VAC Overview ¶

Description ¶

The following pages describe the content and construction of the FastSpecFit value-added catalogs (VACs) for the following DESI Data Releases and Data Assemblies:

Note

A DESI data release has been publicly released whereas a data assembly is only available internally to DESI collaboration members; not all data assemblies become data releases.

Data Organization ¶

Within the data release directory of each VAC, there are two key subdirectories, healpix and catalogs, which we now describe in more detail.

We run FastSpecFit on the healpix-coadded DESI spectra, and organize the files identically to how the spectra, redshift catalogs, and other data products are organized in the DESI data releases and assemblies (as documented here). In other words, relative to the VAC data release directory VACDIR, the individual fastspec files (see Algorithms) can be found at the following location:

$VACDIR/healpix/SURVEY/PROGRAM/HPIXGROUP/HEALPIX/fastspec-SURVEY-PROGRAM-HEALPIX.fits.gz

where SURVEY, PROGRAM, HPIXGROUP, and HEALPIX are fully documented here.

Note

The fastspec catalogs are gzipped because they contain the fitting results as well as the best-fitting model spectra; see the fastspec data model pages for a full description of the contents of these files.

Merged Catalogs ¶

Most users will be interested in the merged FastSpecFit catalogs, which combine all the individual healpix catalogs for a given SURVEY and PROGRAM into a single file. Relative to the top-level data release directory VACDIR, the merged catalog filenames have the following syntax:

$VACDIR/catalogs/fastspec-SURVEY-PROGRAM.fits

Note

In order to keep the size of the files reasonable, these files do not contain the MODELS FITS extension (see the fastspec data model page for a description of this FITS extension).

Sample Selection ¶

The sample selection criteria were chosen to be very inclusive so that modeling results would be available for as many objects as possible.

In brief, we fit all extragalactic (redshift greater than \(10^{-3}\)), non-sky targets with no cuts on targeting bits, redshift or fiber-assignment warning bits other than a simple cut which throws out spectra with no data.

Specifically, let redrockfile be the full pathname to a given Redrock catalog. The following bit of Python code illustrates which targets we fit:

import fitsio
import numpy as np
from fastspecfit.io import ZWarningMask

zb = fitsio.read(redrockfile, 'REDSHIFTS')
fm = fitsio.read(redrockfile, 'FIBERMAP')

I = np.where((zb['Z'] > 0.001) * (fm['OBJTYPE'] == 'TGT') *
             (zb['ZWARN'] & ZWarningMask.NODATA == 0))[0]

Here, the ZWarningMask.NODATA bit indicates a spectrum which contains no data (all inverse variance pixel values in the extracted spectrum are zero).

Updated QSO Redshifts ¶

For a small but important fraction of quasar (QSO) targets, the redshift determined by Redrock is incorrect. To mitigate this issue, the DESI team has developed an approach to rectify the redshift nominally measured by Redrock using the machine-learning algorithm QuasarNet.

Let redrockfile and qnfile be the full pathname to a given Redrock catalog and QuasarNet catalog, respectively. We update the Redrock redshift Z (and store the original Redrock redshift in Z_RR; see the fastspec data model) for all QSO targets using the following bit of code:

import fitsio
import numpy as np
from astropy.table import Table
from desitarget.targets import main_cmx_or_sv

QNLINES = ['C_LYA', 'C_CIV', 'C_CIII', 'C_MgII', 'C_Hbeta', 'C_Halpha']
QNCOLS = ['TARGETID', 'Z_NEW', 'IS_QSO_QN_NEW_RR', 'C_LYA', 'C_CIV',
          'C_CIII', 'C_MgII', 'C_Hbeta', 'C_Halpha']

zb = Table(fitsio.read(redrockfile, 'REDSHIFTS'))

# find QSO targets
surv_target, surv_mask, surv = main_cmx_or_sv(meta)
if surv == 'cmx':
    desi_target = surv_target[0]
    desi_mask = surv_mask[0]
    # need to check multiple QSO masks
    IQSO = []
    for bitname in desi_mask.names():
        if 'QSO' in bitname:
            IQSO.append(np.where(meta[desi_target] & desi_mask[bitname] != 0)[0])
    if len(IQSO) > 0:
        IQSO = np.sort(np.unique(np.hstack(IQSO)))
else:
    desi_target, bgs_target, mws_target = surv_target
    desi_mask, bgs_mask, mws_mask = surv_mask
    IQSO = np.where(meta[desi_target] & desi_mask['QSO'] != 0)[0]

if len(IQSO) > 0:
    qn = Table(fitsio.read(qnfile, 'QN_RR', columns=QNCOLS))
    assert(np.all(qn['TARGETID'] == zb['TARGETID'][IQSO]))
    print('Updating QSO redshifts using a QN threshold of 0.95.')
    qn['IS_QSO_QN'] = np.max(np.array([qn[name] for name in QNLINES]), axis=0) > 0.95
    qn['IS_QSO_QN_NEW_RR'] &= qn['IS_QSO_QN']
    if np.count_nonzero(qn['IS_QSO_QN_NEW_RR']) > 0:
        zb['Z'][IQSO[qn['IS_QSO_QN_NEW_RR']]] = qn['Z_NEW'][qn['IS_QSO_QN_NEW_RR']]

Issues ¶

For questions (or problems) regarding any of the VACs catalogs or their construction, please open a ticket and/or contact John Moustakas (Siena College).