Speaker
Description
The reconstruction of physical observables in hadron collider events from recorded experimental quantities poses a repeated task in almost any data analysis at the LHC. While the experiments record hits in tracking detectors and signals in the calorimeters, which are subsequently combined into particle-flow objects, jets, muons, electrons, missing transverse energy, or similar high-level objects, the obervables of interest are commonly related to the dynamics of the particles created in the hard collision, like top-quarks, weak bosons (W,Z), or the Higgs boson. Their reconstruction is more challenging, suffering from combinatorial ambiguities, tagging inefficiencies, acceptance losses, pile-up, or other experimental effects.
We present a new strategy for the reconstruction of hadron collider events using mini-jets as only reconstructed objects together with a machine-learning algorithm for the determination of observables of interest. These mini-jets are obtained with a distance measure of R=0.1, and reduce the full information from all particles in an event to an experimentally and computationally managable size. We show that with the help of a deep neural network observables related to intermediate W bosons or top quarks can be directly regressed, as well as particle-level jets with larger R, or dressed leptons. This ansatz outperforms classical reconstruction algorithms and paves the way for a simplified and more generic event reconstruction for future LHC analyses.
