EGS master catalogue¶
Preparation of AEGIS data¶
This resource contains the near-infrared catalogue of Extended Groth Strip observations with the WIRC instrument at the Palomar Observatory.: the catalogue comes from dmu0_AEGIS.
In the catalogue, we keep:
- The identifier (it's unique in the catalogue);
- The position;
- The magnitude for each band in 2 arcsecond aperture.
- The auto magnitude to be used as total magnitude.
We don't know when the maps have been observed. We will use the year of the reference paper.
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 = "aegis_ra"
DEC_COL = "aegis_dec"
I - Column selection¶
In [4]:
imported_columns = OrderedDict({
'id': "aegis_id",
'ra': "aegis_ra",
'dec': "aegis_dec",
'kmag_auto': "m_aegis_k",
'kerr2_auto': "merr_aegis_k",
'kmag_d2': "m_ap_aegis_k",
'kerr2_d2': "merr_ap_aegis_k",
#'jmag_auto': "m_aegis_j",
#'jerr2_auto': "merr_aegis_j",
'jmag_d2': "m_ap_aegis_j",
'jerr2_d2': "merr_ap_aegis_j"
})
catalogue = Table.read("../../dmu0/dmu0_AEGIS/data/EGS_Palomar_20160804.fits")[list(imported_columns)]
for column in imported_columns:
catalogue[column].name = imported_columns[column]
epoch = 2011
# Clean table metadata
catalogue.meta = None
In [5]:
#AB correction
#Values from http://svo2.cab.inta-csic.es/svo/theory/fps/index.php?id=P200/WIRC.Ks&&mode=browse&gname=P200&gname2=WIRC#filter
j_vega_to_ab = -2.5 * np.log10 (1564.9/3631) # = +0.85 (WIRC.J_ori)
ks_vega_to_ab = -2.5 * np.log10 (676.92/3631) # = +1.82 (WIRC.Ks) #The K band in AEGIS is Ks
In [6]:
# Adding flux and band-flag columns
for col in catalogue.colnames:
if col.startswith('m_'):
errcol = "merr{}".format(col[1:])
catalogue[errcol].unit = u.mag
# Some object have a magnitude to 0, we suppose this means missing value
catalogue[col][catalogue[col] <= 0] = np.nan
catalogue[errcol][catalogue[errcol] <= 0] = np.nan
#TODO convert Vega to AB
if col.endswith('j') :
catalogue[col] += j_vega_to_ab
catalogue[col].unit = u.mag
if col.endswith('k') :
catalogue[col] += ks_vega_to_ab
catalogue[col].unit = u.mag
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:])))
#Add nans for total J which is absent
catalogue.add_column(Column(np.full(len(catalogue), np.nan), name="m_aegis_j"))
catalogue.add_column(Column(np.full(len(catalogue), np.nan), name="merr_aegis_j"))
catalogue.add_column(Column(np.full(len(catalogue), np.nan), name="f_aegis_j"))
catalogue.add_column(Column(np.full(len(catalogue), np.nan), name="ferr_aegis_j"))
catalogue.add_column(Column(np.zeros(len(catalogue), dtype=bool), name="flag_aegis_j"))
# TODO: Set to True the flag columns for fluxes that should not be used for SED fitting.
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_ap_aegis_j', 'merr_ap_aegis_k']
FLAG_NAME = 'aegis_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_EGS.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 = "aegis_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 [16]:
catalogue.write("{}/AEGIS.fits".format(OUT_DIR), overwrite=True)