The SURVEY
Stellar mass assembly in
space and time:
CALIFA ’s perspective
Stellar Population group
CALIFA
(CALAR ALTO Legacy Integral Field Area survey)
S.F. Sánchez et al. 2012, A&A, 538, 8
Science drivers:
Model the stellar population and constrain the star formation histories
Trace the distribution of ionized gas and chemical abundances for the gas phase
Measure the stellar and gaseous kinematics
a crash tour
~ 80 members / 13 countries
Enrique Pérez
Roberto Cid Fernandes
Rosa M. González Delgado
Rubén García Benito
André L. Amorim
Sebastián F. Sánchez
P.I.
Damián Mast
Clara Cortijo
Rafael López Fernández
Bernd Husemann
CALIFA team & IAA-GRID team
P.I.: S. F. Sánchez (Granada)
P.S.: C. J. Walcher (Potsdam)
Board chair: J.M. Vilchez (Granada)
250 dark nights in 3 years:
IFU PPAK @ 3.5m CAHA
Full optical wavelength range (3700-6700 Å)
bundle: 382 fibers (2.7arcsec), 331 on galaxy
FOV: 64” x 74” 60% filling factor
3-fold dithering pattern
~2000 spectra per galaxy
Sample: 270 gxs observed!
600 galaxies 0.005 < redshift < 0.03
~20 galaxies per 1x1 mag bin in the CMD
+ diameter selection …
Instituto de Astrofísica de Andalucía (CSIC), Granada (Spain)
Universidade de Santa Catarina, Florianópolis (Brasil)
Leibniz-Institut für Astrophysik Potsdam (Germany)
1st public release: December 2012
David Axon Memorial 20130419
B. Husemann et al. 2013, A&A, 549, 87
The METHOD
Bimodal distribution of galaxies
SDSS stamps of the CALIFA sample
fossil record method of stellar population spectral synthesis
#2: Decomposing galaxy spectra: The basics...
=! M1
Lgal(!) =
$
Red sequence
Blue Cloud
http://califa.caha.es
Observables
Full spectrum:
F!
6
"
t,Z
+ M2
+ M3
+ ...
MSSP(t,Z) x SSP(!;t,Z) x e-#(!) $
SFH:
mass or light
fractions
! Pop vector
Spectral Base
SSPs from
BC03, Granada,
Pegase, “CB07”,
Vazdekis, …
Dust:
1 !V?
2!V?
!V(t,Z)?
…
The analysis pipeline
de-construct
spatial binning, etc
pre-processing
CALIFA spectral cube
The results: spectral fits : 400.000 (200 galaxies)
STARLIGHT
Cid Fernandes
re-construct
MagesDict
[[Z,t]
Z,t
,t]
!Z
(t
)
R.A.
Magebins
[[t]
t]
Mcen[zone,t]
Mcen[zone,t
Mcen[zone,
t] =
zone(y
zone(
y,x) x
y,
zone(y,x)
Magebins[t]
Magebins[ ] / area(zone)
lo
g
Mages_cube[t,y,x]
Mages_cube[t
Mages_cube[
tt,,y,
y,x] =
zone(y
zone(
y,,x) x
y
zone(y,x)
Magebins[t]
Magebins[tt] / area(zone)
Magebins[
dec.
PyCASSO
zone #
READ
Starlight
output
L(t) M(t) Z(t)
FITS
Av, v, vd
1Ma
The results: spectral fits
log(t)
13Ma
The results: spectral fits
PyCASSO Products: DOSSIER
stellar
Av
Cid Fernandes, Pérez, et al 2013
PyCASSO Products: DOSSIER
stellar
velocities
1
2
stars
Ha line emission
Position-Velocity
gas
(r.a., dec, lookbacktime)
3D Mass evolution
stellar velocity dispersion
Mass
assembly
cumulative
SFH: K0008 (Sbc)
Bimodal distribution of galaxies
SDSS stamps of the CALIFA sample
Red sequence
Blue Cloud
How Mass builds up in time
age Myr
Massive galaxies already evolved 10Gyr ago
stacking
www.faceresearch.org
Sir Francis Galton FRS, 1878
'Composite portraits made by combining those of many different persons into a single figure.'
Nature 18 : 97-100
CMD : SFH (Light)
CMD : SFH (Mass)
Mass build up
SFH: stacking by galaxy mass
nucleus
HLR
>HLR
Mass
u-r
log age
Mr
Mass build up
logM=9.5
logM=10.1
logM=10.6
logM=11.2
logM=11.6
logM=12.2
nuc
0.5HLR
HLR
>HLR
Kauffmann et al 2003
Mateus et al. 2006
Leauthaud et al 2012
Shankar et al 2006
Booth & Schaye 2012
Behroozi et al. 2012
Stinson et al. 2013
logM
SMHR
~
6x1010
~
3-10 x1010
COSMOS data and find that this mass marks
where the accumulated stellar growth of the
central galaxy has been the most efficient
P. Thomas
D. Alexander
R. Morganti
Shankar et al 2006
relative assembly rate
Pérez, Cid Fernandes, González Delgado et al.
2013, ApJL,764, L1
Thank you