after performing the polynomial foreground removal with $n=3$. Interestingly, although the foreground removal method is relatively simple and has no prior that uses spatial correlation, we can reduce the diffuse structure shown in the left panel of Fig. \[fig:image\] and reduce the fluctuation by one order of magnitude.

The quality of fitting depends on regions because the smoothness is different for each LOS. In this work, we quantify the quality of the fitting using squared error, $\sum_i (d_i-f_i)^2$, where $i$ denotes the number of frequency bins, $d_i$ denotes the data and $f_i$ is the fitting function. Fig. \[fig:chi2\] shows the distribution of the error, and the spectral fitting works well in the center of the region. Thus, we calculate the APS using a region centered at (RA, Dec) = (-0.133 h, -28 deg) with a 4 degree radius, and then the resulting APS is reduced by 2 times. However we do not use the mask for simplicity and reducing the error of CPS which is proportional to $f_{\rm sky}^{-0.5}$.

![image](./Fig_paper/LOS_max.eps){width="8.5cm"} ![image](./Fig_paper/LOS_min.eps){width="8.5cm"}

![The EoR0 field at 171 MHz with $\Delta B=8 \,\rm MHz$, $t_{\rm int}= 3 \,\rm hours$, $(20\,\rm deg)^2$. We remove the foregrounds by the polynomial fitting with $n=7$ to 