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Analysis-Specific Fast Simulation at the LHC with Deep Learning.

C Chen1, O Cerri2, T Q Nguyen2

  • 1State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University Haidan, Beijing, 100871 China.

Computing and Software for Big Science
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

We developed a fast deep neural network simulation to generate large datasets for W + jet events. This approach significantly reduces computing and storage needs for High-Luminosity LHC physics.

Keywords:
Deep LearningFast SimulationHadron Collider PhysicsHigh Energy Physics computing

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Area of Science:

  • High energy physics
  • Computational physics
  • Machine learning applications

Background:

  • Simulating particle collisions at the Large Hadron Collider (LHC) requires significant computational resources.
  • Detector resolution effects complicate the analysis of generated events.
  • The High-Luminosity LHC era poses unprecedented data processing challenges.

Purpose of the Study:

  • To develop a fast-simulation application for creating large, analysis-specific datasets.
  • To model detector resolution effects using a deep neural network.
  • To reduce the computational and storage demands of particle collision simulations.

Main Methods:

  • A deep neural network was trained to act as a transfer function, modeling detector resolution effects.
  • The network used analysis-specific features computed at the generation level.
  • A novel fast-simulation workflow was proposed, starting from generator-level events.

Main Results:

  • The application successfully models detector resolution effects.
  • The proposed workflow generates large, analysis-specific samples efficiently.
  • An order-of-magnitude reduction in computing and storage requirements was achieved.

Conclusions:

  • This fast-simulation strategy offers a viable solution for generating large datasets in high energy physics.
  • The approach can help the community address the computing challenges of the High-Luminosity LHC.
  • Deep neural networks provide an effective tool for simulating complex physics processes.