End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

HIGHLIGHTS

• who: Shah Rukh Qasim from the Experimental Physics Department, CERN, Geneva, Switzerland Manchester Metropolitan University, Manchester, UK have published the article: End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks, in the Journal: Eur. Phys. J. C
• what: The authors show the jet reconstruction performance of the method and discuss its inference computational cost. The model is trained for 68 epochs using the Adam optimizer on the training set described in Sect 4, at which point the loss becomes stable and does not further decrease with additional epochs. For . . .
  

   

  

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