Fetch two distinct datasets which have some objects overlap in their photometric quantities, which maybe .ascii files or maybe Pandas objects (in our case it was .cat and .nzpcat files) Compare and cross-match the values of certain properties of the stars between the two datasets, and create a new dataset which contains all the overlapping objects, and plot the error variation between the two catalogues Hence, create a way to get through different datasets and reduce the clutter, and minimize errors in the data
We used 3D HST dataset to get the catalog files. The dataset we used comes from a near-infrared spectroscopic survey with the Hubble Space Telescope designed to study the physical processes that shape galaxies in the distant universe. The 248 3D-HST orbits are divided among 124 individual pointings, each observed for two orbits. This divides the dataset into different catalogs based on the field of observation. Here are the links to the catalogues: https://www.dropbox.com/sh/bwkqzmbfx9oc10h/AADYa7YjEzUW0575vTuCmYdDa?dl=0 http://3dhst.research.yale.edu/Data.php https://archive.stsci.edu/prepds/3d-hst/ https://archive.stsci.edu/prepds/3d-hst/