Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866915302535593984 |
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| author | Kofler, Annalena Stimper, Vincent Mikhasenko, Mikhail Kagan, Michael Heinrich, Lukas |
| author_facet | Kofler, Annalena Stimper, Vincent Mikhasenko, Mikhail Kagan, Michael Heinrich, Lukas |
| contents | High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow Annealed importance sampling Bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16234 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics Kofler, Annalena Stimper, Vincent Mikhasenko, Mikhail Kagan, Michael Heinrich, Lukas High Energy Physics - Phenomenology Machine Learning Computational Physics Data Analysis, Statistics and Probability High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow Annealed importance sampling Bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods. |
| title | Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics |
| topic | High Energy Physics - Phenomenology Machine Learning Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2411.16234 |