ELAIS-S1 master catalogue¶
Preparation of Spitzer datafusion SERVS data¶
The Spitzer catalogues were produced by the datafusion team are available in dmu0_DataFusion-Spitzer.
Lucia told that the magnitudes are aperture corrected.
In the catalouge, we keep:
- The internal identifier (this one is only in HeDaM data);
- The position;
- The fluxes in aperture 2 (1.9 arcsec);
- The “auto” flux (which seems to be the Kron flux);
- The stellarity in each band
A query of the position in the Spitzer heritage archive show that the SERVS-ELAIS-N1 images were observed in 2009. Let's take this as 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, flux_to_mag
In [3]:
OUT_DIR = os.environ.get('TMP_DIR', "./data_tmp")
try:
os.makedirs(OUT_DIR)
except FileExistsError:
pass
RA_COL = "servs_ra"
DEC_COL = "servs_dec"
I - Column selection¶
In [4]:
imported_columns = OrderedDict({
'internal_id': "servs_intid",
'ra_12': "servs_ra",
'dec_12': "servs_dec",
'flux_aper_2_1': "f_ap_servs_irac1",
'fluxerr_aper_2_1': "ferr_ap_servs_irac1",
'flux_auto_1': "f_servs_irac1",
'fluxerr_auto_1': "ferr_servs_irac1",
'class_star_1': "servs_stellarity_irac1",
'flux_aper_2_2': "f_ap_servs_irac2",
'fluxerr_aper_2_2': "ferr_ap_servs_irac2",
'flux_auto_2': "f_servs_irac2",
'fluxerr_auto_2': "ferr_servs_irac2",
'class_star_2': "servs_stellarity_irac2",
})
catalogue = Table.read("../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SERVS_ELAIS-S1.fits")[list(imported_columns)]
for column in imported_columns:
catalogue[column].name = imported_columns[column]
epoch = 2009
# Clean table metadata
catalogue.meta = None
In [5]:
# Adding magnitude and band-flag columns
for col in catalogue.colnames:
if col.startswith('f_'):
errcol = "ferr{}".format(col[1:])
magnitude, error = flux_to_mag(
np.array(catalogue[col])/1.e6, np.array(catalogue[errcol])/1.e6)
# Note that some fluxes are 0.
catalogue.add_column(Column(magnitude, 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:])))
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 = ['ferr_ap_servs_irac1', 'ferr_ap_servs_irac2']
FLAG_NAME = "servs_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_ELAIS-S1.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 = "servs_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("{}/SERVS.fits".format(OUT_DIR), overwrite=True)