# Acknowledgements

Citation: F. Leonarski, M. Bruckner, C. Lopez-Cuenca, A. Mozzanica, H.-C. Stadler, Z. Matej, A. Castellane, B. Mesnet, J. Wojdyla, B. Schmitt and M. Wang "Jungfraujoch: hardware-accelerated data-acquisition system for kilohertz pixel-array X-ray detectors" (2023), J. Synchrotron Rad., 30, 227-234 [doi:10.1107/S1600577522010268](https://doi.org/10.1107/S1600577522010268).

The project is supported by :
* Innosuisse via Innovation Project "NextGenDCU high data rate acquisition system for X-ray detectors in structural biology applications" (101.535.1 IP-ENG; Apr 2023 - Sep 2025).
* ETH Domain via Open Research Data Contribute project (Jan - Dec 2023)
* AMD University Program with donation of licenses of Ethernet IP cores and Vivado software

Decoding bitshuffle+LZ4 images on the GPU, rather than decompressing them on the host and uploading
the result, follows Jon Wright (ESRF): "Experiences with GPU decompression for bitshuffle + LZ4
data", HDF5 User Group meeting (2021), and [bslz4decoders](https://github.com/jonwright/bslz4decoders).
The CUDA kernels in Jungfraujoch are its own, but the approach is his.

Spot extraction groups strong pixels into spots with the sparse connected-component labelling of the
ACTS traccc project: P. Gessinger, H. M. Gray, A. Krasznahorkay, C. Leggett, J. Niermann,
A. Salzburger, S. N. Swatman and B. Yeo, "traccc: GPU track reconstruction library for HEP
experiments" (2025), [arXiv:2505.22822](https://arxiv.org/abs/2505.22822);
[traccc](https://github.com/acts-project/traccc). The CPU spot extractor adapts its SparseCCL source,
and the CUDA spot extractor follows the design of its GPU counterpart - a backward-neighbour graph
over a sorted hit list, resolved by a parallel union-find. traccc is MPL-2.0; see
[THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md).

This software uses Viridis, Magma and Inferno colormaps from Matplotlib under its BSD-compatible license