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))
plt.style.use('ggplot')
import locale
locale.setlocale(locale.LC_ALL, 'en_GB')
import os
import time
import itertools
from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn3, venn2
from herschelhelp_internal.masterlist import nb_compare_mags, nb_ccplots, nb_histograms, find_last_ml_suffix, quick_checks
In [3]:
OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "20171016"
master_catalogue_filename = "master_catalogue_akari-sep_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))
print("Diagnostics done using: {}".format(master_catalogue_filename))
In [4]:
# 0 - Quick checks
In [5]:
quick_checks(master_catalogue).show_in_notebook()
Out[5]:
I - Summary of wavelength domains¶
In [6]:
flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']
In [7]:
venn3(
[
np.sum(flag_obs == 4),
np.sum(flag_obs == 2),
np.sum(flag_obs == 6),
np.sum(flag_obs == 1),
np.sum(flag_obs == 5),
np.sum(flag_obs == 3),
np.sum(flag_obs == 7)
],
set_labels=('Optical', 'near-IR', 'mid-IR'),
subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");
In [8]:
venn3(
[
np.sum(flag_det[flag_obs == 7] == 4),
np.sum(flag_det[flag_obs == 7] == 2),
np.sum(flag_det[flag_obs == 7] == 6),
np.sum(flag_det[flag_obs == 7] == 1),
np.sum(flag_det[flag_obs == 7] == 5),
np.sum(flag_det[flag_obs == 7] == 3),
np.sum(flag_det[flag_obs == 7] == 7)
],
set_labels=('Optical', 'near-IR', 'mid-IR'),
subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
"(among {} sources)".format(
locale.format('%d', np.sum(flag_det != 0), grouping=True),
locale.format('%d', len(flag_det), grouping=True)));
II - Comparing magnitudes in similar filters¶
Om AKARI-SEP there are no bands with multiple observations. It is still instructive to plot magnitude histograms to give a measure of depth.
In [9]:
u_bands = []
g_bands = ["DECam g"]
r_bands = ["DECam r"]
i_bands = ["DECam i"]
z_bands = ["DECam z"]
y_bands = ["DECam y"]
j_bands = [ "VISTA j"]
h_bands = [ "VISTA h"]
k_bands = [ "VISTA k"]
II.a - Comparing depths¶
We compare the histograms of the total aperture magnitudes of similar bands. This revealed that there were no VISTA y band measurements in VHS so we removed that column.
In [10]:
for bands in [g_bands, r_bands, i_bands, z_bands, y_bands, j_bands, h_bands, k_bands]:
colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
nb_histograms(master_catalogue, colnames, bands)
III - Comparing magnitudes to reference bands¶
Cross-match the master list to 2MASS to compare its magnitudes to 2MASS ones.
In [11]:
master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])
III.b - Comparing J and K bands to 2MASS¶
The catalogue is cross-matched to 2MASS-PSC withing 0.2 arcsecond. We compare the UKIDSS total J and K magnitudes to those from 2MASS.
The 2MASS magnitudes are “Vega-like” and we have to convert them to AB magnitudes using the zero points provided on this page:
| Band | Fν - 0 mag (Jy) |
|---|---|
| J | 1594 |
| H | 1024 |
| Ks | 666.7 |
In addition, UKIDSS uses a K band whereas 2MASS uses a Ks (“short”) band, this page give a correction to convert the K band in a Ks band with the formula:
$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$In [12]:
# The AB zero point is 3631 Jy
j_2mass_to_ab = 2.5 * np.log10(3631/1595)
k_2mass_to_ab = 2.5 * np.log10(3631/666.7)
In [13]:
twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_AKARI-SEP.fits")
twomass_coords = SkyCoord(twomass['raj2000'], twomass['dej2000'])
idx, d2d, _ = twomass_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)
twomass = twomass[mask]
ml_twomass_idx = idx[mask]
In [14]:
nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_vista_j'][ml_twomass_idx],
labels=("2MASS J", "VISTA J (total)"))
In [15]:
ukidss_ks_like = master_catalogue['m_vista_k'] + 0.003 + 0.004 * (
master_catalogue['m_vista_j'] - master_catalogue['m_vista_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
labels=("2MASS Ks", "VISTA Ks-like (total)"))
Keeping only sources with good signal to noise ratio¶
From here, we are only comparing sources with a signal to noise ratio above 3, i.e. roughly we a magnitude error below 0.3.
To make it easier, we are setting to NaN in the catalogue the magnitudes associated with an error above 0.3 so we can't use these magnitudes after the next cell.
In [16]:
for error_column in [_ for _ in master_catalogue.colnames if _.startswith('merr_')]:
column = error_column.replace("merr", "m")
keep_mask = np.isfinite(master_catalogue[error_column])
keep_mask[keep_mask] &= master_catalogue[keep_mask][error_column] <= 0.3
master_catalogue[column][~keep_mask] = np.nan
IV - Comparing aperture magnitudes to total ones.¶
In [17]:
nb_ccplots(
master_catalogue['m_vista_k'],
master_catalogue['m_ap_vista_k'] - master_catalogue['m_vista_k'],
"k total magnitude (VISTA)", "k aperture mag - total mag (VISTA)",
master_catalogue["stellarity"],
invert_x=True
)
V - Color-color and magnitude-color plots¶
In [18]:
nb_ccplots(
master_catalogue['m_irac_i1'] - master_catalogue['m_irac_i2'],
master_catalogue['m_vista_j'] - master_catalogue['m_vista_k'],
"irac 1 - irac 2 (SIMES)", "J - K (VISTA)",
master_catalogue["stellarity"]
)
