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| Main Authors: | , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2403.02115 |
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| _version_ | 1866914725775802368 |
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| author | Nagarajappa, Chandan G. Ma, Yin-Zhe |
| author_facet | Nagarajappa, Chandan G. Ma, Yin-Zhe |
| contents | We present a novel approach to estimate the value of primordial non-Gaussianity ($f_{\rm NL}$) parameter directly from the Cosmic Microwave Background (CMB) maps using a convolutional neural network (CNN). While traditional methods rely on complex statistical techniques, this study proposes a simpler approach that employs a neural network to estimate $f_{\rm NL}$. The neural network model is trained on simulated CMB maps with known $f_{\rm NL}$ in range of $[-50,50]$, and its performance is evaluated using various metrics. The results indicate that the proposed approach can accurately estimate $f_{\rm NL}$ values from CMB maps with a significant reduction in complexity compared to traditional methods. With $500$ validation data, the $f^{\rm output}_{\rm NL}$ against $f^{\rm input}_{\rm NL}$ graph can be fitted as $y=ax+b$, where $a=0.980^{+0.098}_{-0.102}$ and $b=0.277^{+0.098}_{-0.101}$, indicating the unbiasedness of the primordial non-Gaussianity estimation. The results indicate that the CNN technique can be widely applied to other cosmological parameter estimation directly from CMB images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02115 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Constraining primordial non-Gaussianity using Neural Networks Nagarajappa, Chandan G. Ma, Yin-Zhe Cosmology and Nongalactic Astrophysics We present a novel approach to estimate the value of primordial non-Gaussianity ($f_{\rm NL}$) parameter directly from the Cosmic Microwave Background (CMB) maps using a convolutional neural network (CNN). While traditional methods rely on complex statistical techniques, this study proposes a simpler approach that employs a neural network to estimate $f_{\rm NL}$. The neural network model is trained on simulated CMB maps with known $f_{\rm NL}$ in range of $[-50,50]$, and its performance is evaluated using various metrics. The results indicate that the proposed approach can accurately estimate $f_{\rm NL}$ values from CMB maps with a significant reduction in complexity compared to traditional methods. With $500$ validation data, the $f^{\rm output}_{\rm NL}$ against $f^{\rm input}_{\rm NL}$ graph can be fitted as $y=ax+b$, where $a=0.980^{+0.098}_{-0.102}$ and $b=0.277^{+0.098}_{-0.101}$, indicating the unbiasedness of the primordial non-Gaussianity estimation. The results indicate that the CNN technique can be widely applied to other cosmological parameter estimation directly from CMB images. |
| title | Constraining primordial non-Gaussianity using Neural Networks |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2403.02115 |