Computational challenges for MC event generation

Abstract

The sophistication of fully exclusive MC event generation has grown at an extraordinary rate since the start of the LHC era, but has been mirrored by a similarly extraordinary rise in the CPU cost of state-of-the-art MC calculations. The reliance of experimental analyses on these calculations raises the disturbing spectre of MC computations being a leading limitation on the physics impact of the HL-LHC, with MC trends showing more signs of further cost-increases rather than the desired speed-ups. I review the methods and bottlenecks in MC computation, and areas where new computing architectures, machine-learning methods, and social structures may help to avert calamity.

Based on a plenary talk at ACAT 2019

References in corpus (8)

• PYTHIA 6.4 Physics and Manual
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• Matching NLO QCD computations with Parton Shower simulations: the POWHEG method
• Event generation with SHERPA 1.1
• Herwig++ Physics and Manual
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Cited by in corpus (10)

• Challenges in Monte Carlo event generator software for High-Luminosity LHC
• Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
• Novel approaches in Hadron Spectroscopy
• Quantum integration of elementary particle processes
• How to GAN Event Subtraction
• Uncertainties associated with GAN-generated datasets in high energy physics
• OASIS: Optimal Analysis-Specific Importance Sampling for event generation
• Accelerating Berends-Giele recursion for gluons in arbitrary dimensions over finite fields
• A general approach to quantum integration of cross sections in high-energy physics
• Event Generation and Density Estimation with Surjective Normalizing Flows