rgbloom 1.2

RGB from Gaia EDR3

rgbloom

This Python script retrieves RGB magnitudes computed from low resolution spectra published in Gaia DR3, following the work described in Carrasco et al. (2023). These magnitudes are given in the standard system defined by Cardiel et al. (2021a).

This code is an updated version of rgblues, which provides RGB magnitudes from Gaia EDR3 photometric data, as explained in Cardiel et al. (2021b).

The RGB magnitudes provided by Carrasco et al. (2023) are more reliable because they have been directly computed from the source spectrum without the need to employ any approximate calibration, nor introducing constraints on the source colour or extinction. In addition, the number of sources with RGB estimates has increased from ~15 million to ~200 million objects (the 200M sample). Anyway, since the sky coverage of the 200M sample is still not very good at some high Galactic latitudes, rgbloom also provides RGB estimates for sources that do not belong to the 200M sample making use of the polynomial calibrations of Cardiel et al. (2021b), which may still be useful for those users requiring calibrated RGB sources at those sky regions.

The code rgbloom performs a cone search defined by coordinates right ascension and declination on the sky and a search radius. The cone search is performed making use of the Astroquery coordinated package of astropy.

You need to have a live connection to the Internet for the script to work!

Installing the code

In order to keep your current Python installation clean, it is highly recommended to install a python 3 virtual environment first.

Creating and activating the python virtual environment

$ python3 -m venv venv_rgb
$ . venv_rgb/bin/activate
(venv_rgb) $

Installing the package

(venv_rgb) $ pip install git+https://github.com/guaix-ucm/rgbloom.git@main#egg=rgbloom

Executing the program

Just execute it from the command line:

(venv_rgb) $ rgbloom 56.66 24.10 1.0 12

The last instruction executes the program providing the four positional arguments: right ascension, declination, search radius and limiting Gaia G magnitude. Note that the coordinates and search radius must be given in decimal degrees.

Each time the code is executed, some auxiliary files are downloaded to your computer (if they have not been downloaded in a previous execution). These files are kept in a cache directory that is displayed in the terminal output (you do not have to worry about its location unless you need to delete them in order to recover disk space).

The execution of this example should led to the following output in the terminal (except for the absolute path where the auxiliary downloaded files are stored):

        Welcome to rgbloom version 1.2
        ==============================

Downloading data from 'http://nartex.fis.ucm.es/~ncl/rgbphot/gaiaDR3/reference_healpix8.csv' to file '/Users/cardiel/Library/Caches/pooch/635cd722cf61b23bd8eee20635e4d580-reference_healpix8.csv'.
<STEP1> Starting cone search in Gaia DR3... (please wait)
  INFO: Query finished. [astroquery.utils.tap.core]
        --> 310 objects found
        --> 23 objects classified as VARIABLE
<STEP2> Estimating RGB magnitudes in DR3 query using C21 polynomials OK!
<STEP3> Retrieving objects from the 200M sample in the enclosing HEALPIx level-8 tables
Downloading data from 'http://nartex.fis.ucm.es/~ncl/rgbphot/gaiaDR3/RGBsynthetic_NOVARIABLES/sortida_XpContinuousMeanSpectrum_006602-007952_RGB_NOVARIABLES_final.csv.gz' to file '/Users/cardiel/Library/Caches/pooch/2d94d5acfcb380d6dff1eaa207caa086-sortida_XpContinuousMeanSpectrum_006602-007952_RGB_NOVARIABLES_final.csv.gz'.
        * Required file: /Users/cardiel/Library/Caches/pooch/2d94d5acfcb380d6dff1eaa207caa086-sortida_XpContinuousMeanSpectrum_006602-007952_RGB_NOVARIABLES_final.csv.gz
          md5:f9cf7ed0f84eecda13ef6a408d291b96
        --> Number of objects: 100553
        --> Total number of objects: 100553
<STEP4> Cross-matching DR3 with 200M sample
        --> Number of objects in the 200M subsample............: 100553
        --> Number of objects in DR3 query.....................: 310
        --> Number of DR3 objects within the 200M sample.......: 248
        --> Number of DR3 objects no present in the 200M sample: 62
<STEP5> Saving output CSV files
        --> file rgbloom_200m.csv saved
        --> file rgbloom_no200m.csv saved
<STEP6> Generating PDF plot
End of program

The rgbloom script executes the following steps:

• Step 1: cone search in Gaia DR3, gathering the following parameters: source_id, ra, dec, phot_g_mean_mag, phot_bp_mean_mag, phot_rp_mean_mag and phot_variable_flag

• Step 2: initial RGB magnitude estimation using the polynomial transformations given in Eqs. (2)-(4) of Cardiel et al. (2021b). These values are only provided for objects in the field of view that do not belong to the 200M sample.

• Step 3: downloading of the RGB magnitude estimates corresponding to the 200M sample objects within the HEALPIx level-8 tables enclosing the region of the sky defined in the initial cone search.

• Step 4: cross-matching between the DR3 and 200M subsamples to identify objects with RGB estimates derived from the low resolution Gaia DR3 spectra.

• Step 5: generation of the output files. Two files (in CSV format) are generated:

  • rgbloom_200m.csv: objects belonging to the 200M sample with RGB magnitudes computed as described in Carrasco et al. (2023). This CSV file provides the following columns:

    • number: consecutive number of the object in the CSV file (used in the final plot)
    • source_id: identification in Gaia DR3
    • ra: right ascension (from Gaia DR3)
    • dec: declination (from Gaia DR3)
    • RGB_B: blue RGB magnitude estimate
    • RGB_G: green RGB magnitude estimate
    • RGB_R: red RGB magnitude estimate
    • errRGB_B: uncertainty in the blue RGB magnitude estimate
    • errRGB_G: uncertainty in the green RGB magnitude estimate
    • errRGB_R: uncertainty in the red RGB magnitude estimate
    • objtype: type of source, according to the classification provided by Gaia DR3 (see description of GAIA_SOURCE table for details):
      • 1: object flagged as NON_SINGLE_STAR
      • 2: object flagged as IN_QSO_CANDIDATES
      • 3: object flagged as IN_GALAXY_CANDIDATES
      • 0: none of the above
    • qlflag: global quality flag:
      • 0: reliable source
      • 1: suspicious source (blending, contamination, non-stellar identification)

  • rgbloom_no200m.csv: objects not included in the 200M sample, which RGB magnitudes are estimated using the approximate polynomial calibrations of Cardiel et al. (2021b). This CSV file contains the following columns:

    • number: consecutive number of the object in the CSV file (used in the final plot)
    • source_id: identification in Gaia DR3
    • ra: right ascension (from Gaia DR3)
    • dec: declination (from Gaia DR3)
    • phot_variable_flag: photometric variability flag (from Gaia DR3)
    • bp_rp: G_BP-G_RP colour (from Gaia DR3)
    • RGB_B: blue RGB magnitude estimate
    • RGB_G: green RGB magnitude estimate
    • RGB_R: red RGB magnitude estimate

  The list of objects in these two files is sorted by right ascension.

• Step 6: generation of a finding chart plot (in PDF format): rgbloom.pdf. The execution of the previous example generates a cone search around the Pleiades star cluster: The objects in this plot are color coded based on the Gaia G_BP - G_RP colour. Stars brighter than a pre-defined threshold are displayed with big star symbols. To facilitate the identification of each object, the consecutive identification numbers in the two files rgbloom_200m.csv and rgbloom_no200m.csv are also displayed, in red and black, respectively. The identification numbers corresponding to the less reliable sources in rgbloom_20m.csv (qlflag=1) appear inside a rectangle with a light-gray border. Note that the identification numbers are not displayed when using the parameter --nonumbers in the command line.

  In the case of objects that do not belong to the 200M sample (i.e., those in rgbloom_no200m.csv), a blue square has been overplotted on the sources flagged as variable in Gaia DR3, and a grey diamond on objects outside the Gaia -0.5 < G_BP - G_RP < 2.0 colour interval.

Note that the three output archives (1 PDF and 2 CSV files) share the same root name rgbloom. This can be easily modified using the optional argument --basename <newbasename> in the command line.

Additional help

Some auxiliary optional arguments are also available. See description invoking the script help:

$ rgbloom --help

...
...

positional arguments:
  ra_center             right Ascension (decimal degrees)
  dec_center            declination (decimal degrees)
  search_radius         search radius (decimal degrees)
  g_limit               limiting Gaia G magnitude

optional arguments:
  -h, --help            show this help message and exit
  --basename BASENAME   file basename for output files
  --brightlimit BRIGHTLIMIT
                        objects brighter than this Gaia G limit are displayed with star symbols (default=8.0)
  --symbsize SYMBSIZE   multiplying factor for symbol size (default=1.0)
  --nonumbers           do not display object identification number in PDF chart
  --noplot              skip PDF chart generation
  --nocolor             do not use colors in PDF chart
  --verbose             increase program verbosity

Citation

If you find this Python package useful, please cite Cardiel et al. (2021a) (to quote the use of the standard RGB system) and Carrasco et al. (2023) (where the computation of the RGB magnitudes from the low resolution spectra published in Gaia DR3 is explained).

Related information

You can visit the RGB Photometry web page at the Universidad Complutense de Madrid.

Bibliography

Cardiel et al. (2021a), MNRAS, https://ui.adsabs.harvard.edu/abs/2021MNRAS.504.3730C/abstract

Cardiel et al. (2021b), MNRAS, https://ui.adsabs.harvard.edu/abs/2021MNRAS.507..318C/abstract

Carrasco et al. (2023), Remote Sensing, in press