Precision kinematics Demonstration on Bootes dSph

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13 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

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Precision kinematics
Demonstration on Bootes dSph
Sergey Koposov
Matt Walker, Vasily Belokurov,
Gerry Gilmore, Jorge Pennarubia
and others


Stellar kinematics in dwarfs


Dwarfs – most dark matter dominated systems.


The only way to find the DM mass in dwarf galaxies -
stellar kinematics (velocity dispersion).


The number of dwarfs of given mass – test of CDM


Dark matter annihilation
Radial velocity
Diemand et al. (2005)


Brief intro on dwarf galaxies
By 2000, around 15 dwarf galaxies
were known
Kinematics – high M/L – large dark
matter masses
Grebel(1999)
Mateo (1998)


Reasons to be interested in the dwarf galaxy kinematics


Number of dwarfs
can be a check of
small scale power
spectrum of DM


Can be sensitive to
the exact nature of
DM (CDM, WDM)
Moore et al.(1999)


Multiple discoveries of new dwarf galaxies
Tens new galaxies discovered: Willman et al(2005),
Belokurov et al(2006-2010), Walsh et al (2007), Irwin
et al. (2007), Zucker et al. (2006)
Courtesy of Vasily Belokurov


Substructure crisis solved ?
Simon & Geha (2007)
Need precise kinematics for mass measurements.


Other reasons to be interested in the masses of dwarfs
Dark matter
annihilation.
Need to be sure in
the DM mass if you
point large
satellite at it.
Diemand et al. (2005)


Problems with dwarf kinematics


Dynamical equilibrium (embedded in streams) ?


Binary stars


Underestimated errors


Small number of stars (faint)
Most effects only increase the observed velocity
dispersions – main reason to try to measure the velocities
precisely.


Niederste-Ostholt et al. (2009)
Strangeness of ultra-faints
Kinematic evidence: Willman et al. (2010), Aden et al.
(2009), Martin et al. (2010)
Coleman et al. (2007)


Problems with dwarf kinematics


Dynamical equilibrium (embedded in streams) ?


Binary stars


Underestimated errors


Small number of stars (faint)
Most effects only increase the observed velocity
dispersions – main reason to try to measure the velocities
precisely.


Effect of binaries
Odenkirchen et al. (2002)
(see also Minor et al. 2010)
Binaries effectively smear out
the distribution of velocities
Binaries studied to some
extend in the field in the GCs.


Problems with dwarf kinematics


Dynamical equilibrium (embedded in streams) ?


Binary stars


Underestimated errors


Small number of stars (faint)
Most effects only increase the observed velocity
dispersions – main reason to try to measure the velocities
precisely.


Martin et al. (2007)
Simon & Geha (2007)


The general idea of dwarf kinematics


Bootes dSph
Bootes dSph
Discovery: Belokurov et al.
(2006)
Luminosity: Mv ~ -6.6
Half-light radius ~ 230 pc
Distance ~ 66 kpc
Boo
Size, [pc]
M
V
Martin et al.(2007)
Belokurov et al (2007)
Belokurov et al (2006)


Okamoto (2010; PhD thesis)
Bootes dSph
Deep imaging (courtesy
of Sakurako Okamoto).


Stellar
distribution

smooth


Stellar population
consistent with single
old M92-like SP


The simplicity of the
system seems to be good
for kinematics.


Multiple epoch spectroscopy
Purposes:



Reduce and calibrate errors


Assess binarity (radial velocity variability)


Assess dynamic equilibrium


Previous measurements
First measurement:
Munoz et al (2006) ~ WYIN data, 10-
15 member stars.
Measured velocity dispersion – 6-14
km/s
Martin et al(2007) – Keck data, 30
members. Measured velocity
dispersion – 6 km/s
Munoz et al. (2006)
Martin et al. (2007)


Spectroscopy: data
VLT
GIRAFFE – fiber spectrograph
Medium resolution mode
R~8000, CaT region
120 targets
~20 Observations during one
Month period


Data demonstration
120 spectra
Most visible features belong to sky-lines


A little bit of data reduction
Data reduction – boring, but
important.
Special care – proper error-bars.
Error-bar is key for velocity
dispersion measurements.
i.

Check the errorbars produced by
latest ESO (giraf-3.8.1) pipeline –
apparently wrong. Scaling incorrect.
We have fixed that.
ii.

The more interpolation you do, the
more correlated noise you introduce.
iii.

Self calibrate the spectra using
sky-lines. Offsets from 1 to 3 km/s
were detected and corrected for.


Radial velocity determination
More or less standard here – cross-correlation IRAF routine fxcor
Disadvantages:
Usually additional interpolation to log(lambda) + fourier filtering
Does not provide proper error-bars
Not clear how to treat multiple templates
Requires continuum subtraction
Galaxy kinematics is done by direct spectral fitting
istead of cross-correlation for a long time
Rix&White(1992); Cappellari&Emsellem(2006);
Chilingarian (2007); Koleva et al. (2008)


Fitting analogous to galaxy spectra fitting Koleva et al. (2009)
Spectral library: Munari et al. (2010)
Spectral resolution: 20000
Parameters: -2.5<[Fe/H]<0.5; T
eff
=3000K-100000K
1<log(g)<5; [
α
/Fe]=0,0.4; V
rot
;
Spectral fitting
Model


,
i
,
p
j
,
v

=
P



T
i




1

v
c


P



=

j

j

2

i
,
v
,
p
j

=

i

Model


k
,
i
,
p
j
,
v


Spec
k
ESpec
k

2
We derive probability distribution for radial
velocites and best fit templates for each spectra.


Results of spectral fitting
I show the fitting of the coadded spectra.
We determine best fit template + rad. Vel. Estimate.
Best fit templates are very different for different stars
(e.g. MW dwarfs, Bootes giants, BHBs)


Parameters of best fit templates
All contamination with [Fe/H]<~-1,
log(g)>4
Teff is correlated with color


Radial velocity errors
We have multiple measurements – can compare
the scatter with the measured error-bars; check
the consistency of error-bars.
Conclusions: Our error-bars are correct within
10% level.
The precision floor of one exposure is ~ 200-300
m/s
(averaged over multiple exposures 50 m/s)
Several stars are clear outliers:

repeated



fit



Stellar variability
How to assess it ?
From Each exposure →
Probability(RV) (20 exposures)
For each star two hypothesis
Hypothesis 1: There is intrinsic RV
variability.
Hypothesis 2: The star is stationary.
Derive the bayes factors for these
hypothesis relative plausibility of →
them.
Conclusions:
Several stars clearly identified as variable.


Looking at objects with variable radial velocities
Lets look at one particular
object – RRlyra
Phased with the period from Siegel (2006)


Binaries or variable stars
Short period
fluctuation
in disk
dwarf.
Possible
Bootes long
periodic
binaries


Binary detectability
We have detected radial velocity variability in
10%
What can we say about binary fraction, and
how the velocity dispersion can be affected by
binaries ?
We can create simple
models assuming
P(separation),
P(eccentricity)
P(mass ratios)
Period, yrs
V
el, km/s


Measuring the velocity dispersion
Velocities distribution from individual epochs (without combining them)
RV distribution for the high probability members and stars with
σ
(v)<2km/s


All the stars in the sample
Best sample


Conclusions


Careful analysis and good data(multiple epochs + VLT GIRAFFE)
lead to significantly better precision.


Precise measurements lower the velocity dispersion of Bootes
down to ~2.5+/-0.7km/s (mass goes down by factor of 4).


There maybe a secondary high velocity dispersion component
(stream, binaries, non-Gaussianity of the velocity distribution)


It is hard to correct for the effect of binaries


Ultra-faints are tricky systems for kinematics and mass
measurements.


All the possible systematic effects on velocity dispersions make
them higher.


Such big difference to previous measurements suggest we
should be careful in interpreting results of kinematic analyses.