Bootes master catalogue¶
Preparation of IBIS data¶
The catalogue comes from dmu0_IBIS.
In the catalogue, we keep:
- The identifier (it's unique in the catalogue);
- The position;
- The stellarity;
- The magnitude for each band in apertude 4 ($1.2 * \sqrt{2}$ arcsec = 1.7 arcsec).
- The kron magnitude to be used as total magnitude (no “auto” magnitude is provided).
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 = "ibis_ra"
DEC_COL = "ibis_dec"
In [4]:
#TODO get these (non-zero) values from Catalogue header
j_ap_to_tot = 0.
h_ap_to_tot = 0.
k_ap_to_tot = 0.
newfirm_ap_to_tot = {
'j': j_ap_to_tot,
'h': h_ap_to_tot,
'k': k_ap_to_tot
}
1.ii - Vega-like to AB¶
In [5]:
## Vega to AB
# http://svo2.cab.inta-csic.es/svo/theory/fps3/index.php?mode=browse&gname=NOAO
j_vega_to_ab = -2.5*np.log10(1559.06 / 3631.)
h_vega_to_ab = -2.5*np.log10(1044.7 / 3631.)
k_vega_to_ab = -2.5*np.log10( 677.1/ 3631.)
#Vega correction - the magnitudes are nto in true Vega and have to be converted first
#Magnitudes also need a minor correction to turn them into Vega
# http://r2.sdm.noao.edu/nsa/NEWFIRM_NDWFS.html
j_newfirm_to_vega = -0.056
h_newfirm_to_vega = -0.007
k_newfirm_to_vega = 0.
newfirm_to_ab = {
'j': j_newfirm_to_vega + j_vega_to_ab,
'h': h_newfirm_to_vega + h_vega_to_ab,
'k': k_newfirm_to_vega + k_vega_to_ab,
}
In [6]:
imported_columns = OrderedDict({
'internal_id': "ibis_id",
'alpha_j2000': "ibis_ra",
'delta_j2000': "ibis_dec",
'k_class_star': "ibis_stellarity",
'j_mag_auto': "m_newfirm_j",
'j_magerr_auto': "merr_newfirm_j",
'j_mag_aper_3': "m_ap_newfirm_j",
'j_magerr_aper_3': "merr_ap_newfirm_j",
'h_mag_auto': "m_newfirm_h",
'h_magerr_auto': "merr_newfirm_h",
'h_mag_aper_3': "m_ap_newfirm_h",
'h_magerr_aper_3': "merr_ap_newfirm_h",
'k_mag_auto': "m_newfirm_k",
'k_magerr_auto': "merr_newfirm_k",
'k_mag_aper_3': "m_ap_newfirm_k",
'k_magerr_aper_3': "merr_ap_newfirm_k"
})
catalogue = Table.read("../../dmu0/dmu0_IBIS/data/IBIS_MLselected_20160801.fits")[list(imported_columns)]
for column in imported_columns:
catalogue[column].name = imported_columns[column]
epoch = 2011
# Clean table metadata
catalogue.meta = None
In [7]:
# Adding flux and band-flag columns
for col in catalogue.colnames:
if col.startswith('m_'):
errcol = "merr{}".format(col[1:])
# Some object have a magnitude to 0, we suppose this means missing value
mask = (catalogue[col] <= 0.) | (catalogue[col] > 90. )
catalogue[col][mask] = np.nan
#Vega to AB
catalogue[col] = catalogue[col] + newfirm_to_ab[col[-1]]
#Aperture correction
if "ap" in col:
catalogue[col] = catalogue[col] + newfirm_ap_to_tot[col[-1]]
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:])))
# TODO: Set to True the flag columns for fluxes that should not be used for SED fitting.
In [8]:
catalogue[:10].show_in_notebook()
Out[8]:
II - Removal of duplicated sources¶
We remove duplicated objects from the input catalogues.
In [9]:
SORT_COLS = ['merr_ap_newfirm_j', 'merr_ap_newfirm_h', 'merr_ap_newfirm_k']
FLAG_NAME = 'ibis_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 [10]:
gaia = Table.read("../../dmu0/dmu0_GAIA/data/GAIA_Bootes.fits")
gaia_coords = SkyCoord(gaia['ra'], gaia['dec'])
In [11]:
nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL],
gaia_coords.ra, gaia_coords.dec)
In [12]:
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 [13]:
catalogue[RA_COL] += delta_ra.to(u.deg)
catalogue[DEC_COL] += delta_dec.to(u.deg)
In [14]:
nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL],
gaia_coords.ra, gaia_coords.dec)
IV - Flagging Gaia objects¶
In [15]:
catalogue.add_column(
gaia_flag_column(SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]), epoch, gaia)
)
In [16]:
GAIA_FLAG_NAME = "ibis_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 [17]:
catalogue.write("{}/IBIS.fits".format(OUT_DIR), overwrite=True)