7 citations · 15 across the 3 of their papers we have counts for
4 papers
Quark-versus-gluon tagging in CMS Open Data with CWoLa and TopicFlow
Matthew J. Dolan, John Gargalionis, Ayodele Ore
We use the CMS Open Data to examine the performance of weakly-supervised learning for tagging quark and gluon jets at the LHC. We target +jet and dijet events as respective quar…
TopicFlow: Disentangling quark and gluon jets with normalizing flows
Matthew J. Dolan, Ayodele Ore
The isolation of pure samples of quark and gluon jets is of key interest at hadron colliders. Recent work has employed topic modeling to disentangle the underlying distributions in…
Meta-learning and data augmentation for mass-generalised jet taggers
Matthew J. Dolan, Ayodele Ore
Deep neural networks trained for jet tagging are typically specific to a narrow range of transverse momenta or jet masses. Given the large phase space that the LHC is able to probe…
Equivariant Energy Flow Networks for Jet Tagging
Matthew J. Dolan, Ayodele Ore
Jet tagging techniques that make use of deep learning show great potential for improving physics analyses at colliders. One such method is the Energy Flow Network (EFN) - a recentl…