Enhanced Cross-Task Learning Architecture for Boosted Decision Trees
Jayram Bhat Palamadai · 2025
In this project, we introduce Cross-Sectional Adaptive Transfer Learning (CATL), a novel transfer learning algorithm for signal classification in high-energy physics. CATL estimates a representation-based task distance between source and target background tasks using event cross-sections. We demonstrate the validity of CATL, its ability to produce robust classifications, and its efficacy in minimizing empirical loss for signal tasks. We then apply CATL to the ongoing search for the dark photon to compare results against previous methodologies. These results are generalized by producing risk bounds through gradient descent. Extensive model validation across multiple datasets, including cross-validation and statistical tests, provides strong evidence of the model's robustness and adaptability. The CATL methodology consistently yields improvements in signal efficiency by over 20% while simultaneously increasing target-aware background rejection compared to standard signal classifiers.