Unifying Simulation and Inference with Normalizing Flows
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917982130667520 |
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| author | Du, Haoxing Krause, Claudius Mikuni, Vinicius Nachman, Benjamin Pang, Ian Shih, David |
| author_facet | Du, Haoxing Krause, Claudius Mikuni, Vinicius Nachman, Benjamin Pang, Ian Shih, David |
| contents | There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum likelihood estimation (MLE) from conditional generative models for energy regression. Unlike direct regression techniques, the MLE approach is prior-independent and non-Gaussian resolutions can be determined from the shape of the likelihood near the maximum. Using an ATLAS-like calorimeter simulation, we demonstrate this concept in the context of calorimeter energy calibration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18992 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unifying Simulation and Inference with Normalizing Flows Du, Haoxing Krause, Claudius Mikuni, Vinicius Nachman, Benjamin Pang, Ian Shih, David High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability Instrumentation and Detectors Machine Learning There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum likelihood estimation (MLE) from conditional generative models for energy regression. Unlike direct regression techniques, the MLE approach is prior-independent and non-Gaussian resolutions can be determined from the shape of the likelihood near the maximum. Using an ATLAS-like calorimeter simulation, we demonstrate this concept in the context of calorimeter energy calibration. |
| title | Unifying Simulation and Inference with Normalizing Flows |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability Instrumentation and Detectors Machine Learning |
| url | https://arxiv.org/abs/2404.18992 |