تقرير
Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure
| العنوان: | Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure |
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| المؤلفون: | Yip, Jacky H. T., Rouhiainen, Adam, Shiu, Gary |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Mathematics Astrophysics |
| مصطلحات موضوعية: | Astrophysics - Cosmology and Nongalactic Astrophysics, Computer Science - Machine Learning, Mathematics - Algebraic Topology |
| الوصف: | The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topological information, the optimal method for parameter estimation from the tool remains an open question. To address this, we propose a neural network model to map persistence images to cosmological parameters. Through a parameter recovery test, we demonstrate that our model makes accurate and precise estimates, considerably outperforming conventional Bayesian inference approaches. Comment: 7 pages, 4 figures. Accepted to the Synergy of Scientific and Machine Learning Modeling Workshop (ICML 2023) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2308.02636 |
| رقم الأكسشن: | edsarx.2308.02636 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |