XMM-LSS master catalogue¶
Preparation of Canada France Hawaii Telescope Legacy Survey (CFHTLS) deep data¶
CFHTLS has both a wide area across XMM-LSS and a smaller deep field. We will process each independently and add them both to the master catalogue, taking the deep photometry where both are available.
The catalogue is in dmu0_CFHTLS.
In the catalogue, we keep:
- The position;
- The stellarity (g band stellarity);
- The aperture magnitude (3 arcsec).
- The total magnitude (Kron like aperture magnitude).
We use the 2007 release, which we take as the date.
In [1]:
from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))
In [2]:
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
from collections import OrderedDict
import os
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy.table import Column, Table
import numpy as np
from herschelhelp_internal.flagging import gaia_flag_column
from herschelhelp_internal.masterlist import nb_astcor_diag_plot, remove_duplicates
from herschelhelp_internal.utils import astrometric_correction, mag_to_flux
In [3]:
OUT_DIR = os.environ.get('TMP_DIR', "./data_tmp")
try:
os.makedirs(OUT_DIR)
except FileExistsError:
pass
RA_COL = "cfhtls-deep_ra"
DEC_COL = "cfhtls-deep_dec"
I - Column selection¶
In [4]:
imported_columns = OrderedDict({
'cfhtls': "cfhtls-deep_id",
'raj2000': "cfhtls-deep_ra",
'dej2000': "cfhtls-deep_dec",
'gcl': "cfhtls-deep_stellarity",
'umaga': "m_cfhtls-deep_u",
'e_umaga': "merr_cfhtls-deep_u",
'gmaga': "m_cfhtls-deep_g",
'e_gmaga': "merr_cfhtls-deep_g",
'rmaga': "m_cfhtls-deep_r",
'e_rmaga': "merr_cfhtls-deep_r",
'imaga': "m_cfhtls-deep_i",
'e_imaga': "merr_cfhtls-deep_i",
'zmaga': "m_cfhtls-deep_z",
'e_zmaga': "merr_cfhtls-deep_z",
'ymaga': "m_cfhtls-deep_y",
'e_ymaga': "merr_cfhtls-deep_y",
'umag': "m_ap_cfhtls-deep_u",
'e_umag': "merr_ap_cfhtls-deep_u",
'gmag': "m_ap_cfhtls-deep_g",
'e_gmag': "merr_ap_cfhtls-deep_g",
'rmag': "m_ap_cfhtls-deep_r",
'e_rmag': "merr_ap_cfhtls-deep_r",
'imag': "m_ap_cfhtls-deep_i",
'e_imag': "merr_ap_cfhtls-deep_i",
'zmag': "m_ap_cfhtls-deep_z",
'e_zmag': "merr_ap_cfhtls-deep_z",
'ymag': "m_ap_cfhtls-deep_y",
'e_ymag': "merr_ap_cfhtls-deep_y"
})
catalogue = Table.read("../../dmu0/dmu0_CFHTLS/data/CFHTLS-DEEP_XMM-LSS.fits")[list(imported_columns)]
for column in imported_columns:
catalogue[column].name = imported_columns[column]
epoch = 2007
# Clean table metadata
catalogue.meta = None
In [5]:
# Adding flux and band-flag columns
for col in catalogue.colnames:
if col.startswith('m_'):
errcol = "merr{}".format(col[1:])
#catalogue[col][catalogue[col] <= 0] = np.nan
#catalogue[errcol][catalogue[errcol] <= 0] = np.nan
flux, error = mag_to_flux(np.array(catalogue[col]), np.array(catalogue[errcol]))
# Fluxes are added in µJy
catalogue.add_column(Column(flux * 1.e6, name="f{}".format(col[1:])))
catalogue.add_column(Column(error * 1.e6, name="f{}".format(errcol[1:])))
# Band-flag column
if "ap" not in col:
catalogue.add_column(Column(np.zeros(len(catalogue), dtype=bool), name="flag{}".format(col[1:])))
# TODO: Set to True the flag columns for fluxes that should not be used for SED fitting.
In [6]:
catalogue[:10].show_in_notebook()
Out[6]:
II - Removal of duplicated sources¶
We remove duplicated objects from the input catalogues.
In [7]:
SORT_COLS = ['merr_ap_cfhtls-deep_u',
'merr_ap_cfhtls-deep_g',
'merr_ap_cfhtls-deep_r',
'merr_ap_cfhtls-deep_i',
'merr_ap_cfhtls-deep_z',]
FLAG_NAME = 'cfhtls-deep_flag_cleaned'
nb_orig_sources = len(catalogue)
catalogue = remove_duplicates(catalogue, RA_COL, DEC_COL, sort_col=SORT_COLS,flag_name=FLAG_NAME)
nb_sources = len(catalogue)
print("The initial catalogue had {} sources.".format(nb_orig_sources))
print("The cleaned catalogue has {} sources ({} removed).".format(nb_sources, nb_orig_sources - nb_sources))
print("The cleaned catalogue has {} sources flagged as having been cleaned".format(np.sum(catalogue[FLAG_NAME])))
III - Astrometry correction¶
We match the astrometry to the Gaia one. We limit the Gaia catalogue to sources with a g band flux between the 30th and the 70th percentile. Some quick tests show that this give the lower dispersion in the results.
In [8]:
gaia = Table.read("../../dmu0/dmu0_GAIA/data/GAIA_XMM-LSS.fits")
gaia_coords = SkyCoord(gaia['ra'], gaia['dec'])
In [9]:
nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL],
gaia_coords.ra, gaia_coords.dec)
In [10]:
delta_ra, delta_dec = astrometric_correction(
SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]),
gaia_coords
)
print("RA correction: {}".format(delta_ra))
print("Dec correction: {}".format(delta_dec))
In [11]:
catalogue[RA_COL] += delta_ra.to(u.deg)
catalogue[DEC_COL] += delta_dec.to(u.deg)
In [12]:
nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL],
gaia_coords.ra, gaia_coords.dec)
IV - Flagging Gaia objects¶
In [13]:
catalogue.add_column(
gaia_flag_column(SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]), epoch, gaia)
)
In [14]:
GAIA_FLAG_NAME = "cfhtls-deep_flag_gaia"
catalogue['flag_gaia'].name = GAIA_FLAG_NAME
print("{} sources flagged.".format(np.sum(catalogue[GAIA_FLAG_NAME] > 0)))
V - Flagging objects near bright stars¶
VI - Saving to disk¶
In [15]:
catalogue.write("{}/CFHTLS-DEEP.fits".format(OUT_DIR), overwrite=True)