تقرير
Neural Network Reconstruction of Late-Time Cosmology and Null Tests
| العنوان: | Neural Network Reconstruction of Late-Time Cosmology and Null Tests |
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| المؤلفون: | Dialektopoulos, Konstantinos, Said, Jackson Levi, Mifsud, Jurgen, Sultana, Joseph, Adami, Kristian Zarb |
| المصدر: | Journal of Cosmology and Astroparticle Physics, 02, 023 (2022) |
| سنة النشر: | 2021 |
| المجموعة: | Astrophysics General Relativity and Quantum Cosmology Physics (Other) |
| مصطلحات موضوعية: | Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, General Relativity and Quantum Cosmology, Physics - Data Analysis, Statistics and Probability |
| الوصف: | The prospect of nonparametric reconstructions of cosmological parameters from observational data sets has been a popular topic in the literature for a number of years. This has mainly taken the form of a technique based on Gaussian processes but this approach is exposed to several foundational issues ranging from overfitting to kernel consistency problems. In this work, we explore the possibility of using artificial neural networks (ANN) to reconstruct late-time expansion and large scale structure cosmological parameters. We first show how mock data can be used to design an optimal ANN for both parameters, which we then use with real data to infer their respective redshift profiles. We further consider cosmological null tests with the reconstructed data in order to confirm the validity of the concordance model of cosmology, in which we observe a mild deviation with cosmic growth data. Comment: 28 pages, 9 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1088/1475-7516/2022/02/023 |
| URL الوصول: | http://arxiv.org/abs/2111.11462 |
| رقم الأكسشن: | edsarx.2111.11462 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1088/1475-7516/2022/02/023 |
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