Constraining primordial non-Gaussianity using neural networks

We present a novel approach to estimate the value of primordial non-Gaussianity (fNL) 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 fNL. The neural network model is trained on simulated CMB maps with known fNL in range of [-50, 50], and its performance is evaluated using various metrics. The results indicate that the proposed approach can accurately estimate fNL 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 suggest that the CNN technique can be widely applied to other cosmological parameter estimation directly from CMB images.