Main Topics

The Minimum Spanning Tree procedure was successfully used for decades within the QMD transport approach and its derivatives, but was limited by the usage of coordinate space only. The Phase-space Minimum Spanning Tree library was developed to overcome this limit by adding momentum space to the algorithm and to provide a model-independent MST version for QMD-based models, the BUU family, and other approaches propagating baryons.

Later development included a coalescence algorithm for deuterons, making it possible to compare different deuteron production mechanisms and suggest further experimental measurements.

• Main paper on psMST
• MST vs coalescence for deuterons
• How to identify the deuteron production mechanism

A new output file format based on ROOT::TTree was implemented for the PHQMD transport approach. The internal event structure speeds up analysis and enables interactive studies of heavy-ion collision evolution using Plotly.

• Source code
• b = 1 fm, small clusters
• b = 6 fm, small and intermediate size clusters
• b = 10 fm, big clusters

A new light nuclei analysis wagon was implemented for the Multi-Purpose Detector MpdRoot train-like analysis chain. For each particle of interest and centrality bin it automatically provides phase-space distributions, efficiency and contamination corrections, double-differential transverse momentum spectra, and other outputs.

The main wagon part is written in C++, and the post-processing analysis macro is written in Python. The source code was supplemented with documentation comments for Doxygen.

• Source code
• Slides

A QA framework for meteorological observables was created using PyGMT, Plotly, and Matplotlib. It represents error types such as ME, MAE, and RMSE for observables including wind speed, wind direction, temperature, and precipitation at different levels.

The framework can also show meteorological stations and other objects of interest on maps of selected regions.

• Source code
• Wind speed measurement systematic error
• Index of Agreement