DAPall File Construction

Analysis class: mangadap.survey.dapall.DAPall

File Root: $MANGA_SPECTRO_ANALYSIS/$MANGADRP_VER/$MANGADAP_VER

File Template: dapall-$MANGADRP_VER-$MANGADAP_VER.fits

Important class dependencies:

• mangadap.survey.drpcomplete.DRPComplete: Provides the database used to construct which PLATEIFU observations were analyzed.
• mangadap.par.analysisplan.AnalysisPlan: Identifies the unique analysis approaches used to analyze each datacube.
• mangadap.dapfits.DAPMapsBitMask: Interprets the masks in the MAPS files.
• Many of the other core classes are needed but only to define the methods used by the analysis approaches selected.

Algorithm:

• Instantiate the class used to perform cosmology calculations, astropy.cosmology.FlatLambdaCDM, where we set \(h=1\), \(\Omega_M_=0.3\), and \(\Omega_\Lambda = 0.7\).

• Parse the plan file

• Ensure that all plans to add compute the same emission-line moments, emission-line models, and spectral indices.

• Create list of possibly complete observations and analysis products.

• For each MAPS file that should exist:

  • Find the associated row in the DRPall file, and copy some of those data.

  • Calculate the luminosity and angular diameter distance based on the NSA redshift

  • Check that the file exists, and if so continue:

  • Grab information from the MAPS file header

  • Calculate the luminosity and angular diameter distance based on the input guess redshift (usually the same as the NSA redshift)

  • Calculate the radial coverage metric using mangadap.survey.dapall.DAPall._radial_coverage_metric().

  • Pull the S/N metrics from the MAPS header

  • Get the mean g-band surface brightness within 1 \(R_e\).

  • Get the binning metrics using mangadap.survey.dapall.DAPall._binning_metrics().

  • Get the stellar kinematics metrics using mangadap.survey.dapall.DAPall._stellar_kinematics_metrics().

  • Get the H-alpha kinematics metrics using mangadap.survey.dapall.DAPall._halpha_kinematics_metrics().

  • Get the emission-line metrics using mangadap.survey.dapall.DAPall._emission_line_metrics().

  • Get the spectral-index metrics using mangadap.survey.dapall.DAPall._spectral_index_metrics().

  • Calculate the star-formation rates based on the H-alpha flux within 1 \(R_e\) and over the full FOV. E.g.,

    log_Mpc_in_cm = numpy.log10(astropy.constants.pc.to('cm').value) + 6
    log_halpha_luminosity_1re = numpy.log10(4*numpy.pi) \
                + numpy.log10(db['EMLINE_GFLUX_1RE'][i,self.elfit_channels['Ha-6564']]) \
                - 17 + 2*numpy.log10(db['LDIST_Z'][i]) + 2*log_Mpc_in_cm
    db['SFR_1RE'][i] = numpy.power(10, log_halpha_luminosity_1re - 41.27)
    

• Add the channel names to the header