COSMOS master catalogue¶
Preparation of HST CANDELS-3D data¶
The catalogue comes from dmu0_CANDELS-3D-HST.
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.
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, flux_to_mag
In [3]:
OUT_DIR = os.environ.get('TMP_DIR', "./data_tmp")
try:
os.makedirs(OUT_DIR)
except FileExistsError:
pass
RA_COL = "candels_ra"
DEC_COL = "candels_dec"
I - Column selection¶
In [4]:
imported_columns = OrderedDict({
'ID': "candels_id",
'RAJ2000': "candels_ra",
'DEJ2000': "candels_dec",
'S/G': "candels_stellarity",
'F140Wap': "f_ap_candels_f140w",
'e_F140Wap': "ferr_ap_candels_f140w",
'F140W': "f_candels_f140w",
'e_F140W': "ferr_candels_f140w",
'F160Wap': "f_ap_candels_f160w",
'e_F160Wap': "ferr_ap_candels_f160w",
'F160W': "f_candels_f160w",
'e_F160W': "ferr_candels_f160w",
'F606W': "f_candels_f606w",
'e_F606W': "ferr_candels_f606w",
'F814W': "f_candels_f814w",
'e_F814W': "ferr_candels_f814w",
'F125W': "f_candels_f125w",
'e_F125W': "ferr_candels_f125w"
})
catalogue = Table.read("../../dmu0/dmu0_CANDELS-3D-HST/data/CANDELS-3D-HST_COSMOS.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('f_'):
errcol = "ferr{}".format(col[1:])
catalogue[col] = catalogue[col] * 0.3631e-6
catalogue[col].unit = u.microjansky
catalogue[errcol] = catalogue[errcol] * 0.3631e-6
catalogue[errcol].unit = u.microjansky
mag, error = flux_to_mag(np.array(catalogue[col]) * 1.e-6 , np.array(catalogue[errcol] ) * 1.e-6)
# Fluxes are added in µJy
catalogue.add_column(Column(mag, name="m{}".format(col[1:])))
catalogue.add_column(Column(error, name="m{}".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['candels_stellarity'] = catalogue['candels_stellarity'].astype(float)
catalogue['candels_stellarity'][np.isclose(catalogue['candels_stellarity'], 1.)] = 0.9
catalogue['candels_stellarity'][np.isclose(catalogue['candels_stellarity'], 0.)] = 0.05
catalogue['candels_stellarity'][np.isclose(catalogue['candels_stellarity'], 2.)] = np.nan
In [7]:
catalogue[:10].show_in_notebook()
Out[7]:
II - Removal of duplicated sources¶
We remove duplicated objects from the input catalogues.
In [8]:
SORT_COLS = ['merr_candels_f140w',
'merr_candels_f160w',
'merr_candels_f606w',
'merr_candels_f814w',
'merr_candels_f125w']
FLAG_NAME = 'candels_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 [9]:
gaia = Table.read("../../dmu0/dmu0_GAIA/data/GAIA_COSMOS.fits")
gaia_coords = SkyCoord(gaia['ra'], gaia['dec'])
In [10]:
nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL],
gaia_coords.ra, gaia_coords.dec)
In [11]:
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 [12]:
catalogue[RA_COL] += delta_ra.to(u.deg)
catalogue[DEC_COL] += delta_dec.to(u.deg)
In [13]:
nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL],
gaia_coords.ra, gaia_coords.dec)
IV - Flagging Gaia objects¶
In [14]:
catalogue.add_column(
gaia_flag_column(SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]), epoch, gaia)
)
In [15]:
GAIA_FLAG_NAME = "candels_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 [16]:
catalogue.write("{}/CANDELS.fits".format(OUT_DIR), overwrite=True)