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Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfaithful to the underlying physics. This leads to significant inaccuracies and biases in the transfer of trained models to real data, limiting the power of deep learning meth...
A modular pipeline decouples the expressiveness of the anomaly-detection model from real-time hardware constraints, enabling complex architectures to be used for model-agnostic searches for new physics at the full LHC collision rate of 40 MHz.
This work presents the FQTree algorithm for fine-grained quantization-aware training of BDTs, together with the QXGB framework for automatic hardware generation, and applies this quantization during boosting so that later trees adapt to the errors of the already-quantized ensemble.
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