ELAIS-N1 master catalogue

Checks and diagnostics

from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))

This notebook was run with herschelhelp_internal version: 
255270d (Fri Nov 24 10:35:51 2017 +0000)

%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

from herschelhelp_internal.masterlist import (nb_compare_mags, nb_ccplots, nb_histograms, find_last_ml_suffix,
                                              quick_checks)

OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "20171016"

master_catalogue_filename = "master_catalogue_elais-n1_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))

print("Diagnostics done using: {}".format(master_catalogue_filename))

Diagnostics done using: master_catalogue_elais-n1_20171016.fits

0 - Quick checks

quick_checks(master_catalogue)

The column f_ap_suprime_g contains 134 zero or negative values!it's minimum is 0.0.
The column f_suprime_g contains 274 zero or negative values!it's minimum is 0.0.
The column f_ap_suprime_r contains 363 zero or negative values!it's minimum is 0.0.
The column f_suprime_r contains 567 zero or negative values!it's minimum is 0.0.
The column f_ap_suprime_i contains 4 zero or negative values!it's minimum is 0.0.
The column f_suprime_i contains 12 zero or negative values!it's minimum is 0.0.
The column f_suprime_z contains 140 zero or negative values!it's minimum is 0.0.
The column f_ap_suprime_y contains 69 zero or negative values!it's minimum is 0.0.
The column f_suprime_y contains 173 zero or negative values!it's minimum is 0.0.
The column f_ap_suprime_n921 contains 371 zero or negative values!it's minimum is 0.0.
The column f_suprime_n921 contains 739 zero or negative values!it's minimum is 0.0.
The column merr_gpc1_r contains 1 zero or negative values!it's minimum is 0.0.
The column merr_ap_gpc1_y contains 1 zero or negative values!it's minimum is 0.0.
The column f_ap_irac_i1 contains 861 zero or negative values!it's minimum is -263238.122402173.
The column merr_ap_irac_i1 contains 861 zero or negative values!it's minimum is -12638.354838083895.
The column f_irac_i1 contains 1740 zero or negative values!it's minimum is -470521.088058024.
The column merr_irac_i1 contains 1740 zero or negative values!it's minimum is -7343.653061571807.
The column f_ap_irac_i2 contains 1266 zero or negative values!it's minimum is -7.57307525969056.
The column merr_ap_irac_i2 contains 1266 zero or negative values!it's minimum is -2160.391951305108.
The column f_irac_i2 contains 1373 zero or negative values!it's minimum is -127.657072585999.
The column merr_irac_i2 contains 1373 zero or negative values!it's minimum is -5513.2513309879905.

I - Summary of wavelength domains

flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']

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");

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=('mid-IR', 'near-IR', 'Optical'),
    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

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

u_bands = ["WFC u", "Megacam u"]
g_bands = ["WFC g", "Megacam g", "SUPRIME g", "GPC1 g"]
r_bands = ["WFC r", "Megacam r", "SUPRIME r", "GPC1 r"]
i_bands = ["WFC i", "SUPRIME i", "GPC1 i"]
z_bands = ["WFC z", "Megacam z", "SUPRIME z", "GPC1 z"]
y_bands = ["SUPRIME y", "GPC1 y"]

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))

Megacam u (aperture) - WFC u (aperture):
- Median: 0.10
- Median Absolute Deviation: 0.24
- 1% percentile: -0.7272730255126953
- 99% percentile: 2.3119604682922468

Megacam u (total) - WFC u (total):
- Median: -0.15
- Median Absolute Deviation: 0.21
- 1% percentile: -1.0918270874023437
- 99% percentile: 2.1320125770568796

Megacam g (aperture) - WFC g (aperture):
- Median: 0.14
- Median Absolute Deviation: 0.15
- 1% percentile: -0.5219296264648438
- 99% percentile: 1.085785179138184

Megacam g (total) - WFC g (total):
- Median: -0.02
- Median Absolute Deviation: 0.15
- 1% percentile: -0.9512448883056641
- 99% percentile: 1.8764633178711005

SUPRIME g (aperture) - WFC g (aperture):
- Median: 0.16
- Median Absolute Deviation: 0.16
- 1% percentile: -0.5483207702636718
- 99% percentile: 2.319765567779542

SUPRIME g (total) - WFC g (total):
- Median: 0.01
- Median Absolute Deviation: 0.19
- 1% percentile: -0.9150347518920898
- 99% percentile: 2.3162221145629904

GPC1 g (aperture) - WFC g (aperture):
- Median: -0.03
- Median Absolute Deviation: 0.25
- 1% percentile: -1.4681995010375974
- 99% percentile: 2.598815269470216

GPC1 g (total) - WFC g (total):
- Median: 0.06
- Median Absolute Deviation: 0.15
- 1% percentile: -1.235805778503418
- 99% percentile: 0.8852024841308583

SUPRIME g (aperture) - Megacam g (aperture):
- Median: -0.03
- Median Absolute Deviation: 0.11
- 1% percentile: -0.6601028442382812
- 99% percentile: 2.253218078613278

SUPRIME g (total) - Megacam g (total):
- Median: -0.08
- Median Absolute Deviation: 0.20
- 1% percentile: -1.6540432739257813
- 99% percentile: 2.5875380706787094

GPC1 g (aperture) - Megacam g (aperture):
- Median: -0.19
- Median Absolute Deviation: 0.35
- 1% percentile: -2.7744883155822753
- 99% percentile: 2.5329534912109364

GPC1 g (total) - Megacam g (total):
- Median: 0.08
- Median Absolute Deviation: 0.15
- 1% percentile: -3.053208236694336
- 99% percentile: 0.909821548461915

GPC1 g (aperture) - SUPRIME g (aperture):
- Median: -0.25
- Median Absolute Deviation: 0.35
- 1% percentile: -2.450922222137451
- 99% percentile: 2.5137275123596465

GPC1 g (total) - SUPRIME g (total):
- Median: 0.01
- Median Absolute Deviation: 0.17
- 1% percentile: -1.6039471626281738
- 99% percentile: 0.8286709785461426

Megacam r (aperture) - WFC r (aperture):
- Median: 0.12
- Median Absolute Deviation: 0.15
- 1% percentile: -0.5586348533630371
- 99% percentile: 1.2267741394042968

Megacam r (total) - WFC r (total):
- Median: -0.07
- Median Absolute Deviation: 0.13
- 1% percentile: -0.9979226684570313
- 99% percentile: 1.865820007324213

SUPRIME r (aperture) - WFC r (aperture):
- Median: 0.09
- Median Absolute Deviation: 0.15
- 1% percentile: -0.6121678924560547
- 99% percentile: 3.518004989624025

SUPRIME r (total) - WFC r (total):
- Median: -0.05
- Median Absolute Deviation: 0.16
- 1% percentile: -0.9259447097778319
- 99% percentile: 3.3509555816649943

GPC1 r (aperture) - WFC r (aperture):
- Median: -0.05
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0388346672058106
- 99% percentile: 1.5338702201843275

GPC1 r (total) - WFC r (total):
- Median: 0.03
- Median Absolute Deviation: 0.12
- 1% percentile: -0.9682998657226562
- 99% percentile: 0.8239599800109849

SUPRIME r (aperture) - Megacam r (aperture):
- Median: -0.04
- Median Absolute Deviation: 0.11
- 1% percentile: -0.6416191864013672
- 99% percentile: 2.4968240737914904

SUPRIME r (total) - Megacam r (total):
- Median: -0.05
- Median Absolute Deviation: 0.20
- 1% percentile: -1.462252655029297
- 99% percentile: 2.9655048751831057

GPC1 r (aperture) - Megacam r (aperture):
- Median: -0.16
- Median Absolute Deviation: 0.24
- 1% percentile: -2.5137132263183597
- 99% percentile: 1.4576556015014677

GPC1 r (total) - Megacam r (total):
- Median: 0.11
- Median Absolute Deviation: 0.11
- 1% percentile: -2.6895561695098875
- 99% percentile: 0.8498268508911129

GPC1 r (aperture) - SUPRIME r (aperture):
- Median: -0.16
- Median Absolute Deviation: 0.23
- 1% percentile: -2.961479415893555
- 99% percentile: 1.4932370567321622

GPC1 r (total) - SUPRIME r (total):
- Median: 0.06
- Median Absolute Deviation: 0.13
- 1% percentile: -2.829697322845459
- 99% percentile: 0.7763437843322756

SUPRIME i (aperture) - WFC i (aperture):
- Median: 0.07
- Median Absolute Deviation: 0.15
- 1% percentile: -0.7344942092895508
- 99% percentile: 1.4884119033813477

SUPRIME i (total) - WFC i (total):
- Median: -0.13
- Median Absolute Deviation: 0.17
- 1% percentile: -1.0118545150756835
- 99% percentile: 1.584526290893563

GPC1 i (aperture) - WFC i (aperture):
- Median: -0.01
- Median Absolute Deviation: 0.11
- 1% percentile: -0.7514700889587402
- 99% percentile: 0.7729697227478038

GPC1 i (total) - WFC i (total):
- Median: 0.06
- Median Absolute Deviation: 0.11
- 1% percentile: -0.8002008056640624
- 99% percentile: 0.8199679565429681

GPC1 i (aperture) - SUPRIME i (aperture):
- Median: -0.09
- Median Absolute Deviation: 0.19
- 1% percentile: -1.259005832672119
- 99% percentile: 0.6624520874023434

GPC1 i (total) - SUPRIME i (total):
- Median: 0.18
- Median Absolute Deviation: 0.10
- 1% percentile: -1.0342599487304687
- 99% percentile: 0.6791699981689452

Megacam z (aperture) - WFC z (aperture):
- Median: 0.13
- Median Absolute Deviation: 0.18
- 1% percentile: -0.7321368980407714
- 99% percentile: 3.3361961364746464

Megacam z (total) - WFC z (total):
- Median: -0.07
- Median Absolute Deviation: 0.20
- 1% percentile: -0.9955057525634765
- 99% percentile: 2.3412176895141594

SUPRIME z (aperture) - WFC z (aperture):
- Median: 0.10
- Median Absolute Deviation: 0.17
- 1% percentile: -0.7828454971313477
- 99% percentile: 2.5906721115112408

SUPRIME z (total) - WFC z (total):
- Median: -0.03
- Median Absolute Deviation: 0.22
- 1% percentile: -0.9388100051879882
- 99% percentile: 2.6204393005370985

GPC1 z (aperture) - WFC z (aperture):
- Median: 0.01
- Median Absolute Deviation: 0.15
- 1% percentile: -0.8958967590332031
- 99% percentile: 1.408468627929681

GPC1 z (total) - WFC z (total):
- Median: 0.07
- Median Absolute Deviation: 0.16
- 1% percentile: -0.8547391891479492
- 99% percentile: 1.3617404937744164

SUPRIME z (aperture) - Megacam z (aperture):
- Median: -0.04
- Median Absolute Deviation: 0.15
- 1% percentile: -1.104429817199707
- 99% percentile: 1.5991294288635258

SUPRIME z (total) - Megacam z (total):
- Median: -0.07
- Median Absolute Deviation: 0.28
- 1% percentile: -3.1023595046997072
- 99% percentile: 2.069321308135987

GPC1 z (aperture) - Megacam z (aperture):
- Median: -0.09
- Median Absolute Deviation: 0.22
- 1% percentile: -3.8263822555541993
- 99% percentile: 1.387774467468262

GPC1 z (total) - Megacam z (total):
- Median: 0.17
- Median Absolute Deviation: 0.11
- 1% percentile: -2.2160144233703614
- 99% percentile: 0.9042765426635738

GPC1 z (aperture) - SUPRIME z (aperture):
- Median: -0.05
- Median Absolute Deviation: 0.21
- 1% percentile: -1.0826454162597656
- 99% percentile: 1.4541187286376953

GPC1 z (total) - SUPRIME z (total):
- Median: 0.13
- Median Absolute Deviation: 0.13
- 1% percentile: -0.7019862365722657
- 99% percentile: 0.8331898117065466

GPC1 y (aperture) - SUPRIME y (aperture):
- Median: -0.11
- Median Absolute Deviation: 0.29
- 1% percentile: -1.6758161926269532
- 99% percentile: 2.540292816162108

GPC1 y (total) - SUPRIME y (total):
- Median: 0.20
- Median Absolute Deviation: 0.16
- 1% percentile: -1.1161381721496582
- 99% percentile: 1.035216197967529

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS and 2MASS to compare its magnitudes to SDSS and 2MASS ones.

master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_ELAIS-N1.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

idx, d2d, _ = sdss_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

sdss = sdss[mask]
ml_sdss_idx = idx[mask]

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap = sdss["fiberMag_{}".format(band[-1])]
        master_cat_mag_ap = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss["petroMag_{}".format(band[-1])]
        master_cat_mag_tot = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))

WFC u (aperture) - SDSS u (fiberMag):
- Median: -0.37
- Median Absolute Deviation: 0.26
- 1% percentile: -2.1387645721435544
- 99% percentile: 1.0094253540039075

WFC u (total) - SDSS u (petroMag):
- Median: -0.37
- Median Absolute Deviation: 0.26
- 1% percentile: -2.1387645721435544
- 99% percentile: 1.0094253540039075

Megacam u (aperture) - SDSS u (fiberMag):
- Median: -0.04
- Median Absolute Deviation: 0.59
- 1% percentile: -2.044686393737793
- 99% percentile: 3.2575323295593246

Megacam u (total) - SDSS u (petroMag):
- Median: -0.04
- Median Absolute Deviation: 0.59
- 1% percentile: -2.044686393737793
- 99% percentile: 3.2575323295593246

WFC g (aperture) - SDSS g (fiberMag):
- Median: -0.49
- Median Absolute Deviation: 0.12
- 1% percentile: -1.2727931022644043
- 99% percentile: 0.46194868087768726

WFC g (total) - SDSS g (petroMag):
- Median: -0.49
- Median Absolute Deviation: 0.12
- 1% percentile: -1.2727931022644043
- 99% percentile: 0.46194868087768726

Megacam g (aperture) - SDSS g (fiberMag):
- Median: -0.33
- Median Absolute Deviation: 0.19
- 1% percentile: -1.2042931175231935
- 99% percentile: 2.403406810760495

Megacam g (total) - SDSS g (petroMag):
- Median: -0.33
- Median Absolute Deviation: 0.19
- 1% percentile: -1.2042931175231935
- 99% percentile: 2.403406810760495

SUPRIME g (aperture) - SDSS g (fiberMag):
- Median: -0.28
- Median Absolute Deviation: 0.17
- 1% percentile: -1.1439088821411132
- 99% percentile: 1.2356151580810513

SUPRIME g (total) - SDSS g (petroMag):
- Median: -0.28
- Median Absolute Deviation: 0.17
- 1% percentile: -1.1439088821411132
- 99% percentile: 1.2356151580810513

GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.51
- Median Absolute Deviation: 0.24
- 1% percentile: -1.874479866027832
- 99% percentile: 1.9854761123657223

GPC1 g (total) - SDSS g (petroMag):
- Median: -0.51
- Median Absolute Deviation: 0.24
- 1% percentile: -1.874479866027832
- 99% percentile: 1.9854761123657223

WFC r (aperture) - SDSS r (fiberMag):
- Median: -0.35
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9242410278320312
- 99% percentile: 0.37569358825683397

WFC r (total) - SDSS r (petroMag):
- Median: -0.35
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9242410278320312
- 99% percentile: 0.37569358825683397

Megacam r (aperture) - SDSS r (fiberMag):
- Median: -0.24
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8530236053466798
- 99% percentile: 2.6363726997375587

Megacam r (total) - SDSS r (petroMag):
- Median: -0.24
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8530236053466798
- 99% percentile: 2.6363726997375587

SUPRIME r (aperture) - SDSS r (fiberMag):
- Median: -0.25
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8844428634643554
- 99% percentile: 2.346479873657227

SUPRIME r (total) - SDSS r (petroMag):
- Median: -0.25
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8844428634643554
- 99% percentile: 2.346479873657227

GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.38
- Median Absolute Deviation: 0.14
- 1% percentile: -1.2143004417419432
- 99% percentile: 0.9139746665954609

GPC1 r (total) - SDSS r (petroMag):
- Median: -0.38
- Median Absolute Deviation: 0.14
- 1% percentile: -1.2143004417419432
- 99% percentile: 0.9139746665954609

WFC i (aperture) - SDSS i (fiberMag):
- Median: -0.39
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9540224075317383
- 99% percentile: 0.3984149932861357

WFC i (total) - SDSS i (petroMag):
- Median: -0.39
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9540224075317383
- 99% percentile: 0.3984149932861357

SUPRIME i (aperture) - SDSS i (fiberMag):
- Median: -0.30
- Median Absolute Deviation: 0.13
- 1% percentile: -0.890188274383545
- 99% percentile: 0.9026821899414045

SUPRIME i (total) - SDSS i (petroMag):
- Median: -0.30
- Median Absolute Deviation: 0.13
- 1% percentile: -0.890188274383545
- 99% percentile: 0.9026821899414045

GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.38
- Median Absolute Deviation: 0.10
- 1% percentile: -0.9555757713317872
- 99% percentile: 0.31397663116455077

GPC1 i (total) - SDSS i (petroMag):
- Median: -0.38
- Median Absolute Deviation: 0.10
- 1% percentile: -0.9555757713317872
- 99% percentile: 0.31397663116455077

WFC z (aperture) - SDSS z (fiberMag):
- Median: -0.33
- Median Absolute Deviation: 0.13
- 1% percentile: -1.307823715209961
- 99% percentile: 0.7922947692871078

WFC z (total) - SDSS z (petroMag):
- Median: -0.33
- Median Absolute Deviation: 0.13
- 1% percentile: -1.307823715209961
- 99% percentile: 0.7922947692871078

Megacam z (aperture) - SDSS z (fiberMag):
- Median: -0.19
- Median Absolute Deviation: 0.19
- 1% percentile: -1.167172966003418
- 99% percentile: 3.8105573463439564

Megacam z (total) - SDSS z (petroMag):
- Median: -0.19
- Median Absolute Deviation: 0.19
- 1% percentile: -1.167172966003418
- 99% percentile: 3.8105573463439564

SUPRIME z (aperture) - SDSS z (fiberMag):
- Median: -0.22
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1998800659179687
- 99% percentile: 0.9810749053955068

SUPRIME z (total) - SDSS z (petroMag):
- Median: -0.22
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1998800659179687
- 99% percentile: 0.9810749053955068

GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.30
- Median Absolute Deviation: 0.16
- 1% percentile: -1.2933573722839355
- 99% percentile: 1.1849651336669922

GPC1 z (total) - SDSS z (petroMag):
- Median: -0.30
- Median Absolute Deviation: 0.16
- 1% percentile: -1.2933573722839355
- 99% percentile: 1.1849651336669922

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:

$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$

# 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)

twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_ELAIS-N1.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]

nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_ukidss_j'][ml_twomass_idx],
                labels=("2MASS J", "UKIDSS J (total)"))

UKIDSS J (total) - 2MASS J:
- Median: 0.02
- Median Absolute Deviation: 0.05
- 1% percentile: -0.8077035259916129
- 99% percentile: 0.8731615960786883

ukidss_ks_like = master_catalogue['m_ukidss_k'] + 0.003 + 0.004 * (
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "UKIDSS Ks-like (total)"))

UKIDSS Ks-like (total) - 2MASS Ks:
- Median: 0.07
- Median Absolute Deviation: 0.08
- 1% percentile: -0.7857970560320414
- 99% percentile: 1.1150006786481284

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.

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.

nb_ccplots(
    master_catalogue['m_megacam_r'],
    master_catalogue['m_ap_megacam_r'] - master_catalogue['m_megacam_r'],
    "r total magnitude (CFHT)", "r aperture mag - total mag (CFHT)",
    master_catalogue["stellarity"],
    invert_x=True
)

Number of source used: 1487261 / 4026292 (36.94%)

V - Color-color and magnitude-color plots

nb_ccplots(
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    "g - i (WFC)", "J - K (UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 239478 / 4026292 (5.95%)

nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_irac_i1'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    "WFC i - IRAC1", "g - i (WFC)",
    master_catalogue["stellarity"]
)

Number of source used: 222833 / 4026292 (5.53%)

nb_ccplots(
    master_catalogue['m_megacam_u'] - master_catalogue['m_megacam_g'],
    master_catalogue['m_megacam_g'] - master_catalogue['m_megacam_r'],
    "u - g (CFHT)", "g - r (CFHT)",
    master_catalogue["stellarity"]
)

Number of source used: 1296100 / 4026292 (32.19%)

nb_ccplots(
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_ukidss_j'],
    "J - K (UKIDSS)", "g - J (WFC, UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 272308 / 4026292 (6.76%)

nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_wfc_z'],
    master_catalogue['m_wfc_z'] - master_catalogue['m_ukidss_j'],
    "i - z (WFC)", "z - J (WFC, UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 214619 / 4026292 (5.33%)

nb_ccplots(
    master_catalogue['m_irac_i3'] - master_catalogue['m_irac_i4'],
    master_catalogue['m_irac_i1'] - master_catalogue['m_irac_i2'],
    "IRAC3 - IRAC4", "IRAC1 - IRAC2",
    master_catalogue["stellarity"]
)

Number of source used: 40827 / 4026292 (1.01%)