Transcript Observational cosmology - galaxyformationschool2014

Olivier Le Fèvre, Laboratoire d’Astrophysique de Marseille
DEEP SURVEYS:
GALAXY FORMATION AND
EVOLUTION
1
INTRODUCTION
2
What is “Observational Cosmology” ?
Observational Cosmology is the study of the
structure, the evolution and the origin of the
universe through observation using instruments
such as telescopes
Accurate facts, measurements and their errors
No place for speculation !
3
What are “deep surveys” ?
Deep galaxy surveys are observations of a part
of the sky, assembling representative samples
of galaxies from well defined selection criteria
Two types of complementary surveys:
 Deep photometric surveys
 Deep spectroscopic redshift surveys
Surveys rely on large number statistics
4
Surveys = polls
 Ask the opinion of 1 person: always wrong
 Ask 10 persons: strong biases
 Ask 100 persons: some biases
 Ask 1000 persons: average is probably close
to truth
 …
 Votes from the whole population make the
truth
5
Ban the bad habits !!
 Astrophysics has a bad habit: generalize from
a single observation
 The goal is that you’ll leave these lectures
with a critical eye on observations presented
in the literature
6
Plan of these lectures
1. Surveys: observables
2. Surveys: methods and observations
3. The Universe on large scales
4. The mass assembly and global star
formation history
5. The most distant galaxies
6. Future Surveys
7
LECTURE #1
8
Why measure the Universe ?
 Science knows everything !
 We know the cosmological model !
So why bother ??
9
Cosmology is constantly evolving…
Dogons
Greeks
Copernicus
Modern: Big Bang
Tomorrow
?
10
Cosmological model
 Based on General Relativity
 A theoretical description
 Validated by some key observables
 Expansion of the universe
 Temperature of the microwave background
 Cosmic abundance of elements
11
12
“Accurate” cosmological
parameters show our ignorance !
Dark Matter:
26.8%
Ordinary Matter:
4.9%
Dark Energy: 68.3%
What is Dark Matter ?
What is Dark Energy ?
Need more observations !
13
Models and Simulations
 Standard CDM in a computer
 Dark matter simulations
 Add physical prescriptions on top of DM
 Semi-analytical models
 Hydro simulations
 …
14
MILLENIUM II simulation
Models need to
implement ever
increasing complexity
Cosmic Time
Simulations produce
FAKE universes !
Models are very useful
to understand main
physical processes
and interplay
15
Different models
=
Different
appearance of the
universe at
different cosmic
times
Cosmic Time
Today
Different models
BigBang
NEED Observations !
16
Tracing evolution
 Comparing the properties of galaxies at
different epochs along cosmic time allows to
derive evolution
 Caveat: we cannot follow the same galaxies,
hence we have to infer who is the progenitor
of whom
17
What do we want to measure ?
Cosmological
parameters
Galaxy Evolution
scenario
Statistical
measurements
Indirect measurements
At different redshifts:
evolution
Direct measurements
18
Deep galaxy surveys
Distribution in LSS
N(z)
Luminosity / SFR /
Mass evolution
Luminosity Function
Galaxy density field
Specifc populations
Oldest Galaxies
Luminosity Density
Strongly starforming gal.
Correlation function
SFR
QSO / AGN
Cosmological parameters
Mass function
Clusters / groups
Track evolution versus Environment, Luminosity, galaxy Type,…
Need Observations !
19
The main tracer of the
universe: Galaxies
 Galaxies are (biased) tracers of the dark
matter distribution
 The bias can be modeled (?)
 Observe galaxies and you’ll know (almost) all
about the universe
 Formation and evolution of galaxies
 Dark matter content in galaxies and clusters
 Cosmology from their distribution
20
Observables in deep surveys:
I. Direct measurements
a. Positions in space: 3D + time
b. Apparent magnitudes and flux
c. Sizes, morphology
21
Direct survey measurements:
I.a. Positions in space
 Measure the positions on the plane of the sky
 Deep images
 Measure the distances, using the redshift and
a cosmological model
 Redshift measurement
 Redshift space vs. Real space
See also the ‘cosmological distance ladder’
22
Photometry from deep images
SExtractor
Does all what you need:
astrometry, magnitudes, basic
shapes
See: Bertin and Arnouts, 1996,
A&AS, 117, 393
and
‘SExtractor for dummies’
(Beware of the ‘black box’
syndrom)
23
Measuring image
positions/astrometry
 Use first moments of
light distribution
 Deblending crucial, the
fainter the objects are
24
The Redshift
1 + 𝑧 = 𝜆𝑜𝑏𝑠 /𝜆𝑒𝑚
 The shift in observed vs.
emitted wavelength is a
consequence of motion
 Blueshift when moving
towards the observer
 Redshift when moving away
from the observer
 In an expanding Universe
objects are moving away
from each other: Redshift
 Redshift is distance: v=Hd
 Looking to a galaxy in
rotation: velocity field with
blue/red shift
25
Measuring photometric
redshifts
 Photo-z is a redshift derived




from photometric data
Use the SED (Spectral Energy
Distribution)
Correlate against a set of
templates
Same process gives *-mass,
SFR, age, etc.
Accuracy z~3-5%
 Probability distribution function
 Pb of catastrophic redshifts
26
Measure spectroscopic redshifts
Identify observed spectral
features to rest-frame
known features
 Identify emission /
absorption features
 Take continuum into
account
Cross-correlation to galaxy
templates (Tonry & Davis,
1979, AJ, 84, 1511)
27
Rest-frame spectrum
EZ engine:
Garilli et al., 2010,
PASP, 122, 827
28
Comparing photo-z and spec-z
Photo-z
Spec-z
 Accuracy dz~0.05(1+z)
 Accuracy dz~0.001
 Accurate 3D mapping
 Trained on Spec-z
 Catastrophic failures: a few %
 All objects detected in
photometry
 1 magnitude deeper than
spec-z
 Incompletness ~10-15%
 Evaluated with photo-z
 30-70% of the objects seen
in photometry
Complementary !
29
Excellent photo-z, calibrated on spec-z, Ilbert+ 13
30
Distances and Peculiar velocities
 Galaxies have a velocity
component separate
from the Hubble flow
vpec=vobs-H0d
 Particularly visible in
clusters because of high
velocity dispersion
 Finger of God effect
 Distances derived from
redshift measurements
need to be corrected for
this
31
32
I.b. Apparent magnitudes and flux
 Once objects are identified, get the total
observed flux on an image
 Sum the number of photons on detector
 Calibrated using reference sources
 Apparent magnitude m=-2.5log(Flux)+C
 SExtractor
 In a spectrum, get the flux in a spectral line
 Sum all the photons in a line
 Compute equivalent width
33
I.c. Sizes
 Apparent sizes in arcsec,
arcmin, deg
 Galaxies z>0.5: arcsecscale
 Clusters of galaxies z>0.5:
arcmin-scale
34
I.c. Morphology
 Morphology of extra-
galactic objects
 Galaxies
 Clusters/groups
 Galaxies
 Parametric
 Non-parametric
35
Parametric fit to morphology
 Represent a galaxy by a
2D model
 Typical profile for spirals
(exponential disc), and
ellipticals (r1/4 profile)
 Generalized Sersic profile
𝑙𝑛 𝐼 𝑟 = 𝑙𝑛𝐼0 − 𝑘𝑟1/𝑛
 Offers a basis to
automated classification
 Becomes complicated at
z>1
 Galaxies become mostly
irregular
See CAS (Concentration-Asymetry-Clumpiness) non-parametric classification
36
Observables in deep surveys:
II. Indirect measurements
a. Relative velocities, velocity fields, local
b.
c.
d.
e.
density
Physical sizes
Absolute luminosities and flux
Stellar masses, star formation rate, age,
metallicity, dust,…
Look-back time
37
II.a. Relative velocities, velocity
fields
 In galaxies
 Rotation, sub-
components
 Between galaxies
 Mergers, dv<500km/s
 In clusters
 Velocity dispersion gives
the cluster mass if
virialized
𝑅𝑣 2
𝑀𝑡𝑜𝑡 ~2
𝐺
38
II.a. Local density
 Density excess over
mean
 Environmentdependent properties
39
II.b. Physical sizes
 Transform observed angular
 See cosmology calculators:
http://www.bo.astro.it/~cappi/cosmo
tools
 Examples: @z=1 1deg=29Mpc
For CDM
Angular scale kpc/”
size to physical dimension at the
source : via the cosmological
model
 Use angular diameter distance
Redshift
@z=5 1deg=23Mpc
40
II.c. Absolute luminosities
 Transform apparent to absolute magnitude
Apparent
in band R
Absolute
in band Q
Distance modulus
K correction
41
II.d. Stellar mass, star formation
rate, age, dust,…
 Stellar populations add up to
produce a galaxy luminosity and
colors
 Stellar population synthesis
models aim at reproducing the
observed stellar light from
galaxies
 See Bruzual and Charlot, 2003,
MNRAS, 344, 1000
 Includes changes with age, with
metallicity
 Extinction law from dust
 Difficulties with degeneracy
 Age vs. Metallicity
 IMF and SFR laws
Synthetic spectra vs. Age
(at fixed metallicity)
42
II.d. Spectral energy distribution fit by models
Photometric measurements
SED fit with stellar population
template
Photometry: over a broad
wavelength range:
- Tracer of stellar populations
- Measurement of *-mass (red
SED)
- Measurement of star
formation (blue SED)
- Extinction
- Age (of last burst of SF)
43
II.e Look-back time
 The redshift – distance
relation is also a
distance-cosmic time
relation
 Look-back time: the
time it takes the light
to come from an object
at redshift z
44
Observables in deep surveys:
III. Statistical measurements
a. Counts N(m), N(z)
b. Luminosity Functions, Luminosity Density
and Star Formation Rate
c. Mass Functions, Mass Density
d. Correlation functions, HOD
45
III.a Counts N(m)
Count galaxies as a function of magnitude
• Depends on the band/wavelength
History: “blue galaxy counts excess”
46
III.b Luminosity function
 Luminosity Function:
counts of galaxies per
luminosity, per unit
volume
 Parametrized as a
Schechter function
 * = characteristic
density
 L*= characteristic
luminosity
 = faint end slope
47
III.b Luminosity density, SFRD
 Luminosity Density:
mean luminosity per unit
volume
 Integrate LF
 SFRD: use prescriptions
to transform flux into
star formation




UV
IR
H
…
48
III.c Mass Function and density
 Mass Function: counts of
galaxies per stellar mass,
per unit volume
 Stellar mass density:
integrate Mass Function
49
III.d Correlation Function
 Excess probability over random that a
galaxy in dV2 will be found at a
distance r12 from a galaxy in dV1
 Contains cosmological information




Small scales: redshift space distortions
Large scales: Baryon acoustic oscillations
Halo occupation
rp
 Power spectrum P(k): Fourier
Transform of Correlation function
 In practice (G: galaxy sample, R:
random sample):
 Angular CF:
 2D:
 Projected:
w()
(rp,)
50
From Sylvain de la Torre
51
III.d Correlation Function:
Redshift Space Distortions
 Deviations from
Hubble flow produce
flattening of CF on
large scales along line
of sight
 This is linked to the
growth rate of
structures
52
III.d Correlation Function: BAO
 Baryon acoustic oscillations
produced when photons
decoupled from matter at
recombination
 Leaves a signature in the CF
 Peak at ~100 h-1 Mpc
53
Observables in deep surveys:
IV. Cosmological parameters
a. Hubble constant, age of the universe
b. Density parameters
a. 
b. m
c.
b
c. Equation of state
d. …
54