تقرير
Clusternets: A deep learning approach to probe clustering dark energy
| العنوان: | Clusternets: A deep learning approach to probe clustering dark energy |
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| المؤلفون: | Chegeni, Amirmohammad, Hassani, Farbod, Sadr, Alireza Vafaei, Khosravi, Nima, Kunz, Martin |
| سنة النشر: | 2023 |
| المجموعة: | Astrophysics General Relativity and Quantum Cosmology |
| مصطلحات موضوعية: | Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology |
| الوصف: | Machine Learning (ML) algorithms are becoming popular in cosmology for extracting valuable information from cosmological data. In this paper, we evaluate the performance of a Convolutional Neural Network (CNN) trained on matter density snapshots to distinguish clustering Dark Energy (DE) from the cosmological constant scenario and to detect the speed of sound ($c_s$) associated with clustering DE. We compare the CNN results with those from a Random Forest (RF) algorithm trained on power spectra. Varying the dark energy equation of state parameter $w_{\rm{DE}}$ within the range of -0.7 to -0.99, while keeping $c_s^2 = 1$, we find that the CNN approach results in a significant improvement in accuracy over the RF algorithm. The improvement in classification accuracy can be as high as $40\%$ depending on the physical scales involved. We also investigate the ML algorithms' ability to detect the impact of the speed of sound by choosing $c_s^2$ from the set $\{1, 10^{-2}, 10^{-4}, 10^{-7}\}$ while maintaining a constant $w_{\rm DE}$ for three different cases: $w_{\rm DE} \in \{-0.7, -0.8, -0.9\}$. Our results suggest that distinguishing between various values of $c_s^2$ and the case where $c_s^2=1$ is challenging, particularly at small scales and when $w_{\rm{DE}}\approx -1$. However, as we consider larger scales, the accuracy of $c_s^2$ detection improves. Notably, the CNN algorithm consistently outperforms the RF algorithm, leading to an approximate $20\%$ enhancement in $c_s^2$ detection accuracy in some cases. Comment: 12 pages, 6 figures, 6 tables; data available at https://doi.org/10.5281/zenodo.8220732 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2308.03517 |
| رقم الأكسشن: | edsarx.2308.03517 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |