XMM-LSS master catalogue¶
Preparation of Canada France Hawaii Telescope Lensing Survey (CFHTLenS) data¶
CFHTLenS catalogue: the catalogue comes from dmu0_CFHTLenS.
In the catalogue, we keep:
- The identifier (it's unique in the catalogue);
- The position;
- The stellarity;
- The kron magnitude, there doesn't appear to be aperture magnitudes. This may mean the survey is unusable.
We use the publication year 2012 for the epoch.
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 = "cfhtlens_ra"
DEC_COL = "cfhtlens_dec"
I - Column selection¶
In [4]:
imported_columns = OrderedDict({
'id': "cfhtlens_id",
'ALPHA_J2000': "cfhtlens_ra",
'DELTA_J2000': "cfhtlens_dec",
'CLASS_STAR': "cfhtlens_stellarity",
'MAG_u': "m_cfhtlens_u",
'MAGERR_u': "merr_cfhtlens_u",
'MAG_g': "m_cfhtlens_g",
'MAGERR_g': "merr_cfhtlens_g",
'MAG_r': "m_cfhtlens_r",
'MAGERR_r': "merr_cfhtlens_r",
'MAG_i': "m_cfhtlens_i",
'MAGERR_i': "merr_cfhtlens_i",
'MAG_z': "m_cfhtlens_z",
'MAGERR_z': "merr_cfhtlens_z",
})
catalogue = Table.read("../../dmu0/dmu0_CFHTLenS/data/CFHTLenS_XMM-LSS.fits")[list(imported_columns)]
for column in imported_columns:
catalogue[column].name = imported_columns[column]
epoch = 2012 #Year of publication
# 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
catalogue[col][catalogue[col] > 90.] = np.nan
catalogue[errcol][catalogue[errcol] > 90.] = 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:])))
# We add nan filled aperture photometry for consistency
catalogue.add_column(Column(np.full(len(catalogue), np.nan), name="m_ap{}".format(col[1:])))
catalogue.add_column(Column(np.full(len(catalogue), np.nan), name="merr_ap{}".format(col[1:])))
catalogue.add_column(Column(np.full(len(catalogue), np.nan), name="f_ap{}".format(col[1:])))
catalogue.add_column(Column(np.full(len(catalogue), np.nan), name="f_err{}".format(col[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_cfhtlens_u',
'merr_cfhtlens_g',
'merr_cfhtlens_r',
'merr_cfhtlens_i',
'merr_cfhtlens_z']
FLAG_NAME = 'cfhtlens_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 = "cfhtlens_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("{}/CFHTLENS.fits".format(OUT_DIR), overwrite=True)