TrackFormers Part 2: Enhanced Transformer-Based Models for High-Energy Physics Track Reconstruction
This work addresses the critical need for faster and more accurate track reconstruction in high-energy physics experiments, particularly for the upcoming High-Luminosity LHC upgrade, representing an incremental improvement over their previous models.
The authors tackled the challenge of particle track reconstruction in high-energy physics experiments by enhancing Transformer-based models with new loss functions and attention mechanisms, achieving improved accuracy and efficiency for next-generation data processing demands.
High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data necessitates critical revisions across the data processing pipeline, with particle track reconstruction being a prime candidate for improvement. In our previous work, we introduced "TrackFormers", a collection of Transformer-based one-shot encoder-only models that effectively associate hits with expected tracks. In this study, we extend our earlier efforts by incorporating loss functions that account for inter-hit correlations, conducting detailed investigations into (various) Transformer attention mechanisms, and a study on the reconstruction of higher-level objects. Furthermore we discuss new datasets that allow the training on hit level for a range of physics processes. These developments collectively aim to boost both the accuracy, and potentially the efficiency of our tracking models, offering a robust solution to meet the demands of next-generation high-energy physics experiments.