RGB from Gaia EDR3
Project description
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 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:
(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 1.5
==============================
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
andphot_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 DR3ra
: right ascension (from Gaia DR3)dec
: declination (from Gaia DR3)RGB_B
: blue RGB magnitude estimateRGB_G
: green RGB magnitude estimateRGB_R
: red RGB magnitude estimateerrRGB_B
: uncertainty in the blue RGB magnitude estimateerrRGB_G
: uncertainty in the green RGB magnitude estimateerrRGB_R
: uncertainty in the red RGB magnitude estimateobjtype
: type of source, according to the classification provided by Gaia DR3 (see description ofGAIA_SOURCE
table for details):1
: object flagged asNON_SINGLE_STAR
2
: object flagged asIN_QSO_CANDIDATES
3
: object flagged asIN_GALAXY_CANDIDATES
0
: none of the above
qlflag
: global quality flag:0
: reliable source1
: 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 DR3ra
: 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 estimateRGB_G
: green RGB magnitude estimateRGB_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 filesrgbloom_200m.csv
andrgbloom_no200m.csv
are also displayed, in red and black, respectively. The identification numbers corresponding to the less reliable sources inrgbloom_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.
Since version 1.5, it is also possible to display a particular magnitude
(instead of the objetc number in the CSV files) using --display_mag <magname>
, where <magname>
can be any of the following: RGB_B
, RGB_R
,
RGB_R
, Gaia_G
, Gaia_BP
, Gaia_RP
, Gaia_BP-RP
.
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 1.5)
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
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