Reconstructing unstable heavy particles is a crucial aspect of many analyses at the Large Hadron Collider (LHC). We introduce SPA-Net, a machine-learning approach to this problem which outperforms existing baseline methods, performs several auxiliary tasks, and leads to significant improvements in three example flagship LHC analyses
- Michael James Fenton
- Alexander Shmakov
- Pierre Baldi