XMM-LSS master catalogue¶
Preparation of VIMOS Public Extragalactic Redshift Survey (VIPERS) - Multi Lambda Survey (MLS) data¶
This catalogue comes from dmu0_VIPERS.
In the catalogue, we keep:
- The
identas unique object identifier; - The position which is given for all the sources;
- The ugrizy ks total magnitude.
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 = "vipers_ra"
DEC_COL = "vipers_dec"
I - Column selection¶
In [4]:
imported_columns = OrderedDict({
"ident": "vipers_id",
"ra": "vipers_ra",
"dec": "vipers_dec",
"u": "m_vipers_u",
"err_u": "merr_vipers_u",
"g": "m_vipers_g",
"err_g": "merr_vipers_g",
"r": "m_vipers_r",
"err_r": "merr_vipers_r",
"i": "m_vipers_i",
"err_i": "merr_vipers_i",
"y": "m_vipers_y",
"err_y": "merr_vipers_y",
"z": "m_vipers_z",
"err_z": "merr_vipers_z",
"ks": "m_vipers_ks",
"err_ks": "merr_vipers_ks",
})
catalogue = Table.read("../../dmu0/dmu0_VIPERS-MLS/data/VIPERS-MLS_20160502.fits")[list(imported_columns)]
for column in imported_columns:
catalogue[column].name = imported_columns[column]
epoch = 2012
# 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:])
# -99.0 is used for missing values
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="ferr_ap{}".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_vipers_u',
'merr_vipers_g',
'merr_vipers_r',
'merr_vipers_i',
'merr_vipers_y',
'merr_vipers_z',
'merr_vipers_ks']
FLAG_NAME = 'vipers_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 = "vipers_flag_gaia"
catalogue['flag_gaia'].name = GAIA_FLAG_NAME
print("{} sources flagged.".format(np.sum(catalogue[GAIA_FLAG_NAME] > 0)))
V - Saving to disk¶
In [15]:
catalogue.write("{}/VIPERS.fits".format(OUT_DIR), overwrite=True)