Spectral-Index Measurements

Analysis class: mangadap.proc.spectralindices.SpectralIndices

Reference root: $MANGA_SPECTRO_ANALYSIS/$MANGADRP_VER/$MANGADAP_VER/[PLATE]/[IFUDESIGN]/ref

Reference file: manga-[PLATE]-[IFUDESIGN]-[DRPQA_KEY]-[BIN_KEY]-[CONTINUUM_KEY]-[ELFIT_KEY]-[SPINDEX_KEY].fits.gz

Config files: $MANGADAP_DIR/python/mangadap/config/spectral_indices

Example config: indxen.ini

[default]
 key                      = INDXEN
 minimum_snr              = 0.0
 resolution_fwhm          = -1
 compute_sigma_correction = True
 artifact_mask            = BADSKY
 absorption_indices       = EXTINDX
 bandhead_indices         = BHBASIC

Important class dependencies:

• mangadap.par.absorptionindexdb.AbsorptionIndexDB: Generalized class that provides the detailed parameters for a set of absorption-line spectral indices.
• mangadap.par.bandheadindexdb.BandheadIndexDB: Generalized class that provides the detailed parameters for a set of bandhead (or “color”) spectral indices.
• mangadap.proc.bandpassfilter: Provides the core functions that perform the bandpass integrals.

Algorithm:

• Read the artifact database to setup the mangadap.util.pixelmask.SpectralPixelMask object based on the artifact_mask config.

• Setup the mangadap.par.absorptionindexdb.AbsorptionIndexDB (using absorption_indices config) and mangadap.par.bandheadindexdb.BandheadIndexDB (using bandhead_indices config) databases with the indices to measure.

• Determine the binned spectra above the S/N limit set by the minimum_snr config.

• Mask binned spectra, ignoring pixels masked as DONOTUSE, IGNORED, FLUXINVALID, or FORESTAR in DAP LOGCUBE file.

• Get the best-fitting emission-line models from the mangadap.proc.emissionlinemodel.EmissionLineModel object and subtract it from them from the data; keep track of where an emission-line model is and is not defined.

• Measure the indices using mangadap.proc.spectralindices.SpectralIndices.measure_indices():

  • Compute flux per frequency, needed for some indices; i.e., convert spectra from \(F_\lambda\) to \(F_\nu\).

  • Isolate which indices use each definition (\(F_\lambda\) vs. \(F_\nu\))

  • Mask any “dummy” indices.

  • For each spectrum, redshift the band definition, measure the absorption-line indices using mangadap.proc.spectralindices.AbsorptionLineIndices, and the bandhead indices using mangadap.proc.spectralindices.BandheadIndices, and save the results using mangadap.proc.spectralindices.SpectralIndices.save_results().

    • Part of saving the results is to determine which indices were successfully measured. Only bands that are completely masked (or empty) are flagged as NOVALUE in the output maps. I also keep track of which bands are incomplete (only partially masked).

• Compute the velocity-dispersion corrections:

  • Get the best-fitting continuum model from the mangadap.proc.stellarcontinuummodel.StellarContinuumModel, both with (continuum) and without (continuum_dcnvlv) the convolution with the best-fitting line-of-sight velocity distribution function (LOSVD) using mangadap.proc.stellarcontinuummodel.StellarContinuumModel.fill_to_match().

  • Remeasure the indices on these two models (indx and dcnvlv_indx, respectively) and the correction based on the result using mangadap.proc.spectralindices.SpectralIndices.calculate_dispersion_corrections()

    • For mag unit indices, the correction is dcnvlv_indx-indx
    • For ang unit indices, the correction is dcnvlv_indx/indx

  • Any index with a bad correction is flagged as NOCORRECTION

• Construct spectral-index BINID map. Bin IDs are the same as for the binned spectra except that any bin that does not meet the S/N limit are given a spectral-index bin ID of -1.