177 citations · 1.3k across the 60 of their papers we have counts for
4 papers · 1 filter
GNN For Muon Particle Momentum estimation
Vishak K Bhat, Eric A. F. Reinhardt, Sergei Gleyzer
Due to a high rate of overall data generation relative to data generation of interest, the CMS experiment at the Large Hadron Collider uses a combination of hardware- and software-…
End-to-end deep learning inference with CMSSW via ONNX using docker
Purva Chaudhari, Shravan Chaudhari, Ruchi Chudasama +1
Deep learning techniques have been proven to provide excellent performance for a variety of high-energy physics applications, such as particle identification, event reconstruction…
End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data
Michael Andrews, Bjorn Burkle, Yi-fan Chen +10
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a…
End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC
Michael Andrews, Manfred Paulini, Sergei Gleyzer +1
This paper describes the construction of novel end-to-end image-based classifiers that directly leverage low-level simulated detector data to discriminate signal and background pro…