Speaker
Description
b-hive is a general-purpose machine learning framework developed for the CMS experiment. Every stage of the workflow, from dataset construction and training to inference and evaluation, is encapsulated in a self-contained Law task, while physics-specific choices such as architecture, input features, truth definitions, and kinematic selections are injected through YAML configuration files and Python modules. The same core infrastructure therefore serves arbitrary classification and regression tasks on complex, variable-length HEP data. ROOT files are processed columnar-wise with coffea into LZ4-compressed datasets served by a custom iterable data loader; combined with mixed-precision training and torch.compile, this yields a factor 12.8 speed-up over the previous DeepJetCore-based setup, reducing a week-long training to roughly 13 hours. b-hive ships implementations of model jet tagging algorithms, together with a fully configurable adversarial module for robustness studies and adversarial training. The framework was used to develop the Unified Particle Transformer v2, the official CMS jet tagging algorithm for Run 3. This presentation covers the design, current performance, and planned developments.