In [1]:
from __future__ import print_function, division
In [2]:
# This changes the current directory to the base saga directory - make sure to run this first!
# This is necessary to be able to import the py files and use the right directories,
# while keeping all the notebooks in their own directory.
import os
import sys
from time import time
if 'saga_base_dir' not in locals():
saga_base_dir = os.path.abspath('..')
if saga_base_dir not in sys.path:
os.chdir(saga_base_dir)
In [3]:
import targeting
import casjobs
import numpy as np
from scipy import interpolate
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy import table
from astropy.table import Table
from astropy.io import fits
from astropy.utils.console import ProgressBar
from astropy.utils import data
from collections import Counter
In [4]:
%matplotlib inline
from matplotlib import style, pyplot as plt
plt.style.use('seaborn-deep')
plt.rcParams['image.cmap'] = 'viridis'
plt.rcParams['image.origin'] = 'lower'
plt.rcParams['figure.figsize'] = (14, 8)
plt.rcParams['axes.titlesize'] = plt.rcParams['axes.labelsize'] = 16
plt.rcParams['xtick.labelsize'] = plt.rcParams['ytick.labelsize'] = 14
In [5]:
from IPython import display
In [40]:
def make_cutout_comparison_table(dcat):
"""
Produces a table comparing DECaLS and SDSS objects side-by-side
`dcat` should be a *DECaLS* catalog, not SDSS
"""
de_cutout_url = 'http://legacysurvey.org/viewer/jpeg-cutout/?ra={0.ra.deg}&dec={0.dec.deg}&layer=decals-dr3&pixscale=0.1&bands=grz'
sd_cutout_url = 'http://legacysurvey.org/viewer/jpeg-cutout/?ra={0.ra.deg}&dec={0.dec.deg}&layer=sdssco&pixscale=0.1&bands=gri'
print('put this into http://skyserver.sdss.org/dr13/en/tools/chart/listinfo.aspx')
print('name ra dec')
tabrows = []
for row in dcat:
dviewurl = 'http://legacysurvey.org/viewer?ra={}&dec={}'.format(row['ra'], row['dec'])
sviewurl = 'http://skyserver.sdss.org/dr12/en/tools/chart/navi.aspx?ra={}&dec={}'.format(row['ra'], row['dec'])
sc = SkyCoord(row['ra'], row['dec'], unit=u.deg)
objstr = '{}_{}<br>RA={:.4f}<br>Dec={:.4f}<br>r={:.2f}<br>sb={:.2f}'.format(row['brickid'], row['objid'], row['ra'], row['dec'], row['r'], row['sb_r_0.5'])
deimg = '<a href="{}"><img src="{}"></a>'.format(dviewurl, de_cutout_url.format(sc))
sdimg = '<a href="{}"><img src="{}"></a>'.format(sviewurl, sd_cutout_url.format(sc))
tabrows.append('<tr><td>{}</td><td>{}</td><td>{}</td></tr>'.format(objstr, deimg, sdimg))
print(row['brickname']+'_'+str(row['objid']), row['ra'], row['dec'])
htmlstr = """
<table>
<tr>
<th>obj</th>
<th>DECALS</th>
<th>SDSS</th>
</tr>
{}
</table>
""".format('\n'.join(tabrows))
return display.HTML(htmlstr)
In [6]:
# from the DECALS low-SB_brick selection and data download notebook
bricknames = ['1181m012', '2208m005']
For more in-depth on the context for these, see DECALS low-SB_catalog completeness.ipynb
In [7]:
catalog_fns = ['decals_dr3/catalogs/tractor-{}.fits'.format(bnm) for bnm in bricknames]
decals_catalogs = [Table.read(fn) for fn in catalog_fns]
In [8]:
sdss_fns = ['decals_dr3/catalogs/sdss_comparison_{}.csv'.format(bnm) for bnm in bricknames]
sdss_catalogs = [Table.read(fn) for fn in sdss_fns]
In [9]:
bricks = Table.read('decals_dr3/survey-bricks.fits.gz')
bricksdr3 = Table.read('decals_dr3/survey-bricks-dr3.fits.gz')
In [10]:
ap_sizes = [0.5,0.75,1.0,1.5,2.0,3.5,5.0,7.0] * u.arcsec
In [11]:
def compute_sb(rad, apflux):
if len(apflux.shape)==2:
idxs = np.where(ap_sizes==rad)[0]
assert len(idxs)==1, 'No aperture with size {}'.format(rad)
apflux = apflux[:, idxs[0]]
A = 2.5*np.log10(np.pi*(rad.to(u.arcsec).value)**2)
return np.array(22.5 - 2.5*np.log10(apflux) + A) * u.mag * u.arcsec**-2
for dcat in decals_catalogs:
dcat['g'] = np.array(22.5 - 2.5*np.log10(dcat['decam_flux'][:, 1]))*u.mag
dcat['r'] = np.array(22.5 - 2.5*np.log10(dcat['decam_flux'][:, 2]))*u.mag
dcat['z'] = np.array(22.5 - 2.5*np.log10(dcat['decam_flux'][:, 4]))*u.mag
dcat['sb_r_0.5'] = compute_sb(0.5*u.arcsec, dcat['decam_apflux'][:, 2, :])
dcat['sb_r_0.75'] = compute_sb(0.75*u.arcsec, dcat['decam_apflux'][:, 2, :])
dcat['sb_r_1'] = compute_sb(1.0*u.arcsec, dcat['decam_apflux'][:, 2, :])
dcat['sb_r_2'] =compute_sb(2.0*u.arcsec, dcat['decam_apflux'][:, 2, :])
We want to try to get a surface birghtness something like SDSS's expmag + 2.5 log10(2 pi exprad^2). But DECaLS has no expmag. So lets try interpolating the aperture fluxes to the appropriate radius.
In [12]:
dcat0 = decals_catalogs[0]
rap_fluxes = dcat0['decam_apflux'][:, 2, :]
expfluxr = np.empty_like(rap_fluxes[:, 0])
ap_sizesv = ap_sizes.to(u.arcsec).value
for i in ProgressBar(range(len(rap_fluxes)), ipython_widget=True):
f = rap_fluxes[i]
r = dcat0['shapeExp_r'][i]
expfluxr[i] = np.interp(r, ap_sizesv, f)
Now lets visualize a few of these flux profiles just to see if this seems sensible
In [13]:
j = 0
for i in np.random.permutation(len(rap_fluxes)):
fnorm = rap_fluxes[i][4]
p = plt.plot(ap_sizes, rap_fluxes[i]/fnorm,'-o', ms=4, mew=0)[0]
plt.scatter([dcat0['shapeExp_r'][i]], [expfluxr[i]/fnorm], color=p.get_color(), s=60, zorder=5)
j += 1
if j>9:
break
plt.xlim(0, 7)
plt.ylim(0, 2)
plt.xlabel('aperture')
plt.ylabel('flux w/i aperture, normalized to 2"')
Out[13]:
What's the deal with all those objects that have an shapeExp_r of 0?
In [14]:
Counter(dcat0[dcat0['shapeExp_r']>0]['type']), Counter(dcat0[dcat0['shapeExp_r']==0]['type'])
Out[14]:
Oops... looks like stars, and more importantly, DeV-fit galaxies, have no shapeExp_r! Guess we have to use other shapes for them. So we'll use decam_psfsize for the stars, shapeDev_r for the early-types, and shapeExp_r for other gals.
In [15]:
dcat0 = decals_catalogs[0]
rap_fluxes = dcat0['decam_apflux'][:, 2, :]
fluxr = np.empty_like(rap_fluxes[:, 0])
rap_sizesv = ap_sizes.to(u.arcsec).value
intr = interpolate.BarycentricInterpolator(ap_sizesv, [0]*len(ap_sizesv))
for i in ProgressBar(range(len(rap_fluxes)), ipython_widget=True):
f = rap_fluxes[i]
if dcat0['type'][i] == 'PSF ':
r = dcat0['decam_psfsize'][i, 2]
elif dcat0['type'][i] == 'DEV ':
r = dcat0['shapeDev_r'][i]
else:
r = dcat0['shapeExp_r'][i]
intr.set_yi(f)
fluxr[i] = intr(r)
In [16]:
fig, (ax1, ax2) = plt.subplots(1, 2)
rs = []
fluxes = []
colors = []
markers = []
for i in np.random.permutation(len(rap_fluxes))[:15]:
fnorm = rap_fluxes[i][4]
p = ax1.plot(ap_sizes, rap_fluxes[i]/fnorm,'-o', ms=4, mew=0)[0]
colors.append(p.get_color())
if dcat0['type'][i] == 'PSF ':
rs.append(dcat0['decam_psfsize'][i, 2])
markers.append('*')
elif dcat0['type'][i] == 'DEV ':
rs.append(dcat0['shapeDev_r'][i])
markers.append('^')
else:
rs.append(dcat0['shapeExp_r'][i])
markers.append('o')
fluxes.append(fluxr[i])
ax1.scatter([rs[-1]], [fluxr[i]/fnorm], color=colors[-1],
s=60 if markers[-1]=='o' else 80,
zorder=5, marker=markers[-1])
# #uncomment this to look at the total flux of a star
# if markers[-1]=='*':
# ax1.scatter([rs[-1]], [dcat0['decam_flux'][i, 2]/fnorm], color=colors[-1],
# s=60 if markers[-1]=='o' else 80,
# zorder=5, marker='s')
ax1.set_xlim(0, 7)
ax1.set_ylim(0, 2)
ax1.set_xlabel('aperture')
ax1.set_ylabel('flux w/i aperture, normalized to 2"')
sb = compute_sb(rs*u.arcsec, np.array(fluxes))
mags = np.array(22.5 - 2.5*np.log10(fluxes))
for x, y, c, m in zip(mags, sb, colors, markers):
ax2.scatter([x], [y.value], color=c,
s=60 if m=='o' else 80,
zorder=5, marker=m)
ax2.set_xlabel('$r$')
ax2.set_ylabel('SDSS-like SB')
print('triangles are DeV, circles are exp, * are PSFs')
In [17]:
for dcat in decals_catalogs:
rap_fluxes = dcat['decam_apflux'][:, 2, :]
expflux_r = np.empty_like(rap_fluxes[:, 0])
rad = np.empty(len(rap_fluxes[:, 0]))
ap_sizesv = ap_sizes.to(u.arcsec).value
intr = interpolate.BarycentricInterpolator(ap_sizesv, [0]*len(ap_sizesv))
for i in ProgressBar(range(len(rap_fluxes)), ipython_widget=True):
f = rap_fluxes[i]
if dcat['type'][i] == 'PSF ':
r = dcat['decam_psfsize'][i, 2]
elif dcat['type'][i] == 'DEV ':
r = dcat['shapeDev_r'][i]
else:
r = dcat['shapeExp_r'][i]
intr.set_yi(f)
expflux_r[i] = intr(r)
rad[i] = r
dcat['sdss_like_sb_r'] = compute_sb(rad*u.arcsec, np.array(expflux_r))
In [18]:
boundaries = [((-1.375, -1.2), (118, 118.145)),
((-0.625, -0.375), (220.75, 221))]
In [19]:
dcut = []
scut = []
In [20]:
dcat = decals_catalogs[0]
scat = sdss_catalogs[0]
bnds = boundaries[0]
plt.scatter(dcat['ra'], dcat['dec'], lw=0, c='g')
plt.scatter(scat['RA'], scat['DEC'], lw=0, c='r')
plt.axhline(bnds[0][0], c='k', ls='--')
plt.axhline(bnds[0][1], c='k', ls='--')
plt.axvline(bnds[1][0], c='k', ls='--')
plt.axvline(bnds[1][1], c='k', ls='--')
dmsk = ((bnds[1][0]<dcat['ra']) & (dcat['ra']<bnds[1][1]) &
(bnds[0][0]<dcat['dec']) & (dcat['dec']<bnds[0][1]))
dcut.append(dcat[dmsk])
smsk = ((bnds[1][0]<scat['RA']) & (scat['RA']<bnds[1][1]) &
(bnds[0][0]<scat['DEC']) & (scat['DEC']<bnds[0][1]))
scut.append(scat[smsk])
In [21]:
dcat = decals_catalogs[1]
scat = sdss_catalogs[1]
bnds = boundaries[1]
plt.scatter(dcat['ra'], dcat['dec'], lw=0, c='g')
plt.scatter(scat['RA'], scat['DEC'], lw=0, c='r')
plt.axhline(bnds[0][0], c='k', ls='--')
plt.axhline(bnds[0][1], c='k', ls='--')
plt.axvline(bnds[1][0], c='k', ls='--')
plt.axvline(bnds[1][1], c='k', ls='--')
dmsk = ((bnds[1][0]<dcat['ra']) & (dcat['ra']<bnds[1][1]) &
(bnds[0][0]<dcat['dec']) & (dcat['dec']<bnds[0][1]))
dcut.append(dcat[dmsk])
smsk = ((bnds[1][0]<scat['RA']) & (scat['RA']<bnds[1][1]) &
(bnds[0][0]<scat['DEC']) & (scat['DEC']<bnds[0][1]))
scut.append(scat[smsk])
In [22]:
dcutall = table.vstack(dcut)
scutall = table.vstack(scut)
In [23]:
dsc = SkyCoord(dcutall['ra'], dcutall['dec'], unit=u.deg)
ssc = SkyCoord(scutall['RA'], scutall['DEC'], unit=u.deg)
In [24]:
idx, d2d, _ = ssc.match_to_catalog_sky(dsc)
plt.hist(d2d.arcsec, bins=100, range=(0, 3),histtype='step', log=True)
None
In [25]:
dmatchmsk = idx[d2d<1.5*u.arcsec]
dmatch = dcutall[dmatchmsk]
smatch = scutall[d2d<1.5*u.arcsec]
In [26]:
didxs = np.arange(len(dcutall))
dnomatchmsk = didxs[~np.in1d(didxs, idx[d2d<1.5*u.arcsec])]
dnomatch = dcutall[dnomatchmsk]
In [27]:
plt.figure(figsize=(12, 10))
xnm = 'r'
ynm = 'sb_r_0.5'
dmextinction = -2.5*np.log10(dmatch['decam_mw_transmission'][:, 2])
x = dmatch[xnm] - dmextinction
y = dmatch[ynm] - dmextinction
dstar = dmatch['type']=='PSF '
sstar = smatch['PHOT_SG']=='STAR'
plt.scatter(x[~dstar&~sstar], y[~dstar&~sstar], c='b', lw=0, alpha=.5, label='Glx in Both')
plt.scatter(x[dstar&~sstar], y[dstar&~sstar], c='r', lw=0, alpha=.5, label='Star in DECaLS, Glx in SDSS')
plt.scatter(x[~dstar&sstar], y[~dstar&sstar], c='g', lw=0, alpha=.5, label='Glx in DECaLS, Star in SDSS')
dnstar = dnomatch['type']=='PSF '
dnoext = -2.5*np.log10(dnomatch['decam_mw_transmission'][:, 2])
plt.scatter((dnomatch[xnm] - dnoext)[~dnstar], (dnomatch[ynm] - dnoext)[~dnstar],
c='k', lw=0, alpha=.5, label='Glx in DECALS, not in SDSS')
#plt.hlines(24.5, 17, 21, colors='k', linestyles='--')
plt.axvline(20.75, color='k', ls=':')
plt.xlim(17, 24.5)
plt.ylim(18, 28)
plt.xlabel(r'$r_{0, {\rm DECaLS}}$', fontsize=28)
plt.ylabel(r'$SB_{0.5^{\prime \prime}, {\rm DECaLS}}$', fontsize=28)
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.legend(loc='lower right', fontsize=20)
plt.savefig(os.environ['HOME'] + '/Desktop/fig_decals.pdf')
In [41]:
to_check = (dcutall['r']<21)&(dcutall['sb_r_0.5']>24.5) & (dcutall['type']!='PSF ')
make_cutout_comparison_table(dcutall[to_check])
Out[41]:
All seem to be artifacts in DECaLS - scattered light, cosmic rays, etc
In [42]:
to_check = ((18<dnomatch['r']) & (dnomatch['r']<21) &
(18<dnomatch['sb_r_0.5']) & (dnomatch['sb_r_0.5']<24.5) &
(dnomatch['type']!='PSF '))
make_cutout_comparison_table(dnomatch[to_check])
Out[42]:
These are almost entirely either deblender ambiguities (i.e. by eye may or may not be two sources) or areas where SDSS had a scattered-light/background problem. A few debatebly real objects, but not preferentially low-SB or anything.
In [46]:
to_check = ~dstar&sstar&(dmatch['r']<21)
make_cutout_comparison_table(dmatch[to_check])
Out[46]:
A few are possibly real due to improved seeing (323640_5940, 323640_5564), vast majority are either areas where SDSS backgrounds are problematic, or bright star artifacts/blended stars.
This also has a few more points for stars just to see the differences. That's why we still include 0.5 even though it's above, too
In [30]:
plt.figure(figsize=(12, 10))
xnm = 'r'
ynm = 'sb_r_0.5'
dmextinction = -2.5*np.log10(dmatch['decam_mw_transmission'][:, 2])
x = dmatch[xnm] - dmextinction
y = dmatch[ynm] - dmextinction
dstar = dmatch['type']=='PSF '
sstar = smatch['PHOT_SG']=='STAR'
dnstar = dnomatch['type']=='PSF '
dnoext = -2.5*np.log10(dnomatch['decam_mw_transmission'][:, 2])
plt.scatter((dnomatch[xnm] - dnoext)[~dnstar], (dnomatch[ynm] - dnoext)[~dnstar],
c='k', lw=0, alpha=.5, label='Glx in DECALS, not in SDSS', marker='s')
plt.scatter((dnomatch[xnm] - dnoext)[dnstar], (dnomatch[ynm] - dnoext)[dnstar],
c='k', lw=0, alpha=.3, s=60,
label='Star in DECALS, not in SDSS', marker='*')
plt.scatter(x[dstar&sstar], y[dstar&sstar], c='m', lw=0, alpha=.5, s=60,
label='Star in both', marker='*')
plt.scatter(x[~dstar&~sstar], y[~dstar&~sstar], c='b', lw=0, alpha=.5, label='Glx in Both')
plt.scatter(x[dstar&~sstar], y[dstar&~sstar], c='r', lw=0, alpha=.5,
label='Star in DECaLS, Glx in SDSS', marker='^', s=50)
plt.scatter(x[~dstar&sstar], y[~dstar&sstar], c='g', lw=0, alpha=.5,
label='Glx in DECaLS, Star in SDSS', marker='^', s=50)
#plt.hlines(24.5, 17, 21, colors='k', linestyles='--')
plt.axvline(20.75, color='k', ls=':')
plt.xlim(17, 24.5)
plt.ylim(18, 28)
plt.xlabel(r'$r_{0, {\rm DECaLS}}$', fontsize=28)
plt.ylabel(r'$SB_{0.5^{\prime \prime}, {\rm DECaLS}}$', fontsize=28)
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.legend(loc='lower right', fontsize=20)
Out[30]:
In [31]:
plt.figure(figsize=(12, 10))
xnm = 'r'
ynm = 'sb_r_0.75'
dmextinction = -2.5*np.log10(dmatch['decam_mw_transmission'][:, 2])
x = dmatch[xnm] - dmextinction
y = dmatch[ynm] - dmextinction
dstar = dmatch['type']=='PSF '
sstar = smatch['PHOT_SG']=='STAR'
dnstar = dnomatch['type']=='PSF '
dnoext = -2.5*np.log10(dnomatch['decam_mw_transmission'][:, 2])
plt.scatter((dnomatch[xnm] - dnoext)[~dnstar], (dnomatch[ynm] - dnoext)[~dnstar],
c='k', lw=0, alpha=.5, label='Glx in DECALS, not in SDSS', marker='s')
plt.scatter((dnomatch[xnm] - dnoext)[dnstar], (dnomatch[ynm] - dnoext)[dnstar],
c='k', lw=0, alpha=.3, s=60,
label='Star in DECALS, not in SDSS', marker='*')
plt.scatter(x[dstar&sstar], y[dstar&sstar], c='m', lw=0, alpha=.5, s=60,
label='Star in both', marker='*')
plt.scatter(x[~dstar&~sstar], y[~dstar&~sstar], c='b', lw=0, alpha=.5, label='Glx in Both')
plt.scatter(x[dstar&~sstar], y[dstar&~sstar], c='r', lw=0, alpha=.5,
label='Star in DECaLS, Glx in SDSS', marker='^', s=50)
plt.scatter(x[~dstar&sstar], y[~dstar&sstar], c='g', lw=0, alpha=.5,
label='Glx in DECaLS, Star in SDSS', marker='^', s=50)
#plt.hlines(24.5, 17, 21, colors='k', linestyles='--')
plt.axvline(20.75, color='k', ls=':')
plt.xlim(17, 24.5)
plt.ylim(18, 28)
plt.xlabel(r'$r_{0, {\rm DECaLS}}$', fontsize=28)
plt.ylabel(r'$SB_{0.75^{\prime \prime}, {\rm DECaLS}}$', fontsize=28)
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.legend(loc='lower right', fontsize=20)
Out[31]:
In [32]:
plt.figure(figsize=(12, 10))
xnm = 'r'
ynm = 'sb_r_1'
dmextinction = -2.5*np.log10(dmatch['decam_mw_transmission'][:, 2])
x = dmatch[xnm] - dmextinction
y = dmatch[ynm] - dmextinction
dstar = dmatch['type']=='PSF '
sstar = smatch['PHOT_SG']=='STAR'
dnstar = dnomatch['type']=='PSF '
dnoext = -2.5*np.log10(dnomatch['decam_mw_transmission'][:, 2])
plt.scatter((dnomatch[xnm] - dnoext)[~dnstar], (dnomatch[ynm] - dnoext)[~dnstar],
c='k', lw=0, alpha=.5, label='Glx in DECALS, not in SDSS', marker='s')
plt.scatter((dnomatch[xnm] - dnoext)[dnstar], (dnomatch[ynm] - dnoext)[dnstar],
c='k', lw=0, alpha=.3, s=60,
label='Star in DECALS, not in SDSS', marker='*')
plt.scatter(x[dstar&sstar], y[dstar&sstar], c='m', lw=0, alpha=.5, s=60,
label='Star in both', marker='*')
plt.scatter(x[~dstar&~sstar], y[~dstar&~sstar], c='b', lw=0, alpha=.5, label='Glx in Both')
plt.scatter(x[dstar&~sstar], y[dstar&~sstar], c='r', lw=0, alpha=.5,
label='Star in DECaLS, Glx in SDSS', marker='^', s=50)
plt.scatter(x[~dstar&sstar], y[~dstar&sstar], c='g', lw=0, alpha=.5,
label='Glx in DECaLS, Star in SDSS', marker='^', s=50)
#plt.hlines(24.5, 17, 21, colors='k', linestyles='--')
plt.axvline(20.75, color='k', ls=':')
plt.xlim(17, 24.5)
plt.ylim(18, 28)
plt.xlabel(r'$r_{0, {\rm DECaLS}}$', fontsize=28)
plt.ylabel(r'$SB_{1.0^{\prime \prime}, {\rm DECaLS}}$', fontsize=28)
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.legend(loc='lower right', fontsize=20)
Out[32]:
In [33]:
plt.figure(figsize=(12, 10))
xnm = 'r'
ynm = 'sb_r_2'
dmextinction = -2.5*np.log10(dmatch['decam_mw_transmission'][:, 2])
x = dmatch[xnm] - dmextinction
y = dmatch[ynm] - dmextinction
dstar = dmatch['type']=='PSF '
sstar = smatch['PHOT_SG']=='STAR'
dnstar = dnomatch['type']=='PSF '
dnoext = -2.5*np.log10(dnomatch['decam_mw_transmission'][:, 2])
plt.scatter((dnomatch[xnm] - dnoext)[~dnstar], (dnomatch[ynm] - dnoext)[~dnstar],
c='k', lw=0, alpha=.5, label='Glx in DECALS, not in SDSS', marker='s')
plt.scatter((dnomatch[xnm] - dnoext)[dnstar], (dnomatch[ynm] - dnoext)[dnstar],
c='k', lw=0, alpha=.3, s=60,
label='Star in DECALS, not in SDSS', marker='*')
plt.scatter(x[dstar&sstar], y[dstar&sstar], c='m', lw=0, alpha=.5, s=60,
label='Star in both', marker='*')
plt.scatter(x[~dstar&~sstar], y[~dstar&~sstar], c='b', lw=0, alpha=.5, label='Glx in Both')
plt.scatter(x[dstar&~sstar], y[dstar&~sstar], c='r', lw=0, alpha=.5,
label='Star in DECaLS, Glx in SDSS', marker='^', s=50)
plt.scatter(x[~dstar&sstar], y[~dstar&sstar], c='g', lw=0, alpha=.5,
label='Glx in DECaLS, Star in SDSS', marker='^', s=50)
#plt.hlines(24.5, 17, 21, colors='k', linestyles='--')
plt.axvline(20.75, color='k', ls=':')
plt.xlim(17, 24.5)
plt.ylim(18, 28)
plt.xlabel(r'$r_{0, {\rm DECaLS}}$', fontsize=28)
plt.ylabel(r'$SB_{2.0^{\prime \prime}, {\rm DECaLS}}$', fontsize=28)
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.legend(loc='lower right', fontsize=20)
Out[33]:
Important thing to understand here: this is not the same as the SDSS SB's for a subtle reason. The SDSS SB's are all from assuming an exponential profile. Here, instead (because DECaLS doesn't have the data for exp profiles if the object is not found to be exp type), we use an SB within either the exponential half-light radius, the de Vaucouleurs effective radius, or the seeing FWHM for exp galaxies, DeV galaxies, or PSF's, respectively.
The flux is also not the modeled flux, but rather an interpolated flux at that radius from the apertures.
In [34]:
plt.figure(figsize=(12, 10))
xnm = 'r'
ynm = 'sdss_like_sb_r'
dmextinction = -2.5*np.log10(dmatch['decam_mw_transmission'][:, 2])
x = dmatch[xnm] - dmextinction
y = dmatch[ynm] - dmextinction
dstar = dmatch['type']=='PSF '
sstar = smatch['PHOT_SG']=='STAR'
dnstar = dnomatch['type']=='PSF '
dnoext = -2.5*np.log10(dnomatch['decam_mw_transmission'][:, 2])
plt.scatter((dnomatch[xnm] - dnoext)[~dnstar], (dnomatch[ynm] - dnoext)[~dnstar],
c='k', lw=0, alpha=.5, label='Glx in DECALS, not in SDSS', marker='s')
plt.scatter((dnomatch[xnm] - dnoext)[dnstar], (dnomatch[ynm] - dnoext)[dnstar],
c='k', lw=0, alpha=.3, s=60,
label='Star in DECALS, not in SDSS', marker='*')
plt.scatter(x[dstar&sstar], y[dstar&sstar], c='m', lw=0, alpha=.5, s=60,
label='Star in both', marker='*')
plt.scatter(x[~dstar&~sstar], y[~dstar&~sstar], c='b', lw=0, alpha=.5, label='Glx in Both')
plt.scatter(x[dstar&~sstar], y[dstar&~sstar], c='r', lw=0, alpha=.5,
label='Star in DECaLS, Glx in SDSS', marker='^', s=50)
plt.scatter(x[~dstar&sstar], y[~dstar&sstar], c='g', lw=0, alpha=.5,
label='Glx in DECaLS, Star in SDSS', marker='^', s=50)
#plt.hlines(24.5, 17, 21, colors='k', linestyles='--')
plt.axvline(20.75, color='k', ls=':')
plt.xlim(17, 24.5)
plt.ylim(18, 30)
plt.xlabel(r'$r_{0, {\rm DECaLS}}$', fontsize=28)
plt.ylabel(r'$SB_{sdssish}, {\rm DECaLS}}$', fontsize=28)
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.legend(loc='lower right', fontsize=20)
Out[34]:
The below plot is to see if there's significant differences between the two galaxy types, since their SB calculations are quite different
In [35]:
plt.figure(figsize=(12, 10))
xnm = 'r'
ynm = 'sdss_like_sb_r'
dmextinction = -2.5*np.log10(dmatch['decam_mw_transmission'][:, 2])
x = dmatch[xnm] - dmextinction
y = dmatch[ynm] - dmextinction
dstar = dmatch['type']=='PSF '
ddev = dmatch['type']=='DEV '
sstar = smatch['PHOT_SG']=='STAR'
dnstar = dnomatch['type']=='PSF '
dndev = dnomatch['type']=='DEV '
dnoext = -2.5*np.log10(dnomatch['decam_mw_transmission'][:, 2])
plt.scatter((dnomatch[xnm] - dnoext)[~dnstar&~dndev], (dnomatch[ynm] - dnoext)[~dnstar&~dndev],
c='g', lw=0, alpha=.5, label='Exp Glx in DECALS, not in SDSS', marker='*', s=40)
plt.scatter((dnomatch[xnm] - dnoext)[~dnstar&dndev], (dnomatch[ynm] - dnoext)[~dnstar&dndev],
c='k', lw=0, alpha=.5, label='DeV Glx in DECALS, not in SDSS', marker='^', s=30)
plt.scatter(x[~dstar&ddev], y[~dstar&ddev], c='b', lw=0, alpha=.5,
label='Exp Glx in DECaLS, in SDSS', marker='o', s=15)
plt.scatter(x[~dstar&~ddev], y[~dstar&~ddev], c='r', lw=0, alpha=.5,
label='DeV Glx in DECaLS, in SDSS', marker='s', s=20)
#plt.hlines(24.5, 17, 21, colors='k', linestyles='--')
plt.axvline(20.75, color='k', ls=':')
plt.xlim(17, 24.5)
plt.ylim(18, 30)
plt.xlabel(r'$r_{0, {\rm DECaLS}}$', fontsize=28)
plt.ylabel(r'$SB_{sdssish}, {\rm DECaLS}}$', fontsize=28)
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.legend(loc='lower right', fontsize=20)
Out[35]:
In [36]:
maggaldepth = -2.5*(np.log10(5*dmatch['decam_galdepth']**-0.5)-9)
maggaldepthr = maggaldepth[:, 2]
In [37]:
plt.figure()
plt.hist(maggaldepthr[np.isfinite(maggaldepthr)], bins=100, histtype='step', log=False)
plt.xlabel('galdepth in $r$')
plt.ylabel('$n$')
plt.tight_layout()
plt.figure()
plt.hist(maggaldepthr[np.isfinite(maggaldepthr)], bins=100, histtype='step', log=True)
plt.xlabel('galdepth in $r$')
plt.ylabel('$n$')
plt.tight_layout()
In [38]:
for bnm in np.unique(dmatch['brickname']):
inbrick = dmatch['brickname'] == bnm
plt.hist(maggaldepthr[np.isfinite(maggaldepthr)&inbrick], bins=100, histtype='step', log=True, label='Brick '+bnm)
plt.xlabel('galdepth in $r$')
plt.ylabel('$n$')
plt.legend(loc=0)
plt.tight_layout()