"Machine learning can be used to fully reconstruct particle collisions at the LHC [Large Hadron Collider]. This new approach can reconstruct collisions more quickly and precisely than traditional methods, helping physicists better understand LHC data. [...] Each proton–proton collision at the LHC sprays out a complex pattern of particles that must be carefully reconstructed to allow physicists to study what really happened. For more than a decade, CMS has used a particle-flow (PF) algorithm, which combines information from the experiment's different detectors, to identify each particle produced in a collision. Although this method works remarkably well, it relies on a long chain of hand-crafted rules designed by physicists. The new CMS machine-learning-based particle-flow (MLPF) alg...
