Footprint

MaNGA footprint

MaNGA’s target selection needs galaxies with SDSS imaging and known redshifts. Therefore, the sample is selected from the NYU VAGC LSS footprint. We assign all targets selected to a number of 3-degree-diameter circular fields which match the field of view of the telescope. Our final field list (what we refer to as “tiling catalogue”) contains ~1800 of these fields (tiles). Given the observing time allocated, we will only be able to observe about 600 tiles in six years.

The grey symbols in the figure above show the distribution of all our potential fields (our tiling catalogue) in the NYU VAGC LSS footprint. The blue symbols indicate the 80 fields (plates) that MaNGA has observed during its first year of observations. These exclude all-sky plates and standard star plates (see special plates page for detail). The observed plates are largely concentrated in a band around declination ~45 degrees. Fields around that declination are easier to observe, as they reach high altitude in the sky whilst not being too close to the zenith, where tracking is more challenging for an Alt-Az telescope.

The fact that there are more fields or tiles than MaNGA will be able to observe during the lifetime of SDSS-IV means that we have a fair degree of freedom to optimize our footprint. In future years we plan to select fields for observation to maximize the overlap with other surveys that will provide additional, multi-wavelength data for MaNGA galaxies.

Python

It is possible to obtain a list of all the observed plates from the drpall file, as well as select plates that contain certain targets in which you are interested. Using Astropy and Numpy to obtain a list of all reduced plates, you can do

from astropy.table import Table
import numpy as np
drpall = Table.read('drpall-v1_5_4.fits')
plates = np.unique(drpall['plate'])

At this point you may want to reject plates without galaxy targets, for instance:

targets = drpall[np.where((drpall[‘mngtarg1'] > 0) | (drpall['mngtarg3'] > 0))]
plates = np.unique(drpall['plate'])

If you are interested in finding all plates that contain ancillary targets for a specific program, you can use the manga_target3 column (see more about bitmasks). For instance, the bit for the Milky Way analogues program is 13. To get the plates for the two observed targets in DR13, we do

targets = drpall[np.where(drpall['mngtarg3'] & 1<<13)]
plates = np.unique(targets['plate'])