Galaxy Evolution
•! Unfortunately, we can’t hang around for billions of
years and watch galaxies evolve or study them in a
lab, so how do we measure galaxy evolution instead?
•! There are different types of galaxy evolution:
–! Morphological evolution, dynamical evolution, and number
evolution
–! Chemical evolution
–! Spectro-photometric evolution (luminosity AND color) also
known as population evolution due to the star formation
history of a galaxy and stellar evolution
Hubble Deep Field
Results from the Hubble Deep Field
•! The fraction of blue disturbed galaxies is increased
over local samples
–! But note we are observing these galaxies in the rest frame
ultraviolet
•! There are still many “normal” spirals and ellipticals,
some very red (old?)
•! Follow-up spectroscopy indicates that many are at
redshifts greater than 2, but most at z<1.4 (the
redshift desert affects this number). There is
significant large scale structure observed in the
redshift distribution.
Hubble Deep Field – zoomed in
Hubble Deep Field – with redshifts
Hubble Deep Field – zoomed in, with redshifts
Hubble Deep Field – redshift distribution
Hubble Deep Field – redshift distribution
Cohen et al 2000
Cohen et al 2000
Hubble Deep Field – redshift distribution
Hubble Deep Field – Photometric Redshifts
(Benitez 2000)
From Drew Phillips
Hubble Deep Field – Photometric Redshifts
(Benitez 2000)
Hubble Deep Field – Number counts
Great Observatories Origins
Deep Survey (GOODS)
Giavalisco et al., including Brandt @ PSU)
Image a large area (~60!HDF)
with the ACS in four filters, in
two separate areas of the sky
There are ~25,000 galaxies in
this image, which is just in the
north
Released March 9, 2004
11.3 days of observing!
K-corrections and Evolutionary Corrections
•! When we compare samples at various redshifts we need to
account for two different effects:
–! K-correction -- due to redshift effects
•! Light emitted between "e and #"e becomes light observed between
"e(1+z) and #"e(1+z), both the wavelength AND the bandpass change
–! Evolutionary correction (e) -- changes in the galaxy’s luminosity and
color between the time the light was emitted and today
–! We generally are trying to measure the evolution in a galaxy, but
need to apply the k-correction to do so
–! In a given bandpass (BP):
•! mBP = MBP + 5log(dL/10pc) + kBP + eBP
•! kBP = 2.5 log (1+z) – 2.5 log[I("/1+z)S(")d" / $I(")S(")d"] where
S(")=filter transmission curve
UDF09 – deep near-infrared image with WFC3,
finds z~7-8 galaxies
K-corrections and Evolutionary Corrections
E
•! K-corrections are large for elliptical galaxies because
they emit little flux in the UV, but smaller for spirals
and irregulars
•! K-corrections also depend on observed filter, they are
smaller as one observes further in the red, and are
negative in the near-IR
Sa
Sc
Poggianti et al., 1997
Predicting Spectrophotometric Evolution
•! We can synthesize predicted galaxy spectra as a
function of time by assuming the following:
–! Star formation rate (as a function of time)
–! Initial mass function
–! Libraries of stellar spectra for stars of different masses and
metallicities and ages, etc.
–! Stellar evolutionary tracks (isochrones)
•! A simple stellar population (SSP) is the result of an
instantaneous burst of star formation
•! We can model more complex star formation histories
by adding together multiple SSPs, parameterize star
formation rate as a function of time as:
–! dM/dt ~ exp (-t/%) where t is the time since the start of star
formation and % is the star-formation time scale
Initial mass function
•! &(m) is often approximated as a power-law
–! &(m) ~ m-(1+x) where x is the “slope”
•! x=1.35 is the Salpeter (1955) IMF from 0.1 M! to 100
M!
•! Miller-Scalo IMF (1979) as updated by Kroupa et al
(1991):
&(m)
Mass fraction
0.1<m<0.5 M!
~ m-0.85
0.31
0.5<m<1.0 M!
~ m-1.85
0.31
1.0<m<3.16 M!
~ m-3.4
0.26
3.16<m<100 M! ~ m-2.7
0.12
Initial mass function
•! &(m) = initial mass function (IMF) = number of stars formed in the
mass interval (m,m+dm) per total mass of formed stars (units =
1 /mass2), note that this could depend on t
•! '(t) = star formation rate (SFR) = total mass of stars formed per
unit time
•! The number of stars formed in the mass interval (m,m+dm) and
time interval (t,t+dt) is:
–! &(m)'(t)dmdt
•! The IMF is normalized so that $mLmU m&(m)dm = 1 where mL and
mU are the lower and upper mass cutoffs of the IMF
Predicting Spectrophotometric Evolution
SFR history vs Hubble type:
•! Ellipticals are best fit by a burst of early star formation followed
by “passive evolution” where they fade and get redder with time %
~ 1 Gyr or less
•! Spirals are best fit by % ~ 3-10 Gyr – they stay bluer and don’t
fade as much
•! Irregulars are best fit by constant star formation rates
•! Spectrophotometric synthesis pioneered by Beatrice Tinsley in
the late 1960’s (her PhD thesis!), there are several groups who
have competing synthesis models, including Gustavo Bruzual
and Stephane Charlot
Bruzual & Charlot, 1993
Bruzual & Charlot, 1993
Bruzual & Charlot, 1993
Bruzual & Charlot, 1993
Instantaneous
burst
Bruzual & Charlot, 1993
Bruzual & Charlot, 1993
Measuring Spectrophotometric Evolution
Constant SFR
•! We can measure spectrophotometric evolution by
observing samples of galaxies at different redshifts
and comparing their:
–!
–!
–!
–!
Fundamental plane relations
Tully-Fisher relations
Luminosity functions
Integrated luminosity densities (more next lecture)
Bruzual & Charlot, 1993
#m ~ 0.4 to z=0.15 – 0.75
Vogt et al., 1997
Vogt et al., 1997
Models w/ z_form = 6,3,2
Cluster @ z=1.27
van Dokkum & Stanford, 2003
van Dokkum & Stanford, 2003
Gebhardt et al., 2004 – field ellipticals
Gebhardt et al., 2004
Red: zf=1.3
Blue: zf=3, cluster
data
Gebhardt et al., 2004