Improving $Λ$ Signal Extraction with Domain Adaptation via Normalizing Flows
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916169938632704 |
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| author | Kelleher, Rowan McEneaney, Matthew Vossen, Anselm |
| author_facet | Kelleher, Rowan McEneaney, Matthew Vossen, Anselm |
| contents | The present study presents a novel application for normalizing flows for domain adaptation. The study investigates the ability of flow based neural networks to improve signal extraction of $Λ$ Hyperons at CLAS12. Normalizing Flows can help model complex probability density functions that describe physics processes, enabling uses such as event generation. $Λ$ signal extraction has been improved through the use of classifier networks, but differences in simulation and data domains limit classifier performance; this study utilizes the flows for domain adaptation between Monte Carlo simulation and data. We were successful in training a flow network to transform between the latent physics space and a normal distribution. We also found that applying the flows lessened the dependence of the figure of merit on the cut on the classifier output, meaning that there was a broader range where the cut results in a similar figure of merit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14076 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving $Λ$ Signal Extraction with Domain Adaptation via Normalizing Flows Kelleher, Rowan McEneaney, Matthew Vossen, Anselm High Energy Physics - Experiment Machine Learning The present study presents a novel application for normalizing flows for domain adaptation. The study investigates the ability of flow based neural networks to improve signal extraction of $Λ$ Hyperons at CLAS12. Normalizing Flows can help model complex probability density functions that describe physics processes, enabling uses such as event generation. $Λ$ signal extraction has been improved through the use of classifier networks, but differences in simulation and data domains limit classifier performance; this study utilizes the flows for domain adaptation between Monte Carlo simulation and data. We were successful in training a flow network to transform between the latent physics space and a normal distribution. We also found that applying the flows lessened the dependence of the figure of merit on the cut on the classifier output, meaning that there was a broader range where the cut results in a similar figure of merit. |
| title | Improving $Λ$ Signal Extraction with Domain Adaptation via Normalizing Flows |
| topic | High Energy Physics - Experiment Machine Learning |
| url | https://arxiv.org/abs/2403.14076 |