this work, we perform a simple polynomial fitting to the spectral dimension of the data, and use this as the foreground removal.

First, we make 24 images with 1.28 MHz spectral channels from the MWA data in the observed frequency range of 167 MHz to 197 MHz. Next, we fit a $n$-th-order polynomial function along the frequency axis at each pixel of the image cube, and obtain smooth functions. Finally, we subtract the smooth functions from our main images.

The left (right) panel of Fig. \[fig:fit\] shows examples of the fitting for the brightest (faintest) pixel at 167 MHz. Circles are values at a pixel of each image for the fitting, and triangles are values of main images. Clearly, the circles are not smooth and have a wave-like feature as a function of frequency. This feature is caused from the mode-mixing and frequency dependence of the instrumental point spread function (e.g. [@2012ApJ...752..137M]). In the images transformed from visibility data, there is a concentric side lobe structure around point sources because of the sparse uv-coverage, and the side lobe propagates emission from point sources in the field. Perfect removal of this structure is impossible due to the number of point sources.

