Refining Fast Calorimeter Simulations with a Schrödinger Bridge
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| Format: | Preprint |
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2023
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| _version_ | 1866909554231476224 |
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| author | Diefenbacher, Sascha Mikuni, Vinicius Nachman, Benjamin |
| author_facet | Diefenbacher, Sascha Mikuni, Vinicius Nachman, Benjamin |
| contents | Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics events are not close to Gaussian and so these neural networks have to learn a highly complex function. We study an alternative approach: Schrödinger bridge Quality Improvement via Refinement of Existing Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of diffusion-based neural networks and Schrödinger bridges to map between samples where the probability density is not known explicitly. We apply SQuIRELS to the task of refining a classical fast simulation to approximate a full classical simulation. On simulated calorimeter events, we find that SQuIRELS is able to reproduce highly non-trivial features of the full simulation with a fraction of the generation time. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_12339 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Refining Fast Calorimeter Simulations with a Schrödinger Bridge Diefenbacher, Sascha Mikuni, Vinicius Nachman, Benjamin Instrumentation and Detectors High Energy Physics - Experiment High Energy Physics - Phenomenology Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics events are not close to Gaussian and so these neural networks have to learn a highly complex function. We study an alternative approach: Schrödinger bridge Quality Improvement via Refinement of Existing Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of diffusion-based neural networks and Schrödinger bridges to map between samples where the probability density is not known explicitly. We apply SQuIRELS to the task of refining a classical fast simulation to approximate a full classical simulation. On simulated calorimeter events, we find that SQuIRELS is able to reproduce highly non-trivial features of the full simulation with a fraction of the generation time. |
| title | Refining Fast Calorimeter Simulations with a Schrödinger Bridge |
| topic | Instrumentation and Detectors High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2308.12339 |