Jet substructure classification in high-energy physics with deep neural networks
Top Cited Papers
Open Access
- 27 May 2016
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review D
- Vol. 93 (9), 094034
- https://doi.org/10.1103/physrevd.93.094034
Abstract
At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap. Deducing whether the substructure of an observed jet is due to a low-mass single particle or due to multiple decay objects of a massive particle is an important problem in the analysis of collider data. Traditional approaches have relied on expert features designed to detect energy deposition patterns in the calorimeter, but the complexity of the data make this task an excellent candidate for the application of machine learning tools. The data collected by the detector can be treated as a two-dimensional image, lending itself to the natural application of image classification techniques. In this work, we apply deep neural networks with a mixture of locally connected and fully connected nodes. Our experiments demonstrate that without the aid of expert features, such networks match or modestly outperform the current state-of-the-art approach for discriminating between jets from single hadronic particles and overlapping jets from pairs of collimated hadronic particles, and that such performance gains persist in the presence of pileup interactions.Funding Information
- National Science Foundation (IIS-1321053, DGE 1106400)
- Nvidia
This publication has 31 references indexed in Scilit:
- Soft dropJournal of High Energy Physics, 2014
- Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012The European Physical Journal C, 2014
- Energy correlation functions for jet substructureJournal of High Energy Physics, 2013
- Jet substructure at the Tevatron and LHC: new results, new tools, new benchmarksJournal of Physics G: Nuclear and Particle Physics, 2012
- Boosted objects: a probe of beyond the standard model physicsThe European Physical Journal C, 2011
- Identifying boosted objects with N-subjettinessJournal of High Energy Physics, 2011
- Stop reconstruction with tagged topsJournal of High Energy Physics, 2010
- Jet trimmingJournal of High Energy Physics, 2010
- Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top QuarksPhysical Review Letters, 2008
- Jet Substructure as a New Higgs-Search Channel at the Large Hadron ColliderPhysical Review Letters, 2008