SDSS-IV MaNGA DR17 Principcal Component Analysis spaxel classifcations

The zip files contains 10120 fits.gz files and two Python .p files. The files spaxel_properties_master_DR17.p and elliptical_radii_params_DR17.p contain all of the PCA values of spaxels from all galaxies and all the fraction of spaxels of a particular type in an ellipse (so looping over all of the maps to get this information is not necessary). There is one map per MaNGA galaxy. The data structure of each fits.gz file is: HDU 0: [image] primary header from the DAP MAPS file. HDU 1: [image] 'PC1' - PC1 amplitude. HDU 2: [image] 'PC2' - PC2 amplitude. HDU 3: [image] 'PC3' - PC3 amplitude. HDU 4: [image] 'PC1ERR' - PC1 error. HDU 5: [image] 'PC2ERR' - PC2 error. HDU 6: [image] 'PC3ERR' - PC3 error. HDU 7: [image] qualmask – Mask applied to PC1 map, has mask=(snr_4000A.T < 4.) | (pc1_map_reshaped.T < -10.) | (nocov) | (lowcov) | (donotuse) | (deadfiber) | (forestar). i.e. it excludes low S/N spaxels, weird PCA values and bad spaxels. HDU 8: [image] 'snr4000A' - Median signal-to-noise in the 4000A break region. HDU 9: [image] 'norm' - normalisation of the spectrum in the PCA. Used for reconstruction of the spectrum. HDU 10: [image] 'class_map' - map of PCA classifications. 1=quiescent, 2=star-forming, 3=starburst, 4=green valley, 5-post-starburst, 0=unclassified (do not use) HDU 11: [image] 'spx_bin_mask' - Mask accounting for identical values in a bin (see below for more details). The PCA code (see https://github.com/KateRowlands/MaNGA-PCA, Rowlands et al. 2018, based on Wild et al. 2007) is run on the HYB10-MILESHC-MASTARSSP cubes, where the stellar continuum is binned but the emission line measurements are done on the unbinned spectra (see SDSS DR17 DAP documentation for more details). In these maps the spectra in each stellar continuum bin are identical, so the PC amplitudes are identical. The analysis is done in this way to preserve the shape of the maps for comparing to other quantities. The identical nature of spaxels in the same bin needs to be accounted for in some analysis e.g. those which count spaxels of a certain PCA class. The spx_bin_mask accounts for this double counting by providing a mask which has the central spaxel in the Voronoi bin set to 1. For spaxels with unique PCA values, set spx_bin_mask==1. If plotting 2D maps of the PCA classes then spx_bin_mask should not be applied otherwise there will be gaps in the maps. To flag out poor quality spaxels, reject anything with snr4000A < 4, although different S/N cuts may be applied depending on your science case. Furthermore, the PCA parameters are affected by dust. PCA classifications of PSBs in regions with visible dust lanes e.g. in edge-on and or/ dusty galaxies should be closely examine by hand to ensure robustness. Values of -99 and 99 indicate no data or bad data and should be excluded.