rgbloom 1.9

RGB from Gaia EDR3

rgbloom

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

The code presented here is an updated version of rgblues, which originally provided RGB magnitudes from Gaia EDR3 photometric data, as explained in Cardiel et al. (2021b).

The RGB magnitudes provided by Carrasco et al. (2023) are considered more reliable as they are directly computed from the source spectrum, without the need for any approximate calibration or constraints on source colour or extinction. Moreover, the number of sources with RGB estimates has significantly increased from approximately 15 million to around 200 million objects (referred to as the 200M sample). However, it is important to note that the sky coverage of the 200M sample is still limited in some high Galactic latitudes. For this reason, 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 can be useful for users requiring calibrated RGB sources at those particular sky regions.

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

Please note that a live internet connection is required for the code to function properly.

Installing the code

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

Creating and activating the Python virtual environment

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

Installing the package

We recommend installing the latest stable version, which is available via the PyPI respository:

(venv_rgb) $ pip install rgbloom

The latest development version is available through GitHub:

(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. For example

(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.

Whenever the code is executed, it will download certain auxiliary files to your computer if they haven't been downloaded in a previous run. These files are stored in a cache directory, and the location of this directory will be displayed in the terminal output. You don't need to be concerned about the specific location unless you want to delete these files to free up 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 1.9
        ==============================

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 not 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: In this plot (see PDF file) the object symbol size is scaled based on the Gaia G magnitude, and are color coded based on the Gaia G_BP - G_RP colour. Objects brighter than a predefined threshold are represented by larger star symbols. To aid in object identification, the consecutive identification numbers from the two files rgbloom_200m.csv and rgbloom_no200m.csv, are displayed in red and black, respectively. As these files are sorted by right ascension, the identification numbers increase sequentially on the chart.

  In the case of less reliable sources in rgbloom_20m.csv (where qlflag=1), the corresponding identification numbers are enclosed within a rectangle with a light-gray border. It is worth noting that when the --nonumbers parameter is used in the command line, the identification numbers will not be displayed.

  Starting from version 1.5, it is now possible to label each object with its magnitude instead of the objetc number in the CSV files. This can be achieved using the --display_mag <magname> option, where <magname> can be any of the following: RGB_B, RGB_R, RGB_R, Gaia_G, Gaia_BP, Gaia_RP, Gaia_BP-RP. When this option is used, the displayed RGB magnitudes for objects outside the 200M sample correspond to the estimates calculated using the polynomial calibrations derived by Cardiel et al. (2021b), and are shown between parenthesis.

  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 files, consisting of one PDF file and two CSV files, share the same root name, which is by default rgbloom. However, you can easily modify this by using the optional argument --basename <newbasename> in the command line. This allows you to specify a new base name for the output files according to your preference.

Additional help

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

$ rgbloom --help

usage: rgbloom [-h] [--basename BASENAME] [--brightlimit BRIGHTLIMIT]
               [--symbsize SYMBSIZE] [--max_symbsize MAX_SYMBSIZE]
               [--min_symbsize MIN_SYMBSIZE] [--mag_power MAG_POWER]
               [--display_mag {None,RGB_B,RGB_G,RGB_R,Gaia_G,Gaia_BP,Gaia_RP,Gaia_BP_RP}]
               [--num_fontsize NUM_FONTSIZE] [--nonumbers] [--noplot]
               [--nocolor] [--verbose]
               ra_center dec_center search_radius g_limit

RGB predictions from Gaia DR3 spectrophotometry (version 1.6)

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   global multiplying factor for symbol size
                        (default=1.0)
  --max_symbsize MAX_SYMBSIZE
                        maximum symbol size in chart (default=1000)
  --min_symbsize MIN_SYMBSIZE
                        minimum symbol size in chart (default=10)
  --mag_power MAG_POWER
                        power to scale symbol sizes in chart (default=3)
  --display_mag {None,RGB_B,RGB_G,RGB_R,Gaia_G,Gaia_BP,Gaia_RP,Gaia_BP_RP}
                        display selected magnitude instead of object number
  --num_fontsize NUM_FONTSIZE
                        font size for numbers in chart (default=5)
  --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, https://www.mdpi.com/2072-4292/15/7/1767