AKARI-NEP master catalogue¶
Preparation of AKARI-NEP-OptNIR data¶
This product contains the 8-band (u*, g', r', i', z', Y, J, Ks) optical to near-infrared catalogue from Oi et al., 2014.
- The identifier (it's unique in the catalogue);
- The position;
- The stellarity;
- The total magnitude for each band (u*, g', r', i', z', Y, J, Ks).
- There are no aperture mags so we put empty columns in the masterlist.
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 = "akari_ra"
DEC_COL = "akari_dec"
I - Column selection¶
In [4]:
imported_columns = OrderedDict({
'OBJID': "akari_id",
'RAJ2000': "akari_ra",
'DEJ2000': "akari_dec",
'stl': "akari_stellarity",
'umag': "m_megacam_u",
'e_umag': "merr_megacam_u",
'gmag': "m_megacam_g",
'e_gmag': "merr_megacam_g",
'rmag': "m_megacam_r",
'e_rmag': "merr_megacam_r",
'imag': "m_megacam_i",
'e_imag': "merr_megacam_i",
'zmag': "m_megacam_z",
'e_zmag': "merr_megacam_z",
'Ymag': "m_wircam_y",
'e_Ymag': "merr_wircam_y",
'Jmag': "m_wircam_j",
'e_Jmag': "merr_wircam_j",
'Kmag': "m_wircam_k",
'e_Kmag': "merr_wircam_k"
})
catalogue = Table.read("../../dmu0/dmu0_AKARI-NEP-OptNIR/data/AKARI-NEP_OptNIR.fits")[list(imported_columns)]
for column in imported_columns:
catalogue[column].name = imported_columns[column]
epoch = 2014 #This is the paper year. The observations are multi-epoch
# 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:])
# Bad values = -99
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_megacam_u', 'merr_megacam_g', 'merr_megacam_r', 'merr_megacam_i', 'merr_megacam_z',
'merr_wircam_y', 'merr_wircam_j', 'merr_wircam_k']
FLAG_NAME = 'akari_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_AKARI-NEP.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 = "akari_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("{}/AKARI.fits".format(OUT_DIR), overwrite=True)