  Tabular data stands out as one of the most frequently encountered types in
high energy physics. Unlike commonly homogeneous data such as pixelated images,
simulating high-dimensional tabular data and accurately capturing their
correlations are often quite challenging, even with the most advanced
architectures. Based on the findings that tree-based models surpass the
performance of deep learning models for tasks specific to tabular data, we
adopt the very recent generative modeling class named conditional flow matching
and employ different techniques to integrate the usage of Gradient Boosted
Trees. The performances are evaluated for various tasks on different analysis
level with several public datasets. We demonstrate the training and inference
time of most high-level simulation tasks can achieve speedup by orders of
magnitude. The application can be extended to low-level feature simulation and
conditioned generations with competitive performance.
