  We present a novel approach for estimating cosmological parameters,
$\Omega_m$, $\sigma_8$, $w_0$, and one derived parameter, $S_8$, from 3D
lightcone data of dark matter halos in redshift space covering a sky area of
$40^\circ \times 40^\circ$ and redshift range of $0.3 < z < 0.8$, binned to
$64^3$ voxels. Using two deep learning algorithms, Convolutional Neural Network
(CNN) and Vision Transformer (ViT), we compare their performance with the
standard two-point correlation (2pcf) function. Our results indicate that CNN
yields the best performance, while ViT also demonstrates significant potential
in predicting cosmological parameters. By combining the outcomes of Vision
Transformer, Convolution Neural Network, and 2pcf, we achieved a substantial
reduction in error compared to the 2pcf alone. To better understand the inner
workings of the machine learning algorithms, we employed the Grad-CAM method to
investigate the sources of essential information in activation maps of the CNN
and ViT. Our findings suggest that the algorithms focus on different parts of
the density field and redshift depending on which parameter they are
predicting. This proof-of-concept work paves the way for incorporating deep
learning methods to estimate cosmological parameters from large-scale
structures, potentially leading to tighter constraints and improved
understanding of the Universe.
