Plots for SFR-Stellar Mass-Size Paper

Trying to get my thoughts organized for the first paper.



In [2]:

    
import numpy as np
from pylab import *
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')



In [3]:

    
%run ~/Dropbox/pythonCode/LCSanalyzeblue.py









    



Running on Rose's mac pro
Running on Rose's mac pro or laptop
Running on Rose's mac pro or laptop
--------------------------------------------------
Date: Tue Aug  1 16:32:05 2006
User: elbaz
Host: 251-198.ipac.caltech.edu
--------------------------------------------------
Format: 9
Architecture: ppc
Operating System: darwin
IDL Version: 6.1
--------------------------------------------------
Successfully read 14 records of which:
 - 10 are of type VARIABLE
 - 1 are of type TIMESTAMP
 - 1 are of type NOTICE
 - 1 are of type VERSION
--------------------------------------------------
Available variables:
 - nulnu_iras25 [<type 'numpy.ndarray'>]
 - nulnuinlsun [<type 'numpy.ndarray'>]
 - nulnu_iras100 [<type 'numpy.ndarray'>]
 - nulnu_iras12 [<type 'numpy.ndarray'>]
 - lir_sanders [<type 'numpy.ndarray'>]
 - nulnu_iras60 [<type 'numpy.ndarray'>]
 - lir [<type 'numpy.ndarray'>]
 - nulnu_lw3 [<type 'numpy.ndarray'>]
 - nulnu_lw2 [<type 'numpy.ndarray'>]
 - lambda [<type 'numpy.ndarray'>]
--------------------------------------------------
WARNING: problem matching to moustakas MSTAR_50 - probably ok
WARNING: problem matching to moustakas MSTAR_50 - probably ok
did not find  99840
did not find  146607

Converting SDSS Magnitude Limit to Stellar Mass Cut



In [1]:

    
# using John Moustakas's stellar mass estimates
figure()
plot(s.s.ABSMAG[:,4][s.blueflag2],s.logstellarmass[s.blueflag2],'bo')
xlabel('$M_r$')
ylabel('$ log_{10}(M_*) $')
# r-band limit
rlim=17.77
# distance modulus to Hercules, the furthest cluster
mM=35.97
# absolute mag limit corresponding to r=17.7
Mr=rlim-mM
axvline(x=Mr,ls='--',color='r')
axis([-20,-16,8.5,10.5])
axhline(y=9.5)









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-ca46890b67dd> in <module>()
      1 # using John Moustakas's stellar mass estimates
----> 2 figure()
      3 plot(s.s.ABSMAG[:,4][s.blueflag2],s.logstellarmass[s.blueflag2],'bo')
      4 xlabel('$M_r$')
      5 ylabel('$ log_{10}(M_*) $')

NameError: name 'figure' is not defined

RESULT: SDSS mag limit corresponds to a stellar mass cut of approximately $log_{10}(M_*) > 9.5$.

Select Blue Galaxies using NUV-r Color

I have limited the sample to blue galaxies only, using a NUV-r color cut:

self.blueflag2= (self.nsamag[:,1] - self.nsamag[:,4]) < 4.

Some galaxies don't have GALEX data (JM is checking into why this is the case). For these, I require u-r < 1.8.



In [213]:

    
s.plotsalimcolormag()

SFR-Mass-Size for Blue Galaxies only



In [214]:

    
# For blue galaxies only
s.plotSFRStellarmassSizeBlue(blueflag=True)



In [215]:

    
# to compare size distributions
print 'comparing size ratios for field vs cluster'
f1=s.bluesampleflag & ~s.membflag & ~s.agnflag
m1=s.bluesampleflag & s.membflag & ~s.agnflag
t=ks(s.s.SIZE_RATIO[f1],s.s.SIZE_RATIO[m1])
print 'mean of field = %5.2f +/- %5.2f'%(mean(s.s.SIZE_RATIO[f1]),std(s.s.SIZE_RATIO[f1])/sqrt(1.*sum(f1)))
print 'mean of clust = %5.2f +/- %5.2f'%(mean(s.s.SIZE_RATIO[m1]),std(s.s.SIZE_RATIO[m1])/sqrt(1.*sum(m1)))









    



comparing size ratios for field vs cluster
KS Test:
D =   0.32
p-vale = 0.00127 (prob that samples are from same distribution)
Anderson-Darling test Test:
D =   5.88
p-vale = 0.00184 (prob that samples are from same distribution)
mean of field =  0.68 +/-  0.03
mean of clust =  0.55 +/-  0.03



In [216]:

    
s.printsize()









    



$cluster$: 
 	 N_SF  =  70, size =  0.55 ( 0.54) +/-  0.03 
 	 N_AGN =  12, size =  0.41 ( 0.43) +/-  0.06 
	 N_ALL =  82, size =  0.53 ( 0.52) +/-  0.03 


$near \ field$: 
 	 N_SF  =  51, size =  0.66 ( 0.64) +/-  0.03 
 	 N_AGN =   8, size =  0.67 ( 0.72) +/-  0.08 
	 N_ALL =  59, size =  0.66 ( 0.65) +/-  0.03 


$field$: 
 	 N_SF  =  17, size =  0.73 ( 0.72) +/-  0.06 
 	 N_AGN =   2, size =  0.68 ( 0.68) +/-  0.05 
	 N_ALL =  19, size =  0.72 ( 0.72) +/-  0.06



In [ ]:

    
# comparing sizes for sample with Coma removed
nc.plotSFRStellarmassSizeBlue(blueflag=True)
nc.printsize()
ncf1=nc.bluesampleflag & ~nc.membflag & ~nc.agnflag
ncm1=nc.bluesampleflag & nc.membflag & ~nc.agnflag
t=ks(nc.s.SIZE_RATIO[ncf1],nc.s.SIZE_RATIO[ncm1])









    



$cluster$: 
 	 N_SF  =  54, size =  0.59 ( 0.56) +/-  0.04 
 	 N_AGN =   6, size =  0.34 ( 0.28) +/-  0.09 
	 N_ALL =  60, size =  0.56 ( 0.54) +/-  0.03 


$near \ field$: 
 	 N_SF  =  50, size =  0.66 ( 0.65) +/-  0.03 
 	 N_AGN =   8, size =  0.67 ( 0.72) +/-  0.08 
	 N_ALL =  58, size =  0.66 ( 0.67) +/-  0.03 


$field$: 
 	 N_SF  =  12, size =  0.69 ( 0.70) +/-  0.08 
 	 N_AGN =   2, size =  0.68 ( 0.68) +/-  0.05 
	 N_ALL =  14, size =  0.69 ( 0.70) +/-  0.07 


KS Test:
D =   0.26
p-vale = 0.03427 (prob that samples are from same distribution)
Anderson-Darling test Test:
D =   2.01
p-vale = 0.04774 (prob that samples are from same distribution)

RESULTS ARE NOT AS SIGNIFICANT ONCE COMA IS REMOVED. DIFFERENCE BETWEEN FIELD AND CLUSTERS ARE NOW AT 2-SIGMA LEVEL. THE CONCLUSION IS THAT COMA IS IMPORTANT - PERHAPS ENVIRONMENTAL EFFECTS ARE STRONGER; COMA ALSO CONTRIBUTES TO SAMPLE SIZE.

Checking for Other Hidden Systematics

Need to make sure that we are not seeing the effect of some parameter that is linked with environment. For example, B/T is strongly correlated with environments.

Check:

stellar mass
B/T
others?



In [ ]:

    
# stellar mass

print 'comparing stellar mass for field vs cluster'
f1=s.bluesampleflag & ~s.membflag & ~s.agnflag
m1=s.bluesampleflag & s.membflag & ~s.agnflag
t=ks(s.logstellarmass[f1],s.logstellarmass[m1])

# B/T

print ''
print 'comparing B/T for field vs cluster'
f1=s.bluesampleflag & ~s.membflag & ~s.agnflag & s.gim2dflag
m1=s.bluesampleflag & s.membflag & ~s.agnflag & s.gim2dflag
t=ks(s.s.B_T_r[f1],s.s.B_T_r[m1])









    



comparing stellar mass for field vs cluster
KS Test:
D =   0.18
p-vale = 0.18775 (prob that samples are from same distribution)
Anderson-Darling test Test:
D =  -0.11
p-vale = 0.39150 (prob that samples are from same distribution)

comparing B/T for field vs cluster
KS Test:

END OF PAPER 1 PLOTS



In [ ]:

    
# not sure why I was looking at this
plot(s.s.SIZE_RATIO[s.sampleflag & ~s.agnflag],s.s.fcnsersic1[s.sampleflag&~s.agnflag],'bo')
xlabel('Re(24)/Re(r)')
ylabel('Sersic index')
gca().set_yscale('log')

Systematics

looking for systematics in size measurements. In particular, how does size vary with

B/A
Re(r)
distance



In [ ]:

    
# SIZE VS B/A
figure()
plot(s.s.SERSIC_BA[s.sampleflag & ~s.agnflag],s.s.SIZE_RATIO[s.sampleflag & ~s.agnflag],'bo')
xlabel('r-band B/A')
ylabel('Re(24)/Re(r)')
t=spearman(s.s.SERSIC_BA[s.sampleflag & ~s.agnflag],s.s.SIZE_RATIO[s.sampleflag & ~s.agnflag])



In [ ]:

    
# SIZE VS SERSIC_TH50
figure()
plot(s.s.SERSIC_TH50[s.sampleflag & ~s.agnflag],s.s.SIZE_RATIO[s.sampleflag & ~s.agnflag],'bo')
xlabel('r-band Re(r) (arcsec)')
ylabel('Re(24)/Re(r)')
spearman(s.s.SERSIC_TH50[s.sampleflag & ~s.agnflag],s.s.SIZE_RATIO[s.sampleflag & ~s.agnflag])



In [ ]:

    
# SIZE VS DISTANCE
figure()
plot(s.s.ZDIST[s.sampleflag & ~s.agnflag],s.s.SIZE_RATIO[s.sampleflag & ~s.agnflag],'bo')
plot(s.s.ZDIST[s.sampleflag & ~s.agnflag & s.membflag],s.s.SIZE_RATIO[s.sampleflag & ~s.agnflag & s.membflag],'ro',label='Member')
xlabel('ZDISTANCE')
ylabel('Re(24)/Re(r)')
spearman(s.s.ZDIST[s.sampleflag & ~s.agnflag],s.s.SIZE_RATIO[s.sampleflag & ~s.agnflag])

Comparing SE Radii



In [ ]:

    
# looking at using SE FLUX_RADIUS to define a concentration value

figure()
flag=s.sampleflag & ~s.agnflag
plot(s.s.fcre1[flag],s.s.FLUX_RADIUS1[flag],'bo',label='SE R50')
plot(s.s.fcre1[flag],s.s.FLUX_RADIUS2[flag],'go',label='SE R90')
xlabel('GALFIT R50')
ylabel('SE FLUX RADIUS')
legend(numpoints=1)
xl=arange(0,8)
plot(xl,xl,'k-')
plot(xl,xl,'k-')
plot(xl,xl+3.5,'k-')
axis([0,6,0,10])

Galaxies with OK size ratio but small R90



In [ ]:

    
truncflag=(s.s.SIZE_RATIO > 0.6) & (s.s.FLUX_RADIUS2*mipspixelscale < (0.6*s.s.PETROTH90 +3)) & s.sampleflag



In [ ]:

    
t=sort(s.s.NSAID[truncflag])
for i in t: print i, s.s.SIZE_RATIO[s.nsadict[i]],s.c90[s.nsadict[i]]



In [ ]:

    
i=72481
print s.s.SIZE_RATIO[s.nsadict[i]],s.c90[s.nsadict[i]]



In [ ]:

    
figure()
flag=s.sampleflag & ~s.agnflag
scatter(s.s.SIZE_RATIO[flag],s.c90[flag],c=log10(s.ssfr[flag]),s=60)
colorbar(fraction=.08,label='$log_{10}(sSFR)$')
xlabel('Re(24)/Re(r)')
ylabel('R90(24)/Re(24)')
axhline(y=1.34, color='k',ls='--',label='Uniform')
axhline(y=4.2, color='k',ls=':',label='Gaussian')
legend()
axvline(x=.40)



In [ ]:

    
figure()
flag=s.sampleflag & ~s.agnflag
y=s.s.fcre1*mipspixelscale/s.s.PETROTH90
scatter(s.s.SIZE_RATIO[flag],y[flag],c=log10(s.ssfr[flag]),s=60)
colorbar(fraction=.08,label='$log_{10}(sSFR)$')
xlabel('Re(24)/Re(r)')
ylabel('R90(24)/R90(r)')
xl=arange(.1,1.4,.1)
plot(xl,.55*xl,'k-')
plot(xl,.55*xl-.095,'k--')
#axhline(y=1.34, color='k',ls='--',label='Uniform')
#axhline(y=4.2, color='k',ls=':',label='Gaussian')
#legend()
#axvline(x=.40)
checkflag=(s.s.SIZE_RATIO > 0.6) & (y < (0.55*s.s.SIZE_RATIO-.095)) & s.sampleflag & ~s.agnflag
t=sort(s.s.NSAID[checkflag])
for i in t: print i, s.s.SIZE_RATIO[s.nsadict[i]],s.c90[s.nsadict[i]]

This doesn't seem like a physically significant cut in the sense that it is not picking up galaxies that I would say are truncated - meaning ok emission in the center, but it cuts off before the optical end of the galaxy.

Let's identify a list of truncated galaxies, and then see if they stand out in any size parameters. I am going to select galaxies with extended emission that is truncated relative to the stellar disk.

18234 (MKW8)
43851
69517
69610
70703
72481
72679
79360
104090



In [ ]:

    
# 99882 - undefined NUV-r color
i=s.nsadict[99882]
print s.sampleflag[i]
print s.blueflag2[i]
print s.NUVr[i],s.agnflag[i]

i=s.nsadict[163584]
print s.sampleflag[i]
print s.blueflag2[i]
print s.NUVr[i],s.agnflag[i]

going to plot NUV-r vs u-g or maybe u-r to see what NUV-r < 4 corresponds to in SDSS color space.

So 34 galaxies that have galfit fits do not have NUV data from galex. Let's find the ones that are spirals.



In [ ]: