Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2412.21147 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917891987734528 |
|---|---|
| author | Vega, Octavio Lytle, Andrew Shen, Jiayu El-Khadra, Aida X. |
| author_facet | Vega, Octavio Lytle, Andrew Shen, Jiayu El-Khadra, Aida X. |
| contents | Lattice QCD is notorious for its computational expense. Modern lattice simulations require large-scale computational resources to handle the large number of Dirac operator inversions used to construct correlation functions. Machine learning (ML) techniques that can increase, at the analysis level, the information inferred from the correlation functions would therefore be beneficial. We apply supervised learning to infer two-point lattice correlation functions at different target masses. Our work proposes a new method for separating data into training and bias correction subsets for efficient uncertainty estimation. We also benchmark our ML models against a simple ratio method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_21147 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using AI for Efficient Statistical Inference of Lattice Correlators Across Mass Parameters Vega, Octavio Lytle, Andrew Shen, Jiayu El-Khadra, Aida X. High Energy Physics - Lattice Lattice QCD is notorious for its computational expense. Modern lattice simulations require large-scale computational resources to handle the large number of Dirac operator inversions used to construct correlation functions. Machine learning (ML) techniques that can increase, at the analysis level, the information inferred from the correlation functions would therefore be beneficial. We apply supervised learning to infer two-point lattice correlation functions at different target masses. Our work proposes a new method for separating data into training and bias correction subsets for efficient uncertainty estimation. We also benchmark our ML models against a simple ratio method. |
| title | Using AI for Efficient Statistical Inference of Lattice Correlators Across Mass Parameters |
| topic | High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2412.21147 |