Normalizing Flows for Domain Adaptation when Identifying $Λ$ Hyperon Events
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914796306169856 |
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| author | Kelleher, Rowan Vossen, Anselm |
| author_facet | Kelleher, Rowan Vossen, Anselm |
| contents | This study focuses on the novel application of a normalizing flow as a method of domain adaptation. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The final distribution models a simulated distribution. Following the transformation process, the data can be classified by a neural network trained on labeled simulation data. The present study succeeds in training two normalizing flows that can transform between data (or simulation) and a Gaussian distribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14804 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Normalizing Flows for Domain Adaptation when Identifying $Λ$ Hyperon Events Kelleher, Rowan Vossen, Anselm Data Analysis, Statistics and Probability High Energy Physics - Experiment This study focuses on the novel application of a normalizing flow as a method of domain adaptation. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The final distribution models a simulated distribution. Following the transformation process, the data can be classified by a neural network trained on labeled simulation data. The present study succeeds in training two normalizing flows that can transform between data (or simulation) and a Gaussian distribution. |
| title | Normalizing Flows for Domain Adaptation when Identifying $Λ$ Hyperon Events |
| topic | Data Analysis, Statistics and Probability High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2403.14804 |