MaNGA Caveats

The MaNGA Data Reduction Pipeline (DRP) is a living pipeline that is constantly evolving based on the MaNGA team analysis of the data. Although the DR14 data pipeline (v2_1_2) is nearly identical to the DR13 pipeline (v1_5_4) there are nonetheless a few small differences.

• In the previous DR13, comparison of the MaNGA galaxies to high-resolution DiskMass (Bershady et al. 2010) observations suggested that the spectral resolution of the MaNGA data was overestimated by about 10%. In DR14, we now account for both post-pixellization profile-fitting effects when measuring skyline widths and broadening in the effective line spread function (LSF) introduced by wavelength rectification of the data to a common grid. Tests using observations of DiskMass galaxies and twilight sky spectra suggest that the DR14 reductions report more accurate spectral resolutions than DR13, although this is still under investigation. In particular, the DR14 spectral resolution vectors may still be inaccurate at the few percent level shortward of 4000 Angstrom.
• Modified calculation of dereddened signal to noise values to use local reddening map instead of plate-averaged values. (Note spectra are not dereddend.)
• Spaxels affected by foreground stars (where known) are now ignored by the astrometric routine.
• Slightly adjusted bias calculation routine, added artificial inverse variance term to limit arbitrarily high signal-to-noise values in the blue cameras (previously only present in red cameras).
• Slight adjustment to sky subtraction algorithms and logical flow to optimize processing for the Coma cluster ancillary program plates.

Detailed change logs are given in the release notes.

The primary data products of the Data Reduction Pipeline (DRP) are fits files. When reading these files, it's important to understand the ordering of the data within the array. Fits files were originally developed using FORTRAN, a row-major language. When reading the files using IDL, the intended ordering of the axes as (x,y,λ) is maintained. However, this ordering is transposed when using astropy.io.fits to (&lambda,y,x). Please see their FAQ, specifically the response to this question. Please see the MaNGA Python Tutorial for example code.

Each MaNGA data cube has an associated 3d maskbit array describing the quality of a given value DRP3PIXMASK , and whether it should be used in an analysis. This includes effects such as the IFU footprint, missing data, foreground stars (where known), etc. Any use of the MaNGA data should consider these maskbits.

DR14 does not provide covariance calculations for the provided datacubes; however, there is significant covariance between adjacent spaxels. When combining spectra from multiple spaxels, a rigorous calculation of the inverse variance in the combined spectrum must account for this covariance. Short of that, we provide a calibration of the noise vector calculated without considering the covariance to a calculation that does. The calibration is:

n_covar/n_{no covar} = 1 + 1.62 log₁₀(N_bin),

for N_bin ≤ 100
and

n_covar/n_{no covar} = 4.2,

for N_bin > 100

where n_{no covar} is determined via a nominal error calculation using the inverse variance provided in the datacube and N_bin is the number of binned spaxels. The correction factor is constant above N_bin = 100 because additional spaxels at that point are uncorrelated with the original spaxels. It is important to note that this calibration is dependent on the spaxels being adjacent. Full covariance information will be provided in a future data release.

See further discussion in Law et al. (2016)

Although most cosmic rays and other transient features are detected by the DRP and flagged (either for removal or for masking), lower-intensity glitches can make it into the final datacubes and show up as occasional hot pixels. Further improvements to the DRP to handle these spurious cosmic rays is still under development. Currently, some of these artifacts are being addressed on a case-by-case basis; however, individual hot glitches are not addressed. It is particularly important to be wary of this when searching for isolated emission features in the data cubes.

The 3D phase of the DRP has an overall reduction quality bit DRP3QUAL that indicates any potential quality control issues with a given output file for each observation. Most of these issues, like shallow observations, are simply warnings that the data might not be of the usual quality. Flux-calibration failures, however, trigger the CRITICAL quality bit, which indicates that there may be severe problems with the data. This is determined by whether or not the astrometric calibration is successful without a substantial rescaling of the flux to match the imaging data.

Critical failures occur in roughly 2% of observations. These are a mixture of true critical failures (where, e.g., an IFU is badly out of focus, such as 7495-6103) and less critical issues where transients or bright objects at the edges of the field cause problems with the astrometric solution. Reasons for the latter can include some instances where the on-sky surface brightness distribution seems to be genuinely different from that predicted by the preimaging (such as 8332-12702). In some cases the extra flux comes from a terrestrial transient (satellite trails, etc). Further development is underway to address these issues.

The Secondary sample was designed to have a higher density of targets than is required to give the desired 2:1 ratio of Primary+ to Secondary galaxies. Therefore, before allocating IFUs we randomly sample the Secondary sample to the desired target density. Due to a very recently discovered bug in the target selection code this random sampling is not truly random and in fact samples in such a way as to make the number density distribution flat as a function of stellar mass. This is a small change, since the density distribution was already quite close to being flat with stellar mass. However, it does mean the observed Secondary sample is no longer selected purely by i-band absolute magnitude and redshift, but also has a weak dependence on stellar mass. It also has consequences for calculating the appropriate weights to apply to any sample containing the Secondary sample (see the weights FAQ).

For more information, see Wake et al. (in press).