Surrogate models of neural networks in high energy physics

Title Surrogate models of neural networks in high energy physics
Description An event recorded with a particle detector, from which ten particle streams (known as “jets”, represented as orange cones), a certain elementary particle (muon, red line) and additional elementary particles (yellow lines) have emerged. This visualization is based on data measured with the particle detector. In addition to experimental data, simulated or synthetic data are also commonly used in high energy physics. For the correct generation of synthetic data sets, all physical boundary conditions must be taken into account, which makes this task computationally intensive. The use of neural networks could considerably accelerate the production of synthetic data sets. With large amounts of synthetic data generated without great effort, scientists could in the future test more hypotheses on the standard model of elementary particle physics and beyond this model. The illustration here contains only a few of the detector’s elements like the inner layers (blue) as well as the muon chambers (gray). The muon chambers are anchored in the steel yoke (red). The yoke guides the strong magnetic fields necessary for precise measurement of the particles.
Copyright CASUS/2021 CMS Collaboration
Picture Id 63572
Date 15.06.2021
Downloads:
1773 x 1330 px Show | Download JPEG 1,7 MB
140 x 105 px Show | Download JPEG 11 kB
200 x 150 px Show | Download JPEG 10 kB
400 x 300 px Show | Download JPEG 64 kB