The next generation of high energy particle colliders will have features that allows for highly granular detectors and current methods for particle collision reconstruction are limited. The authors explore machine learning algorithms for reconstructing events in electron-positron collisions for such future colliders obtaining a best-performing graph neural network that enhances the jet transverse momentum resolution by up to 50%, outperforming traditional methods and promising significant advancements in future collider measurements.
- Joosep Pata
- Eric Wulff
- Javier Duarte
