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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.15992 |
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| _version_ | 1866909500348301312 |
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| author | Wolf, Moritz Stietz, Lars O. Connor, Patrick L. S. Schleper, Peter Bein, Samuel |
| author_facet | Wolf, Moritz Stietz, Lars O. Connor, Patrick L. S. Schleper, Peter Bein, Samuel |
| contents | The availability of precise and accurate simulation is a limiting factor for interpreting and forecasting data in many fields of science and engineering. Often, one or more distinct simulation software applications are developed, each with a relative advantage in accuracy or speed. The quality of insights extracted from the data stands to increase if the accuracy of faster, more economical simulation could be improved to parity or near parity with more resource-intensive but accurate simulation. We present Fast Perfekt, a machine-learned regression that employs residual neural networks to refine the output of fast simulations. A deterministic morphing model is trained using a unique schedule that makes use of the ensemble loss function MMD, with the option of an additional pair-based loss function such as the MSE. We explore this methodology in the context of an abstract analytical model and in terms of a realistic particle physics application featuring jet properties in hadron collisions at the CERN Large Hadron Collider. The refinement makes maximum use of domain knowledge, and introduces minimal computational overhead to production. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15992 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Perfekt: Regression-based refinement of fast simulation Wolf, Moritz Stietz, Lars O. Connor, Patrick L. S. Schleper, Peter Bein, Samuel High Energy Physics - Experiment Computational Physics The availability of precise and accurate simulation is a limiting factor for interpreting and forecasting data in many fields of science and engineering. Often, one or more distinct simulation software applications are developed, each with a relative advantage in accuracy or speed. The quality of insights extracted from the data stands to increase if the accuracy of faster, more economical simulation could be improved to parity or near parity with more resource-intensive but accurate simulation. We present Fast Perfekt, a machine-learned regression that employs residual neural networks to refine the output of fast simulations. A deterministic morphing model is trained using a unique schedule that makes use of the ensemble loss function MMD, with the option of an additional pair-based loss function such as the MSE. We explore this methodology in the context of an abstract analytical model and in terms of a realistic particle physics application featuring jet properties in hadron collisions at the CERN Large Hadron Collider. The refinement makes maximum use of domain knowledge, and introduces minimal computational overhead to production. |
| title | Fast Perfekt: Regression-based refinement of fast simulation |
| topic | High Energy Physics - Experiment Computational Physics |
| url | https://arxiv.org/abs/2410.15992 |