdf/doi/10.1063/5.0204663/19921467/055106\_1\_5.0204663.pdf},
}