Search

We present a novel approach to estimate the value of primordial non-Gaussianity (f 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 NL. The neural network model is trained on simulated CMB maps with known f 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 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 suggest that the CNN technique can be widely applied to other cosmological parameter estimation directly from CMB images. [ABSTRACT FROM AUTHOR]