This is an outline of the results that will go into the first paper. The first paper will focus on the $$SFR-M_{*}-Size$$ relation, where $$Size \equiv R_e(24)/R_e(r).$$
In [309]:
import numpy as np
from pylab import *
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
As of 1/6/16, need to make one more pass through the sample and remove galaxies that are blended with nearby companion. Not sure if people think the numbers in each panel are useful.
Galaxies that are blended with a nearby companion are:
ALSO running this from ipython and using paperplots().
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# Now moving on to the SFR-M*-Size analysis
%run ~/Dropbox/pythonCode/LCSanalyzeblue.py
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# 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=17.7
# 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=minmass)
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RESULT: SDSS mag limit corresponds to a stellar mass cut of approximately $log_{10}(M_*) > 9.5$.
I have limited the sample to blue galaxies only, using a NUV-r color cut:
self.NUVr=self.s.ABSMAG[:,1] - self.s.ABSMAG[:,4]
self.blueflag2=self.NUVr < 4.1
Some galaxies don't have GALEX data (JM is checking into why this is the case). For these, I require u-r < 1.8.
We have a total of 138 blue star-forming galaxies with successful GALFIT fits.
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s.plotsalimcolormag()
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# For blue galaxies only
s.plotSFRStellarmassSizeBlue(blueflag=True,plotbadfits=False)
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# 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)))
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s.printsizeblue()
RESULT:
The ratio of Re(24)/Re(r) increases as galaxy environment transitions from cluster, to near field, and to field. The difference in size ratios is significant at the $3\sigma$ level.
Interestingly, the cluster and field galaxies are both consistent with the star-forming main sequence, even though the star-formation is more compact on average in the cluster galaxies. Thus, the size of the star-forming region is an important parameter to add to the SFR-Mass analysis when looking at galaxy properties as a function of environment.
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s.calc_size_starburst()
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# comparing sizes for sample with Coma removed
nc.plotSFRStellarmassSizeBlue(blueflag=True,plotbadfits=False)
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])
RESULT:
The difference in size ratios between cluster and field galaxies is not as significant once Coma is removed from the sample. The difference between the field and cluster galaxies is at the $2\sigma$ level. The conclusion is that Coma is important or unique among the clusters in the sample. Perhaps environmental effects are stronger in the more X-ray luminous environment, or maybe the sample size gets too small once Coma is removed (weak, I admit).
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:
In [380]:
# 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])
# ssfr
print ''
print 'comparing sSFR for field vs cluster'
f1=s.bluesampleflag & ~s.membflag & ~s.agnflag
m1=s.bluesampleflag & s.membflag & ~s.agnflag
t=ks(log10(s.ssfr[f1]),log10(s.ssfr[m1]))
# B/A
print ''
print 'comparing B/A for field vs cluster'
f1=s.bluesampleflag & ~s.membflag & ~s.agnflag
m1=s.bluesampleflag & s.membflag & ~s.agnflag
t=ks((s.s.SERSIC_BA[f1]),(s.s.SERSIC_BA[m1]))
# ir surface brightness
print ''
print 'comparing $L{IR}/R_e(24)^2$ for field vs cluster'
f1=s.bluesampleflag & ~s.membflag & ~s.agnflag
m1=s.bluesampleflag & s.membflag & ~s.agnflag
t=ks(log10(s.sigma_ir[f1]),log10(s.sigma_ir[m1]))
# size
print ''
print 'comparing Re(24)/Re(r) 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]))
RESULT:
Difference in size ratio between cluster and field galaxies can't be explained by systematic differences in stellar mass, B/T, sSFR, or B/A.
In [352]:
figure(figsize=(12,5))
subplot(1,2,1)
subplots_adjust(wspace=.5)
pcolor=s.s.CLUSTER_LX
pcolorlabel='$log_{10}(L_X)$'
#pcolor=s.s.SIGMA_5
#pcolor=sqrt(s.s.DR_R200**2 + s.s.DELTA_V**2)
#pcolor=10.**s.logstellarmass
#pcolor=s.massdensity
#pcolor=s.s.B_T_r
#pcolorlabel='$log_{10}(B/T)$'
f=s.bluesampleflag & ~s.agnflag & s.membflag
scatter(log10(s.sigma_ir[f]),log10(s.ssfr[f]*1.e9),c=log10(pcolor[f]),s=60)
f=s.bluesampleflag & ~s.agnflag & ~s.membflag
scatter(log10(s.sigma_ir[f]),log10(s.ssfr[f]*1.e9),c=log10(pcolor[f]),s=60,marker='^')
#f=s.bluesampleflag & ~s.agnflag & s.fieldflag
#plot(log10(s.sigma_ir[f]),log10(s.ssfr[f]*1.e9),'ks',mfc='None',label='Field')
colorbar(fraction=.08,label=pcolorlabel)
xlabel('$log_{10}(\Sigma_{ir}) $')
ylabel('$sSFR/Gyr$')
subplot(1,2,2)
#pcolor=s.s.CLUSTER_LX
#pcolorlabel='$log_{10}(L_X)$'
#pcolor=s.s.SIGMA_5
#pcolor=sqrt(s.s.DR_R200**2 + s.s.DELTA_V**2)
#pcolor=10.**s.logstellarmass
#pcolor=s.massdensity
pcolor=s.s.B_T_r
pcolorlabel='$log_{10}(B/T)$'
f=s.bluesampleflag & ~s.agnflag & s.membflag & s.gim2dflag
scatter(log10(s.sigma_ir[f]),log10(s.ssfr[f]*1.e9),c=log10(pcolor[f]),s=60)
f=s.bluesampleflag & ~s.agnflag & ~s.membflag & s.gim2dflag
scatter(log10(s.sigma_ir[f]),log10(s.ssfr[f]*1.e9),c=log10(pcolor[f]),s=60,marker='^')
#f=s.bluesampleflag & ~s.agnflag & s.fieldflag
#plot(log10(s.sigma_ir[f]),log10(s.ssfr[f]*1.e9),'ks',mfc='None',label='Field')
colorbar(fraction=.08,label=pcolorlabel)
xlabel('$log_{10}(\Sigma_{ir}) $')
#ylabel('$sSFR/Gyr$')
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In [353]:
figure()
#pcolor=s.s.CLUSTER_LX
#pcolorlabel='$log_{10}(L_X)$'
#pcolor=s.s.SIGMA_5
#pcolor=sqrt(s.s.DR_R200**2 + s.s.DELTA_V**2)
#pcolor=10.**s.logstellarmass
#pcolor=s.massdensity
pcolor=s.s.B_T_r
pcolorlabel='$log_{10}(B/T)$'
f=s.bluesampleflag & ~s.agnflag & s.membflag & s.gim2dflag
scatter(log10(s.sigma_ir[f]),log10(s.ssfr[f]*1.e9),c=log10(pcolor[f]),s=60,label='Cluster')
f=s.bluesampleflag & ~s.agnflag & ~s.membflag & s.gim2dflag
scatter(log10(s.sigma_ir[f]),log10(s.ssfr[f]*1.e9),c=log10(pcolor[f]),s=60,marker='^',label='Field')
#f=s.bluesampleflag & ~s.agnflag & s.fieldflag
#plot(log10(s.sigma_ir[f]),log10(s.ssfr[f]*1.e9),'ks',mfc='None',label='Field')
colorbar(fraction=.08,label=pcolorlabel)
xlabel('$log_{10}(\Sigma_{ir}) $')
ylabel('$log_{10}(sSFR/Gyr)$')
legend(scatterpoints=1,loc='upper left')
Out[353]:
RESULT:
Compactness of star-forming region is correlated with B/T. However, you would expect the two quantities to be correlated. The stellar mass density must increase as B/T increases, and stellar mass density is correlated with $\Sigma_{IR}$ because stellar mass correlated with SFR, and optical size correlates with IR size.
PAPER 2
Add SFRs to analysis. Focus on size, stellar mass, SFR relation
RESULTS
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%run ~/github/LCS/python/Python3/LCS_MS_rf_plots.py
used two measures of SF: SFR-Mass (main sequence), and sSFR.
for SFR vs mass and sSFR vs mass, I fit a first order polynomial to the external galaxies with $9.5 < \log(M/M_\star) < 10.5$.
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In [42]:
g.calcstats()
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g.calcstats(allgals=False)
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%run ~/github/LCS/python/Python3/LCS_MS_rf_plots.py
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# plot distance from ms vs size
g.plotsizevsmsdist()
plt.yscale('log')
plt.legend()
plt.axvline(x=0)
plt.axhline(y=1)
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constraint on disk shrinking time
use sample toy model from paper
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g.s.columns
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In [24]:
plt.figure()
plt.plot(g.s.fre1, g.s.fcre1,'k.')
plt.plot(g.s.fre1[g.sampleflag], g.s.fcre1[g.sampleflag],'bo')
plt.plot(g.s.fre1[g.agnflag], g.s.fcre1[g.agnflag],'g*')
plt.plot(g.s.fre1[~g.galfitflag], g.s.fcre1[~g.galfitflag],'co',markersize=9, mfc='None')
plt.xlabel('Re no PSF conv')
plt.ylabel('Re with PSF conv')
plt.axis([-.5,12,-.5,12])
#plt.xscale('log')
#plt.yscale('log')
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In [39]:
plt.figure()
#plt.plot(g.s.fmag1, g.s.fcre1,'k.')
plt.plot(g.s.fmag1[g.sampleflag], g.s.fcmag1[g.sampleflag],'bo')
#plt.plot(g.s.fmag1[g.agnflag], g.s.fcmag1[g.agnflag],'g*')
plt.plot(g.s.fmag1[~g.galfitflag], g.s.fcmag1[~g.galfitflag],'co',markersize=6,alpha=.3)
plt.xlabel('mag no PSF conv')
plt.ylabel('mag with PSF conv')
#plt.axis([10,18,10,18])
#plt.xscale('log')
#plt.yscale('log')
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In [57]:
plt.figure()
dr = g.s.fcre1 - g.s.fre1
dm = g.s.fcmag1 - g.s.fmag1
#plt.plot(g.s.fmag1, g.s.fcre1,'k.')
massflag = (g.logstellarmass > 9.7)
flag = g.lirflag & ~g.galfitflag & g.gim2dflag & massflag & ~g.agnflag & g.sizeflag
#plt.plot(g.s.fmag1[g.agnflag], g.s.fcmag1[g.agnflag],'g*')
plt.plot(dm[flag], dr[flag],'co',markersize=6,alpha=.3)
#plt.plot(dm[g.sampleflag], dr[g.sampleflag],'bo',alpha=.3)
plt.xlabel('fcmag1 - fmag1')
plt.ylabel('fcre1 - fre1')
plt.axis([-2,2,-15,15])
plt.axvline(x=0)
plt.axhline(y=0)
#plt.xscale('log')
#plt.yscale('log')
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sum(flag)
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