One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914939350810624 |
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| author | Daumann, Caio Cesar Donega, Mauro Erdmann, Johannes Galli, Massimiliano Späh, Jan Lukas Valsecchi, Davide |
| author_facet | Daumann, Caio Cesar Donega, Mauro Erdmann, Johannes Galli, Massimiliano Späh, Jan Lukas Valsecchi, Davide |
| contents | Simulated events are key ingredients in almost all high-energy physics analyses. However, imperfections in the simulation can lead to sizeable differences between the observed data and simulated events. The effects of such mismodelling on relevant observables must be corrected either effectively via scale factors, with weights or by modifying the distributions of the observables and their correlations. We introduce a correction method that transforms one multidimensional distribution (simulation) into another one (data) using a simple architecture based on a single normalising flow with a boolean condition. We demonstrate the effectiveness of the method on a physics-inspired toy dataset with non-trivial mismodelling of several observables and their correlations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18582 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch Daumann, Caio Cesar Donega, Mauro Erdmann, Johannes Galli, Massimiliano Späh, Jan Lukas Valsecchi, Davide High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Data Analysis, Statistics and Probability Simulated events are key ingredients in almost all high-energy physics analyses. However, imperfections in the simulation can lead to sizeable differences between the observed data and simulated events. The effects of such mismodelling on relevant observables must be corrected either effectively via scale factors, with weights or by modifying the distributions of the observables and their correlations. We introduce a correction method that transforms one multidimensional distribution (simulation) into another one (data) using a simple architecture based on a single normalising flow with a boolean condition. We demonstrate the effectiveness of the method on a physics-inspired toy dataset with non-trivial mismodelling of several observables and their correlations. |
| title | One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2403.18582 |