Issue |
A&A
Volume 511, February 2010
|
|
---|---|---|
Article Number | A90 | |
Number of page(s) | 16 | |
Section | Stellar structure and evolution | |
DOI | https://doi.org/10.1051/0004-6361/200912471 | |
Published online | 17 March 2010 |
Distance determination for RAVE stars using stellar models
M. A. Breddels1 - M. C. Smith2,3,1 - A. Helmi1 - O. Bienaymé4 - J. Binney5 - J. Bland-Hawthorn6 - C. Boeche7 - B. C. M. Burnett5 - R. Campbell7 - K. C. Freeman8 - B. Gibson9 - G. Gilmore3 - E. K. Grebel10 - U. Munari11 - J. F. Navarro12 - Q. A. Parker13 - G. M. Seabroke14 - A. Siebert4 - A. Siviero11,7 - M. Steinmetz7 - F. G. Watson6 - M. Williams7 - R. F. G. Wyse15 - T. Zwitter16
1 - Kapteyn Astronomical Institute, University of Groningen, PO Box 800,
9700 AV Groningen, The Netherlands
2 -
Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871, PR China
3 -
Institute of Astronomy, University of Cambridge, Cambridge, UK
4 -
Université de Strasbourg, Observatoire Astronomique, Strasbourg,
France
5 -
Rudolf Peierls Centre for Theoretical Physics, Oxford, UK
6 -
Anglo-Australian Observatory, Sydney, Australia
7 -
Astrophysikalisches Institut Potsdam, Potsdam, Germany
8 -
RSAA, Australian National University, Canberra, Australia
9 -
University of Central Lancashire, Preston, UK
10 -
Astronomisches Rechen-Institut, Center for Astronomy of the University
of Heidelberg, Heidelberg, Germany
11 -
INAF, Astronomical Observatory of Padova, Asiago station, Italy
12 -
University of Victoria, Victoria, Canada
13 -
Macquarie University, Sydney, Australia
14 -
e2v Centre for Electronic Imaging, Planetary and Space Sciences
Research Institute, The Open University, Milton Keynes, UK
15 -
Johns Hopkins University, Baltimore, MD, USA
16 -
Faculty of Mathematics and Physics, University of Ljubljana,
Ljubljana, Slovenia
Received 12 May 2009 / Accepted 15 December 2009
Abstract
Aims. We develop a method for deriving distances from
spectroscopic data and obtaining full 6D phase-space coordinates for
the RAVE survey's second data release.
Methods. We used stellar models combined with atmospheric
properties from RAVE (effective temperature, surface gravity and
metallicity) and
photometry from archival sources to derive absolute magnitudes. In
combination with apparent magnitudes, sky coordinates, proper motions
from a variety of sources and radial velocities from RAVE, we are able
to derive the full 6D phase-space coordinates for a large sample of
RAVE stars. This method is tested with artificial data, Hipparcos
trigonometric parallaxes and observations of the open
cluster M 67.
Results. When we applied our method to a set of 16 146
stars, we found that 25% (4037) of the stars have relative
(statistical) distance errors of <35%, while 50% (8073) and 75%
(12 110) have relative (statistical) errors smaller than 45% and
50%, respectively. Our various tests show that we can reliably estimate
distances for main-sequence stars, but there is an indication of
potential systematic problems with giant stars owing to uncertainties
in the underlying stellar models. For the main-sequence star sample
(defined as those with
), 25% (1744) have relative distance errors <
,
while 50% (3488) and 75% (5231) have relative errors smaller than 36%
and 42%, respectively. Our full dataset shows the expected decrease in
the metallicity of stars as a function of distance from the Galactic
plane. The known kinematic substructures in the U and V
velocity components of nearby dwarf stars are apparent in our dataset,
confirming the accuracy of our data and the reliability of our
technique. We provide independent measurements of the orientation of
the UV velocity ellipsoid and of the solar motion, and they are in very good agreement with previous work.
Conclusions. The distance catalogue for the RAVE second data release is available at http://www.astro.rug.nl/ rave, and will be updated in the future to include new data releases.
Key words: methods: numerical - methods: statistical - stars: distances - Galaxy: kinematics and dynamics - Galaxy: structure
1 Introduction
The spatial and kinematic distributions of stars in our Galaxy contain a wealth of information about its current properties, its history and evolution. This phase-space distribution is a crucial ingredient if we are to build and test dynamical models of the Milky Way (e.g. Binney 2005, and references therein). More directly, the kinematics of halo stars can be used to trace the Galaxy's accretion history (Helmi & White 1999), as has been shown to good effect in many subsequent studies (e.g. Kepley et al. 2007; Smith et al. 2009; Helmi et al. 1999). There is also much to learn from the phase-space structure of the disk, where it is possible to identify substructures due to both accretion events and dynamical resonances (e.g. Helmi et al. 2006; Dehnen 2000; Famaey et al. 2005) or learn about the mixing processes that influence the chemical evolution of the disk (e.g. Roskar et al. 2008; Schönrich & Binney 2009).
To fully exploit this rich resource, we need to analyse the full six-dimensional phase-space distribution, which clearly cannot be done without a reliable estimate of the distances to the stars under consideration. Therefore obtaining accurate distances and velocities for a representative sample of stars in our Galaxy will be essential if we are to understand both the structure of our own Galaxy and galaxy formation in general.
The most dramatic recent development in this field was the Hipparcos
satellite mission (Høg et al. 2000; ESA 1997), which carried
out an astrometric survey of stars down to mag with
accuracies of up to 1 mas. This catalogue enabled the distances of
10 000 stars to be measured using the trigonometric
parallax technique, with parallax errors of less than 5%
(van Leeuwen 2007b,a).
However, in general the resulting parallaxes only probe out to a
couple of hundred parsec and are limited to the brightest stars.
This limitation of the trigonometric parallax method led researchers to attempt other techniques for calculating distances. One promising avenue is the study of pulsating variable stars, such as RR Lyraes or Cepheids, for which it is possible to accurately determine distances using period-luminosity relations (see, for example, the reviews of Gautschy & Saio 1996,1995). These have been used effectively to probe the structure of our Galaxy, in particular the study of the old and relatively metal-poor RR Lyrae stars (Vivas et al. 2001; Kunder & Chaboyer 2008; Watkins et al. 2009).
Although pulsating variables can provide accurate tracer populations, the numbers of such stars is clearly limited; ideally we would like to determine distances for large numbers of stars and not just specific populations. As a consequence there have been numerous studies utilising photometric distance determinations, where one estimates the absolute magnitude of a star from its colour. The efficacy of this method can be seen from the work of Siegel et al. (2002) and Juric et al. (2008), who both used this technique to model the stellar density distribution of the Galaxy. Another striking example of the power of this technique was presented by Belokurov et al. (2006), where halo turn-off stars were used to illuminate a host of substructures in the Galactic halo.
The strength of photometric distances is that they can be
constructed for a wide range of stellar populations. An important
recent study was carried out by Ivezic et al. (2008). In this work they
took high-precision multi-band optical photometry from the Sloan
Digital Sky Survey (SDSS; Abazajian et al. 2009) and constructed a
photometric distance relation for F- and G-type dwarfs, using colours
to identify main-sequence stars and estimate metallicity. Globular
clusters were used to calibrate their photometric relation,
resulting in distance estimates accurate to 15 per cent. This is
only possible due to the extremely well-calibrated SDSS photometry
and, in any case, is only applicable to F- and G-type dwarfs.
To determine distances for entire surveys (with a wide range of different
stellar classes and populations) requires complex multi-dimensional
algorithms. In this paper we develop such a technique to estimate
distances for stars using photometry in combination with stellar
atmosphere parameters derived from spectra.
One of the motivations behind our study is so that we can complement
the Radial Velocity Experiment
(RAVE Steinmetz et al. 2006; Zwitter et al. 2008). This project, which started
in 2003, is currently measuring radial velocities and stellar
atmosphere parameters (temperature, metallicity and surface gravity)
for stars in the magnitude range
9 < I < 12. By the time it
reaches completion in 2011 it is hoped that RAVE will have
observed up to one million stars, providing a dataset that will be of
great importance for Galaxy structure studies. A number of
publications have already made use of this dataset
(e.g. Smith et al. 2007; Munari et al. 2008; Klement et al. 2008; Veltz et al. 2008; Siebert et al. 2008),
but to fully utilise the kinematic information we crucially need to
know the distances to the stars. Unfortunately, most of the
stars in the RAVE catalogue are too faint to have accurate
trigonometric parallaxes, hence the importance of a reliable and
well-tested photometric/spectroscopic parallax algorithm.
When distances are combined with archival proper motions and high
precision radial velocities from RAVE, this dataset will provide the
full 6D phase-space coordinates for each star.
Clearly such an algorithm for estimating distances will be a vital
tool when carrying out kinematic analyses of large samples of Galactic
stars, not just for the RAVE survey but for any similar study.
The future prospects for distance determinations are very promising. In the next decade the Gaia satellite (Perryman et al. 2001) will observe up to 109 stars with exquisite astrometric precision. The mission is due to start in 2012, but a final data release will not arrive until near the end of the decade at the earliest. Furthermore, as with any such magnitude limited survey, there will be a significant proportion of stars for which their distances are too great for accurate trigonometric parallaxes to be determined. Therefore, although Gaia will revolutionise this field, it will not close the chapter on distance determinations for stars in the Milky Way and so photometric parallax techniques will remain of crucial importance.
In this paper we present our algorithm for determining distances, which we construct using stellar models. When we apply this method to the RAVE dataset we are able to reproduce several known characteristics of the kinematics of stars in the solar neighbourhood. In Sect. 2, we present a general introduction. We discuss the connection between stellar evolution theory, stellar tracks and isochrones to gain insight in these topics before presenting our statistical methods for the distance determination and testing the method using synthetic data. In Sect. 3 we apply the method to the RAVE dataset and compare the distances to external determinations, namely stars in the open cluster M 67 and nearby stars with trigonometric parallaxes from Hipparcos. Results obtained from the phase-space distribution are presented in Sect. 4 to check whether the data reflect known properties of our Galaxy. We present a discussion of the uncertainties and limitations of the method in Sect. 5 and conclude with Sect. 6.
2 Method for distance determination
2.1 Stellar models and observables
Stellar models are commonly used to estimate distances, for instance in main-sequence fitting. Such methods work for collections of stars, but models can also be used to infer properties of individual stars, such as ages (Jorgensen & Lindegren 2005; Pont & Eyer 2004; da Silva et al. 2006). In our analysis we utilise this approach, combining stellar parameters (temperature, metallicity and surface gravity) with photometry to estimate a star's absolute magnitude.
The evolution of a star is fully determined by its mass and initial
chemical composition (e.g. Salaris & Cassisi 2005).
Stellar tracks and isochrones can be
seen (in a mathematical sense) as a function (
)
of
alpha-enhancement ([
/Fe]), metallicity (
), mass
(
)
and age (
)
that maps onto the observables: absolute
magnitude (
), surface gravity (
), effective temperature
(
), and colours, i.e.
In particular, an isochrone is the function


Assuming solar -abundance,
= 0, we define the function
,
which is
with
fixed at 0,
Therefore the isochrones or stellar tracks from a given model can be seen as samples from the theoretical stars defined by

![$\ensuremath{{[\alpha{\rm /Fe}]}} =0$](/articles/aa/full_html/2010/03/aa12471-09/img41.png)
![$\ensuremath{{{\rm [M/H]}}} =\ensuremath{{{\rm [Fe/H]}}} $](/articles/aa/full_html/2010/03/aa12471-09/img42.png)
![$\ensuremath{{{\rm [M/H]}}} $](/articles/aa/full_html/2010/03/aa12471-09/img43.png)

![]() |
Figure 1:
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![[*]](/icons/foot_motif.png)
A sample of theoretical ``model stars'' from these
Y2 models are shown in Fig. 1. Each model
star is represented as a dot and the connecting lines correspond to
the isochrones of different ages. In Fig. 2 we
show the same isochrones as Fig. 1,
illustrating the relation between MJ and
,
and between MJand
separately. Clearly, for a given
,
and
it is not possible to infer a unique MJ (i.e. the function
is not injective). This can be seen most
clearly in Fig. 1, where around
= 3.8,
= 4 the isochrones overlap. However, this is also evident in
other regions; for example in the top panels of
Fig. 2 the isochrones are systematically
shifted as metallicity goes from 0 to -2. Because we are unable to
determine a unique MJ for a given star we are
forced to adopt a statistical approach, i.e. obtaining a probability
distribution for MJ.
From Fig. 2 we can see how errors in the observables
and
affect the uncertainty in the absolute magnitude (MJ in this
example). The middle row in Fig. 2 shows that the
value of MJ is better defined by
for red giant branch (RGB)
stars than for main-sequence stars, independently of their
metallicity. On the other hand, the bottom row of
Fig. 2 shows that
essentially
determines MJ for main-sequence stars, again independently of
metallicity. We therefore expect that a small error in
will
give better absolute magnitude estimates for RGB stars, while a small
error in
will have a similar effect on main-sequence stars. We
also expect this not to be strongly dependent on metallicity.
![]() |
Figure 2:
Isochrones for
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2.2 Description of the method
We now outline the method that we use to estimate the probability distribution function (PDF) for the absolute magnitude (or, equivalently, the distance). Previous studies have employed similar techniques to determine properties of stars using stellar models. A selection of such work can be found in the following references: Jorgensen & Lindegren (2005); Pont & Eyer (2004); da Silva et al. (2006).
Our method requires
a set of model stars. As was discussed in Sect. 2.1, we
have chosen to use the Y2 models (Demarque et al. 2004). We generate
our set of isochrones using the YYmix2 interpolation code. The set
consists of 600 isochrones, with 40 different ages, spaced
logarithmically between 0.01 and 15.0 Gyr, and 15 different
metallicities with 0.25 dex separation (corresponding to 1 sigma in
for the RAVE data; see Sect. 3.1) between
= -2.5 and
= 1.0. The separation between the points of
the isochrones has been visually inspected and is, in general, smaller
than the errors in
and
.
These isochrones do not track the
evolution beyond the RGB tip. We only use the isochrones with
=
0 because our observational data do not allow an accurate measurement
of
and for most of our stars we expect
.
Later, in Sect. 2.3, we show that assuming
for stars having
does not introduce any noticeable bias in
our results.
Let us suppose we have measured the following parameters for a sample
of stars:
,
,
and
.
Each of these quantities
will have associated uncertainties due to measurement errors
(
,
,
and
), which we assume are Gaussian.
For each observed star we first need to obtain the closest
matching model star, which we do by minimising the usual
statistic,
where Ai corresponds to our observable parameters (i.e. n=4 in this case) and


Having identified the most probable model, we generate 5000
realisations of the observations that could be made of this model star
by sampling Gaussian distributions in each observable that are centred
on the model values, with the dispersion in each observable equal to
the errors in that quantity. By drawing our realisations about
we
are making the assumption that the observables are just a particular
realisation of the model (e.g. Chap. 15.6 of Press et al. 1992).
Then for each such realisation we again find the most probable star by
minimising
in Eq. (3). The
final PDF is the frequency distribution of the intrinsic properties of
the model stars that have been located in this way. One may argue
that the first step of finding the closest model star is not formally
correct since it does not have a corresponding Bayesian
equivalent. However, we have found no apparent differences in the
results in tests where we exclude this step in the procedure.
We use the PDF obtained from the Monte Carlo realisations to determine
the distance. Due to the non-linearity of the isochrones, as can be
seen in Fig. 2, we expect the PDFs to be
asymmetric. In such cases the mode and the mean of the PDF are not the
same. Since the mean is a linear function, we choose to
calculate the mean and standard deviations of MJ (and distance d) from the
Monte Carlo realisations. This gives us our final determination for
the distance to each star and its associated error. We also
compared the method using the median of the distribution of absolute
magnitudes instead of the mean, and found no significant differences.
We have not made use of any priors in this analysis. We could have invoked a prior based on, for example, the luminosity function or mass function of stars in the solar neighbourhood. However, since the luminosity function of our sample is not an unbiased selection from the true luminosity function in the RAVE magnitude range (Zwitter et al. 2008), this makes the task of quantifying our prior very difficult. We therefore choose to adopt a flat prior in order to avoid any potential biases from incorrect assumptions. However, it is hoped that by the end of the RAVE survey it will have produced a magnitude limited catalogue, at which point it may become possible to invoke a prior based on the luminosity function.
2.3 Testing the method
To test the method, we take a sample of 1075 model stars. This set is large enough for testing purposes, allowing us to determine which kind of stars the method works best for. The sample of 1075 model stars are taken from a coarsely generated grid of isochrone models with metallicity![$\ensuremath{{{\rm [M/H]}}} = 0$](/articles/aa/full_html/2010/03/aa12471-09/img63.png)
![\ensuremath {{{\rm [M/H]}}}](/articles/aa/full_html/2010/03/aa12471-09/img3.png)




![$\sigma_{[\rm M/H]} = 0.25$](/articles/aa/full_html/2010/03/aa12471-09/img66.png)

The reason for choosing a fixed metallicity is twofold. In Sect. 2.1 we have seen that different metallicities should give similar results in terms of the precision with which the absolute magnitude can be derived. Secondly, it also means that the results only have to be compared to one set of isochrones, making it easier to interpret. Note that although one metallicity is used to generate the sample, after error convolution, isochrones for all metallicities are used for the fitting method.
![]() |
Figure 3:
Effect of the uncertainties in
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We run the method described in the previous section on this set of
1075 stars and analyse the results in the left column of
Fig. 3. The colours indicate the estimated errors on
MJ obtained from our algorithm and are clipped to a value
.
The middle row shows the results on a
colour-magnitude diagram (CMD). Stars
on the main sequence and on the RGB appear to have the smallest errors
as expected (see Sect. 2.1). In the bottom row, the
difference between the input (i.e. model) and
estimated magnitude is plotted against the input
magnitude of the model star from which the estimate was derived,
showing the deviation from the input absolute magnitude grows with
,
as expected. The method appears to give reasonable
results, showing no serious systematic biases. The left column of
Fig. 3 shows that for the main sequence and
RGB stars in the RAVE data set we expect a relative distance error of the
order of 25% (blue colours), and for the other stars around 50-60%
(green to red colours).
We run this procedure again, now testing the effect of reducing the
error in
.
If we decrease the error in
to 150 K, we obtain the results shown in the middle column of
Fig. 3. The errors in MJ do not seem to have changed
much, except for a very slight improvement for the main-sequence
stars. If, on the other hand, we decrease the error in
to 0.15 dex while keeping the
error at 300 K, we obtain the results shown
in the right column in Fig. 3. This shows that the
accuracy and precision with which we can determine MJ has increased
significantly. Therefore, reducing the uncertainty in
is much
more effective than a similar reduction in
and will result in
significant improvements in the estimate of the absolute magnitude. In
future, high precision photometry from surveys such as Skymapper's
Southern Sky Survey (Keller et al. 2007) may aid the ability of RAVE to
constrain the stellar parameters.
We carry out an additional test to quantify whether our decision to
only fit to
models will bias our results. To do this we
generated three similar catalogues of model stars, but with
dex. We then repeat the above procedure (as usual
fitting to models with
fixed at 0) and analyse the resulting
distances. Reassuringly we find that there is no difference between
the accuracy of the three catalogues, justifying our decision to carry
out the model fitting using only
models.
3 Application to RAVE data
3.1 Data
The Radial Velocity Experiment (RAVE) is an ongoing project measuring radial velocities and stellar atmosphere parameters (temperature, metallicity, surface gravity and rotational velocity) of up to one million stars in the Southern hemisphere. Spectra are taken using the 6dF spectrograph on the 1.2 m UK Schmidt Telescope of the Anglo-Australian Observatory, with a resolution of R = 7500, in the 8500-8750 Å window. The input catalogue has been constructed from the Tycho-2 and SuperCOSMOS catalogues in the magnitude range 9 < I < 12. To date RAVE has obtained spectra of over 250 000 stars, 50 000 of which have been presented in the most recent data release (Zwitter et al. 2008).
This second RAVE data release provides metallicity
(
),
and
from the spectra, and has been
cross-matched with 2MASS to provide J and
band
magnitudes. The
colours used for the Y2 isochrones match the 2MASS
colours very well, so no colour transformation is required (Carpenter 2001).
We choose to use the J and
bands because they are in the
infrared (IR) and are therefore less affected by dust than visual
bands. To see whether extinction will be significant for our sample we
carry out a simple test using the dust maps of Schlegel et al. (1998). If we
model the dust as an exponential sheet with scale-height 130 pc
(Drimmel & Spergel 2001), we find that given the RAVE field-of-view, a typical
RAVE dwarf located 250 pc away would suffer
0.03 mag of
extinction in the J-band. This corresponds to a distance error of
,
which is negligible compared to the overall uncertainty
inherent in our method. Reddening is similarly unimportant, with the
same typical RAVE star suffering
0.02 mag reddening in
.
Even if we only consider fields-of-view with
then we find that the extinction for a star at a distance
of 250 pc is only 0.04 mag (with corresponding distance error of
). Note that for future RAVE data releases it may be possible
to use information from the spectra to include extinction corrections
for some individual stars (Munari et al. 2008).
The observed parameter values used for the model fitting
routine are the weighted average of the available values, where
the weight is the reciprocal of the measurement error:
where Xj are the measured values and

For the RAVE data,






The current RAVE data release (Zwitter et al. 2008) does not include
individual errors for each star's derived parameters and so
for the errors in
,
and
we take 0.25 dex, 300 K
and 0.3 dex respectively. The errors in
and
are
reasonable averages for different types of stars of low temperature,
as can be seen from Fig. 19 in Zwitter et al. (2008). Even though our
error estimate is slightly smaller compared to this figure,
our results do not show evidence of an underestimation in the distance
errors (Sect. 3.3.1).
In fact, repeated observations of certain stars in the RAVE catalogue
indicate that these errors may be conservative (Steinmetz et al. 2008).
The RAVE DR2
dataset has two metal abundances, one uncalibrated, determined from
the spectra alone (
), and a calibrated value (
). The
latter is calibrated using a subset of stars with accurate metallicity
estimates and it is this value which we use in the fitting method.
As above we assume solar-scaled metallicities, which means that
and
.
3.2 Determining distances to RAVE stars
We now use the data set described above to derive absolute magnitudes using our model fitting method (see Sect. 2.2).
The RAVE second data release (Zwitter et al. 2008) contains 51 829
observations, of which 22 407 have astrophysical parameters. We
first clean up the dataset by requiring that the stars have all
parameters required by the fitting method (
,
,
,
J,
), a signal to noise ratio S2N > 20, no 2MASS photometric
quality flags raised (i.e. we require ``AAA'') and the spectrum quality
flag to be empty to be sure we have no obvious binaries or cosmic ray
problems. Although this latter flag will eliminate clear
spectroscopic binaries (132 individual stars, 0.2%), our sample must
suffer from binary contamination given the estimated
binary
fraction for F and G stars in the Copenhagen-Geneva survey
(Holmberg et al. 2009) or the much lower estimates 6-14% of Famaey et al. (2005). In future the use of repeated observations
for the RAVE sample will give a better understanding of the effect of
binaries on, for instance, the
and
estimates
(Matijevic et al. 2009).
Although most of the RAVE survey stars in this data release are
located at high latitude (with
), there are a limited
number of calibration fields with
.
We remove these
low-latitude fields from our analysis since they could suffer from
significant extinction which will bias our distance estimates.
For some stars multiple observations are available, these are grouped
by their ID, and a weighted average (Eq. (4)) and
corresponding error (Eq. (5)) for all radial
velocities are calculated. The astrophysical parameters (
,
and
)
have nominal errors as described in Sect. 3.1. For these parameters an unweighted average is
calculated but the error in the average is kept equal to the nominal
error. The total number of independent sources matching these
constraints is 16 645.
Once we have our clean sample of stars we first find the best model star as
described in Sect. 2.1. If it has a
(Eq. (3)) it is not considered
further. This last step gets rid of the
of stars that are
not well fit by any model.
Our final sample has 16 146 sources which are used for the model fitting method to obtain an estimate of the distance and associated uncertainty for each star.
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Figure 4:
Error distribution ( left) and cumulative plot ( right) for
MJ ( top) and distance ( bottom). These distributions are for the
clean sample of 16 146 stars (see Sect. 3.2).
The black line includes all the stars, while the grey line shows the
distribution for main-sequence stars (defined here as those with
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The distribution of uncertainties in the absolute magnitude and the
distance for this clean sample of 16 146 stars can be found in
Fig. 4 (black line). The x-axes are scaled such that the uncertainties can be compared using
.
The differences between the two histograms show
that the error in the apparent J magnitude does contribute to the relative distance error. In Fig. 5 we show how the
uncertainties behave for the different types of stars.
The distribution of uncertainties for the sample is as follows:
25% (4037) of the stars have relative (statistical)
distance errors of <35%, while 50% (8073) and 75% (12 110) have relative
(statistical) errors smaller than 45% and 50% respectively. For main-sequence stars (which we define here as those with
,
the grey line in Fig. 4) the distribution of uncertainties is:
25% (1744) have
relative distance errors <
,
while 50% (3488)
and 75% (5231) have
relative errors smaller than 36% and 42% respectively.
The Y2 isochrones do not model the later
evolutionary stages of stars, such as the horizontal branches and the
asymptotic giant branch. The red clump (RC), which is the horizontal
branch for Population I stars, is a well populated region in the CMD
due to the relatively long lifetime of this phase (0.1 Gyr) (Girardi et al. 1998). Therefore we expect the RAVE sample to include a non negligible fraction of RC stars. Using the selection criteria of Veltz et al. (2008) and Siebert et al. (2008), namely
and
we find about
10% of the RAVE sample could be on the RC. This region is highlighted in Fig. 5
with a black rectangle. The distance to many of these stars can be
determined using the almost constant absolute magnitude of the RC (e.g.
Siebert et al. 2008; Veltz et al. 2008).
However, since there may be better ways to isolate the RC region,
we choose to determine the distances for all these stars using our
method. Therefore, in the rest of this paper we make no distinction
between RC and RGB stars. Nonetheless, we recommend users to discard
what they believe may be RC stars, and possibly to determine their
distances using the absolute magnitude of the RC.
![]() |
Figure 5:
Results after applying the model fitting method
to the RAVE data. Colours indicate the magnitude of the error
in MJ. Only stars with
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3.3 Testing of RAVE distances
In order to verify the accuracy of our distance estimates, we perform two additional checks using external data and observations of the open cluster M 67.3.3.1 Hipparcos
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Figure 6: Bottom: distance from our method versus Hipparcos distance, the dashed line corresponds to equal distances. Top: histogram of relative distance differences between our distance and that of Hipparcos. The dashed line shows the expected distribution given the quoted errors from our method and Hipparcos. Note that the observed distribution is narrower, indicating that our errors are probably overestimated for these stars (see Sect. 3.3.1). |
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We take the reduction of the Hipparcos data as presented by
van Leeuwen (2007b,a) and cross-match these with our
RAVE stars.
In order to maximise the number of RAVE stars we use a preliminary
dataset larger than the public release described in Sect. 3.1; this dataset contains 250 000 stars, but has
not undergone the rigourous verification and cleaning of the public
data release. This cross-matching provides 624 stars for which the
Hipparcos parallax errors are less than 20% and our distance errors
are less than 50%. Note
that when dealing with uncertain trigonometric parallaxes it is well
known that the corresponding distance determinations are
systematically underestimated (Lutz & Kelker 1973). We correct for this using
the prescription described in Sect. 3.6.2 of Binney & Merrifield (1998), in
particular Eq. (3.51)
.
![]() |
Figure 7:
Relative offset in distance from our method vs. the
trigonometric parallax determination from Hipparcos, as function of
|
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In the bottom panel of Fig. 6 we show a plot of our distance estimate
(
)
vs. the Hipparcos distance (
). Clearly there
is some scatter in this distribution, but in the top panel we
quantify this by showing the distribution of
.
The curve shows the expected
distribution given our error on
and approximating the
error on
from the error on the
parallax (the true error on
is non-trivial to
calculate owing to the aforementioned Lutz-Kelker bias). It can be
seen that the predicted distribution is broader than the observed one;
if we assume our estimate of the Hipparcos errors are reasonable, this
discrepancy between the two distributions indicates that our errors
are probably overestimated.
We believe this can be explained by the fact that only the brightest
RAVE stars have trigonometric parallaxes in the Hipparcos
catalogue. These brighter stars have higher S2N than the average RAVE
stars and so the true uncertainties on the stellar parameters are
actually smaller than our adopted values. The average S2N for these
624 stars is
64, which is twice the typical S2N ratio for RAVE
stars; correspondingly the uncertainties on the stellar parameters
will be smaller by a factor of 1.3 (Sect. 4.2.4 of Zwitter et al. 2008).
We can quantify the overestimation in our distance errors for these
stars. The
clipped standard deviation of the observed
distribution is 22.1% and that of the predicted distribution is
27.8%. To give the predicted distribution the same spread as the
observed distribution would require us to decrease the distance errors from
our method for these stars by
35%.
Note that the
clipping of this distribution is necessary
since a small fraction of our distances are in significant
disagreement with Hipparcos. Of the 624 stars in this cross-matched
sample, there are 3 with distance overestimates of more than 50%,
however closer inspection shows they qualify to be RC stars (Sect. 3.2). One more star qualifies as RC star and has a distance overestimate of 40%, and one star with a
has a distance overestimate of 20%. The systematic overestimation for
possible RC stars and RGB stars is in agreement with our findings in
the next section.
In Fig. 7 we show the distribution of
as a function of the 2MASS colour
and of the three main stellar parameters (
,
[M/H],
). We see no clear systematic
trends at a level of more than
in any of the properties
shown here, which implies that our method is producing reliable
distances for main-sequence stars.
3.3.2 M 67 giants
The results from the previous section give us confidence the method works well for nearby main-sequence stars, but give us no indication of the validity of the distances to giant stars.
Our preliminary RAVE dataset includes a small number of RGB
stars which are members of the old open cluster M 67. As
the distance to M 67 is relatively well known, this makes a perfect
test case for these stars. M 67 has a distance modulus of
(m-M)V=9.70, near-solar metallicities and an age of
Gyr (VandenBerg et al. 2007).
We identify members of M 67 using the following criteria:
offset from the cluster centre of less than 0.55;
heliocentric radial velocity within 3.3
of the mean value of 32.3
(Kharchenko et al. 2005), where this value of 3.3
corresponds
to three times the uncertainty in the mean velocity;
signal to noise ratio S2N > 20;
.
A total of 8 stars pass these criteria.
In Fig. 8 we show these members, where one star is
observed twice. For these stars our method gives a distance of
kpc, more than twice the distance from the literature
(
0.8 kpc; VandenBerg et al. 2007).
Note however that the 4 stars at
qualify as RC stars as defined in Sect. 3.2. If we exclude these stars then the distance to M 67 is
kpc.
The distance estimate is now within 2 sigma of the assumed real
distance of 0.8 kpc, but still systematically overestimated.
This overestimation can be understood when one considers the
performance of
the stellar models. In the bottom panel we show the CMD of the members
with
a set of isochrones for comparison. The black isochrone
is for an age similar to that of the M 67 population (4 Gyr)
and of solar
metallicity. At least one or both of the predicted colour and absolute
magnitude of
the stars is incorrect. In the top panel we
show a plot of
vs.
,
which shows that the stars do not lie
on the isochrone in this plane either. Although the stars are within 1 or 2
from the 4 Gyr isochrone, the deviation is systematic, particularly for the brighter RGB stars. This discrepancy will clearly
impair our method and hence it is not surprising that our distances
are affected. The difficulty of obtaining isochrones that match giants
is a long standing problem that is being addressed by various authors
(e.g. VandenBerg et al. 2008; Yadav et al. 2008).
![]() |
Figure 8:
Bottom: CMD of M 67 giants on top of
theoretical solar-metallicity Y2 isochrones, with the 4 Gyr isochrone in
black. The isochrones are spaced logarithmically in age
between 0.01 to 15 Gyr. Horizontal lines indicate 1 |
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Therefore, given the limitations of the models used in this work,
our distances for stars with
should be
treated with caution. They can still be useful for analysing trends
in the data (Sect. 4),
but distances to
individual stars are likely to be inaccurate. Note as well that our
simplification to treat RC as RGB stars will lead to an overestimation
of their distance. We return to the issue
of stellar models in the discussion (Sect. 5.1).
3.4 6D phase-space coordinates for stars in the RAVE dataset
![]() |
Figure 9:
Distribution of uncertainties for velocity components
U (solid line), V (dashed line) and W (dotted
line) velocities. This corresponds to the clean sample of 16 146
stars (see Sect. 3.2). The black line includes all the
stars, while the grey line shows the distribution for main-sequence
stars (defined here as those with
|
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Besides providing distances to RAVE stars, we also provide full 6D phase-space information derived using the radial velocities (from RAVE) and the archival proper motions contained in the RAVE catalogue (from the Starnet2, Tycho2, and UCAC2 catalogues; see Zwitter et al. 2008).
We use the Monte Carlo techniques described above to calculate 6D phase-space coordinates assuming Gaussian errors on the observed quantities (radial velocities, proper motions, etc.). This is done using the transformations given by Johnson & Soderblom (1987).
The coordinate system we use is a right-handed Cartesian coordinate
system centred on the Galactic Centre (GC): the x axis is aligned
with the GC-Sun axis with the Sun located at x=-8 kpc; the y axis pointing in the direction of rotation and the z axis pointing
towards the Northern Galactic Pole (NGP). The velocities with respect
to the Sun in the directions of (x, y, z) are (U, V, W) respectively,
with the rest frame taken at the Sun (such that the Sun is at
). Our final catalogue also
includes cylindrical polar coordinates
,
defined in a Galactic rest frame such that the local standard of rest (LSR) moves at
.
To transform from the rest frame of the Sun to the Galactic rest frame, we use
for the LSR and take the velocity of the Sun with respect to the LSR to be
(Dehnen & Binney 1998). A full description of the coordinate systems is given in Appendix B.
An overview of the errors for U, V and W are shown in
Fig. 9. We find that 7139 (44% of the 16 146 ) stars have errors less
than 20
in all three velocity components, and 11 742 (73%) have errors less
than 50
.
For the main-sequence stars this is 5425 (78% of the 6975 ) and 6832 (98% of the 6975 ) respectively.
3.5 The catalogue
Our catalogue is available for download from the webpage
http://www.astro.rug.nl/ rave/ and is also hosted by the
CDS service VizieR. We aim to
update the catalogue as future RAVE data releases are issued. The
format of the catalogue is described in full in Appendix A.
4 Scientific results
The main components of our Galaxy are the bulge, the halo and the
thin and thick disks. The thin disk has a scale height of 300 pc, while the thick disk scale height is
1 kpc (e.g. Juric et al. 2008). The
disk is known to be dominated by metal rich stars, while halo
stars are in general metal poor (see Wyse (2006) for a recent review).
To see if this is reflected in the RAVE data, we will now focus on how
the metallicity and kinematics change as a function of distance from
the plane.
In Fig. 10 we show the spatial distribution of stars in the RAVE dataset, where we have restricted ourselves to stars with errors of less than 40% in distance. As expected, we see a strong concentration of stars within 1 kpc, illustrating that most of our stars are nearby disk dwarfs. However, there are also a number of stars at much larger distances, which are giants probing into the Galactic halo (although one should bear in mind that our giant distances are likely to be unreliable; see Sect. 3.3.2).
Given this large span of distances, we can investigate the change in
metallicity as we move out of the Galactic plane. Since we
still have stars with non-negligible errors in distance, this analysis
will be subject to contamination from stars at different distances, so
we show only the relevant trends in our data. The resulting
distribution of metallicity for three |z| bins is shown in
Fig. 11 for stars with relative distance error less than 75%. It is clear that most of the stars in
the |z|<1 kpc bin are consistent with a solar-metallicity thin-disk
population, but as we move away from the plane the mean metallicity
decreases. In particular, a tail of metal-poor stars is evident for
|z| > 3 kpc, consistent with a halo population. The trends that we
are seeing are similar to those seen by Ivezic et al. (2008), where the
metal-poor halo becomes apparent at
for
kpc.
![]() |
Figure 10: The RAVE stars in galactic coordinates, the circle with label GC indicates the galactic centre (which we have assumed to be at a distance of 8 kpc from the Sun). We have only plotted those stars with distance error less than 40%. |
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We now analyse the velocities of stars in our sample, restricting ourselves to
a high-quality subset of 5020 stars. For this sample we only use
those stars with distance error less than 40%, proper motion error less
than 5
(in both components) and radial
velocity error less than 5
.
In Fig. 12 we have plotted the average (where -220
corresponds to the LSR) in different bins of
|z|. It shows a decreasing rotational velocity as we move away from
the Galactic plane, which can be explained by a transition from a fast
rotating disk component, to a non-rotating (or slowly-rotating) halo.
As before, owing to our uncertainties in the giant distances, this
plot should only be used to draw qualitative conclusions.
![]() |
Figure 11: Normalised metallicity distribution for stars in different bins of height above the Galactic plane, where we are only showing stars with distance error less than 75%. As expected, stars further away from the Galactic plane are more metal poor. |
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![]() |
Figure 12:
Rotational velocity as a function of |z| for the
high-quality subset of 5020 stars (see
Sect. 4). The
error bars indicate |
Open with DEXTER |
For nearby dwarfs (
)
the errors in velocity are relatively small,
therefore we refine our sample further by considering a volume-limited
sample. We use a cylindrical volume centred on the Sun with a radius
of 500 pc and a height of 600 pc (300 above and below the Galactic
plane). This sample, which contains 3249 stars, has average
errors of
in the (U, V, W) directions, respectively.
The velocity distributions for these
stars are shown in Fig. 13 and the corresponding means
and velocity dispersions are given in Table 1. The uncertainties are
obtained by a bootstrap method. Note that these distributions will be
broadened by the observational errors, but we have not taken this into
account when calculating these variances.
For this sample, we also tabulate the full
velocity dispersion tensor
.
As has been found by
previous studies (e.g. Dehnen & Binney 1998), the
term is clearly non-zero (
km2 s-2). For this component we can calculate the vertex deviation,
![]() |
(6) |
which is a measure of the orientation of the UV velocity ellipsoid. We find





Close inspection of the middle panel of Fig. 13
shows an asymmetric distribution for the V component, with a longer
tail towards lower velocities. This is due to two effects. The first
is that we are seeing the well-known asymmetric drift, where
populations of stars with larger velocity dispersions lag behind the
LSR (Binney & Merrifield 1998).
Secondly, it is known that the velocity distribution of the
solar neighbourhood is not smooth (see, e.g. Chereul et al. 1998; Nordström et al. 2004; Dehnen 1998). This
issue is further illustrated in Fig. 14, where we show
the distribution of velocities in the UV-plane. A slight over-density
of stars around
,
can be seen which
will affect the symmetry of the V velocity component. This
over-density is called the Hercules stream, and is thought to be
due to a resonance with the bar of our Galaxy (Dehnen 2000; Fux 2001).
It should be noted that all velocities are with respect to the Sun, which implies that the Sun's U and W velocity with respect to the LSR are the negative of the mean U and W in our sample. Due to the asymmetric drift, the V velocity of the complete sample of stars is not equal to the negative of the V velocity of Sun with respect to the LSR (Binney & Merrifield 1998). The velocities and dispersions are in reasonable agreement with the results of Famaey et al. (2005) and Dehnen & Binney (1998) even though we are using different samples from those examined in these previous studies (e.g. probing different volumes or types of stars).
![]() |
Figure 13: Velocity distributions for the U, V and W components (histogram) and the best fit Gaussian (solid line) for high-quality volume-limited sample of 3249 stars (see Sect. 4). The velocity distributions for U and W are symmetric, showing a slight negative mean U and W owing to the solar motion with respect to the LSR. As expected, the V component shows an slight asymmetry, having a longer tail towards the slower rotating stars. |
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Table 1: Means, standard deviations and covariances for U, V and W velocities corresponding to the high-quality volume limited sample of 3249 stars (see Sect. 4).
![]() |
Figure 14:
The UV, UW and VW velocity distributions for the
high-quality volume-limited sample of 3249 stars (see
Sect. 4). The upper-left panel shows isodensity
contours for the UV plane, where the contours contain 2, 6,
12, 21, 33, 50, 68, 80, 90, 99 and 99.9 percent of the stars.
The red + symbol marks the LSR (Dehnen & Binney 1998)
and the green |
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5 Discussion
5.1 The influence of the choice of stellar models
![]() |
Figure 15:
A comparison of isochrones from three separate
groups: Yale-Yonsei (black), Padova (red), Dartmouth
(green). We have chosen isochrones with age 5 Gyr, Z =
0.019,
|
Open with DEXTER |
The method described in Sect. 2.2 clearly relies on the ability of stellar models to accurately predict the observed parameters. Therefore it is worth briefly discussing the potential difficulties which may arise from this assumption.
As was discussed in Sect. 2.2 we have chosen to use
the Yale-Yonsei (Y2) models (Demarque et al. 2004), but there are
several groups who make stellar models. In
Fig. 15 we compare isochrones (with age 5 Gyr,
,
)
from the following three groups:
the Y2 group (Demarque et al. 2004), the Padova group
(Marigo et al. 2008) and the Dartmouth group (Dotter et al. 2008).
The latter paper can be consulted for a more detailed
comparison of the various groups' theoretical models (see
also Glatt et al. 2008).
In general the three curves in the
plane and
plane show reasonably good agreement, certainly within the
observational errors of the RAVE data (see Sect. 3.1). The
largest discrepancy is for the cool dwarfs (
), but we do
not believe this should have any significant effect on our results as
we have very few stars in this regime.
When one considers the
plane the situation is less
satisfactory, probably due to the
-colour transformations.
To assess whether our decision to use the Y2 models has any serious effect on our results, we repeat the analysis presented in Sect. 3.3 using the Dartmouth models. We find that this has very little influence; there is no noticeable improvement for either the Hipparcos dwarfs or the M 67 giants. Therefore we conclude that our method is not particularly sensitive to the choice of stellar models. However, one should still bear in mind that, by definition, our method will be limited by any problems or deficiencies in the adopted set of isochrones.
5.2 Comparisons to other work
Klement et al. (2008, hereafter K08) have used a different method to
derive distances for RAVE stars, seemingly obtaining significantly
smaller errors than ours. They calibrated a photometric distance
relation (relating
to MV) using stars from Hipparcos catalogue with
accurate trigonometric parallaxes, combined with photometry from
Tycho-2, USNO-B and 2MASS. This method was then applied to the first
RAVE data release (DR1; Steinmetz et al. 2006).
Although the number of stars analysed by K08 is
similar to that considered here (
25 000), they obtain
7000 stars with distance errors smaller than 25%, while we have only
431 stars with distance errors smaller than 25%.
The K08 method relies on stars being on the main sequence. However,
from the values of
we can now show that of order half the RAVE
stars are giants: in Fig. 16 we show the
cumulative distribution of
,
showing that main-sequence stars
(
,
see also Fig. 1) are only
40% of the whole sample.
Therefore it is clear that a
large fraction of the RAVE sample are giants, subgiants or close to
the main-sequence turn-off.
This will undoubtedly affect the results presented in
K08. For example, their plot of the UW velocity
distribution is evidently suffering from significant systematics as can
be seen from the correlation between the U and W velocities. Previous
studies of local samples of stars have not found such a
correlation.
Our distribution of UW shows no such strong correlation
(Fig. 14) and
is consistent with 0.
Even in samples of stars out of the plane where one
might expect correlations to appear, there is no evidence for such a
pronounced level of correlation (Siebert et al. 2008).
As well as the problem of misclassified giant stars, additional
factors that will adversely affect the K08 distances include: the
metallicity distribution of the local RAVE sample will probably differ
from that of Hipparcos due to the fact that RAVE probes a different
magnitude range (and hence volume); or that K08 use the V-band which
is more prone to reddening than our choice of
.
With regard to
this latter point, we can repeat the simple analysis presented in
Sect. 2.2. For a typical star 250 pc away, given
the RAVE field-of-view the dust maps of Schlegel et al. (1998) predict
extinction of
0.1 mag in V (with corresponding distance error
of
)
and reddening of
0.1 mag in (V-H).
![]() |
Figure 16:
Cumulative distribution of
|
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6 Conclusion
We have presented a method to derive absolute magnitudes, and therefore distances, for RAVE stars using stellar models. It is based on the use of stellar model fitting in metallicity,

We find that our method reliably estimates distances for main-sequence
stars, but there is an indication of potential systematic problems
with giant stars owing to issues with the underlying stellar models.
The uncertainties in the estimated absolute magnitudes for RGB stars are
found to depend mainly on the uncertainties in
,
while for main-sequence
stars the accuracy of
is also important (Sect. 2.3). For the RAVE data the uncertainties in
and
give rise to relative distance uncertainties in the range 30%-50%,
although from cross-matching with Hipparcos (Sect. 3.3.1) it appears that our uncertainties may be
overestimated for the brighter stars (with higher signal-to-noise spectra).
It is important to note that that some 10% of the RAVE stars may
be on the red clump, but these are treated as RGB by our pipeline,
and hence their distances may be systematically biased.
As can be seen in the results section (Sect. 4), the data
accurately reflect the known properties of halo and disk stars of
the Milky Way. A variation in metallicity and
was found away
from the Galactic plane, corresponding to an increase in the fraction
of metal-poor halo stars. Existing substructure in the UV velocity
plane was recovered, as was the vertex deviation. Upon completion
the RAVE survey will have observed a factor of up to
20 times more
stars than analysed here. Clearly this will be a hugely valuable
resource for studies of the Galaxy.
In future the Gaia satellite mission (Perryman et al. 2001) will revolutionise this field, recording distances to millions of stars with unprecedented accuracy. However, for large numbers of Gaia stars it will not be possible to accurately constrain the distance due to them being too far away or too faint, which implies that it is crucial to develop techniques such as ours for reliably estimating distances.
In the near term it will be possible to improve the accuracy of our pipeline by calibrating it through observations of clusters; a technique which has been used with great success by the Sloan Digital Sky Survey (Ivezic et al. 2008). Within the RAVE collaboration a project is underway to obtain data for cluster stars (e.g. Kiss et al. 2007) and we aim to incorporate this into future analyses. This may allow us to reduce or remove the reliance on stellar models, which will lessen one of the major sources of uncertainty in our work. Our pipeline will allow us to fully utilise current surveys such as RAVE, and also places us in an ideal position exploit future large-scale spectroscopic surveys that will be enabled by upcoming instruments such as LAMOST.
AcknowledgementsWe thank the referee for useful suggestions that helped improve the paper. We also thank Heather L. Morrison and Michelle L. Wilson for their helpful suggestions. M.A.B. and A.H. gratefully acknowledge the the Netherlands Research School for Astronomy (NOVA) for financial support. M.C.S. and A.H. acknowledge financial support from the Netherlands Organisation for Scientific Research (NWO). M.C.S. acknowledges support from the STFC-funded ``Galaxy Formation and Evolution'' program at the Institute of Astronomy, University of Cambridge.
Funding for RAVE has been provided by the Anglo-Australian Observatory, by the Astrophysical Institute Potsdam, by the Australian Research Council, by the German Research foundation, by the National Institute for Astrophysics at Padova, by The Johns Hopkins University, by the Netherlands Research School for Astronomy, by the Natural Sciences and Engineering Research Council of Canada, by the Slovenian Research Agency, by the Swiss National Science Foundation, by the National Science Foundation of the USA (AST-0508996), by the Netherlands Organisation for Scientific Research, by the Science and Technology Facilities Council of the UK, by Opticon, by Strasbourg Observatory, and by the Universities of Basel, Cambridge, Groningen and Heidelberg.
The RAVE web site is at www.rave-survey.org.
Appendix A: Description of RAVE catalogue with phase-space coordinates
We present the results of our distance determinations and corresponding phase-space coordinates as a comma separated values (CSV) file, with headers. The columns are described in Table A.1. See Steinmetz et al. (2006); Zwitter et al. (2008) for a more detailed description of the RAVE data.Table A.1: A full description of the catalogue.
Appendix B: Coordinate systems
The x',y',z' coordinate system we use is a right handed Cartesian coordinate system centred on the Sun indicating positions, with the x' axis pointing from the Sun to the Galactic Centre (GC), the y' axis pointing in the direction of rotation and the z' axis pointing towards the Northern Galactic Pole (NGP). The x,y,z coordinate system is similar to the x',y',z' coordinate system, but centred on the GC, assuming the Sun is at
.
An overview can be found in Fig. B.1 with Galactic longitude (l) and latitude (b) shown for completeness.
The velocities with respect to the Sun in the directions of x, y, z are U, V, W
respectively. For velocities of nearby stars, a Cartesian coordinate
system will be sufficient, but for large distances, a cylindrical
coordinate system makes more sense for disk stars. To calculate these
coordinates, we first have to transform the U, V, W velocities to the Galactic rest frame, indicated by
vx, vy, vz as shown in Fig. B.2.
Assuming a local standard of rest (LSR) of
,
and the velocity of the Sun with respect to the LSR from Dehnen & Binney (1998), we find:
vx | = | ![]() |
(B.1) |
vy | = | ![]() |
(B.2) |
vz | = | ![]() |
(B.3) |
The relations between Cartesian (x, y, z) and cylindrical coordinates (

x | = | ![]() |
(B.4) |
y | = | ![]() |
(B.5) |
z | = | z, | (B.6) |
![]() |
= | x2+y2, | (B.7) |
![]() |
= | ![]() |
(B.8) |
We can use this to find the velocities in the directions of


![]() |
= | ![]() |
(B.9) |
![]() |
= | ![]() |
(B.10) |
Note that the direction of


![]() |
Figure B.1: Overview of the Galactic coordinates. The Sun is found at (x,y,z)=(-8,0,0). l and b are the Galactic sky coordinates. |
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![]() |
Figure B.2:
Overview of Galactic coordinate systems. U,V,W velocities are with respect to the Sun and are aligned with the x',y',z' coordinate system.
vx,vy,vz are Cartesian velocities, and
|
Open with DEXTER |
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Footnotes
- ...website
- http://www-astro.physics.ox.ac.uk/ yi/yyiso.html
- ... quantity
- Note that since J comes into
the method twice (once for
and once in the distance modulus), we draw J and
separately to ensure that the correlations are treated correctly.
- ... function
- The mean of a set of means is equal to the mean of the combined PDF.
- ... Eq. (3.51)
- A mistake is present in Eq.
(3.51) of Binney & Merrifield (1998).
The correct expression can be derived from the preceding equation,
which gives
, where
and
are the true and measured parallax respectively and
the slope of the luminosity function power law (the prior).
- ... VizieR
- http://webviz.u-strasbg.fr
All Tables
Table 1: Means, standard deviations and covariances for U, V and W velocities corresponding to the high-quality volume limited sample of 3249 stars (see Sect. 4).
Table A.1: A full description of the catalogue.
All Figures
![]() |
Figure 1:
|
Open with DEXTER | |
In the text |
![]() |
Figure 2:
Isochrones for
|
Open with DEXTER | |
In the text |
![]() |
Figure 3:
Effect of the uncertainties in
|
Open with DEXTER | |
In the text |
![]() |
Figure 4:
Error distribution ( left) and cumulative plot ( right) for
MJ ( top) and distance ( bottom). These distributions are for the
clean sample of 16 146 stars (see Sect. 3.2).
The black line includes all the stars, while the grey line shows the
distribution for main-sequence stars (defined here as those with
|
Open with DEXTER | |
In the text |
![]() |
Figure 5:
Results after applying the model fitting method
to the RAVE data. Colours indicate the magnitude of the error
in MJ. Only stars with
|
Open with DEXTER | |
In the text |
![]() |
Figure 6: Bottom: distance from our method versus Hipparcos distance, the dashed line corresponds to equal distances. Top: histogram of relative distance differences between our distance and that of Hipparcos. The dashed line shows the expected distribution given the quoted errors from our method and Hipparcos. Note that the observed distribution is narrower, indicating that our errors are probably overestimated for these stars (see Sect. 3.3.1). |
Open with DEXTER | |
In the text |
![]() |
Figure 7:
Relative offset in distance from our method vs. the
trigonometric parallax determination from Hipparcos, as function of
|
Open with DEXTER | |
In the text |
![]() |
Figure 8:
Bottom: CMD of M 67 giants on top of
theoretical solar-metallicity Y2 isochrones, with the 4 Gyr isochrone in
black. The isochrones are spaced logarithmically in age
between 0.01 to 15 Gyr. Horizontal lines indicate 1 |
Open with DEXTER | |
In the text |
![]() |
Figure 9:
Distribution of uncertainties for velocity components
U (solid line), V (dashed line) and W (dotted
line) velocities. This corresponds to the clean sample of 16 146
stars (see Sect. 3.2). The black line includes all the
stars, while the grey line shows the distribution for main-sequence
stars (defined here as those with
|
Open with DEXTER | |
In the text |
![]() |
Figure 10: The RAVE stars in galactic coordinates, the circle with label GC indicates the galactic centre (which we have assumed to be at a distance of 8 kpc from the Sun). We have only plotted those stars with distance error less than 40%. |
Open with DEXTER | |
In the text |
![]() |
Figure 11: Normalised metallicity distribution for stars in different bins of height above the Galactic plane, where we are only showing stars with distance error less than 75%. As expected, stars further away from the Galactic plane are more metal poor. |
Open with DEXTER | |
In the text |
![]() |
Figure 12:
Rotational velocity as a function of |z| for the
high-quality subset of 5020 stars (see
Sect. 4). The
error bars indicate |
Open with DEXTER | |
In the text |
![]() |
Figure 13: Velocity distributions for the U, V and W components (histogram) and the best fit Gaussian (solid line) for high-quality volume-limited sample of 3249 stars (see Sect. 4). The velocity distributions for U and W are symmetric, showing a slight negative mean U and W owing to the solar motion with respect to the LSR. As expected, the V component shows an slight asymmetry, having a longer tail towards the slower rotating stars. |
Open with DEXTER | |
In the text |
![]() |
Figure 14:
The UV, UW and VW velocity distributions for the
high-quality volume-limited sample of 3249 stars (see
Sect. 4). The upper-left panel shows isodensity
contours for the UV plane, where the contours contain 2, 6,
12, 21, 33, 50, 68, 80, 90, 99 and 99.9 percent of the stars.
The red + symbol marks the LSR (Dehnen & Binney 1998)
and the green |
Open with DEXTER | |
In the text |
![]() |
Figure 15:
A comparison of isochrones from three separate
groups: Yale-Yonsei (black), Padova (red), Dartmouth
(green). We have chosen isochrones with age 5 Gyr, Z =
0.019,
|
Open with DEXTER | |
In the text |
![]() |
Figure 16:
Cumulative distribution of
|
Open with DEXTER | |
In the text |
![]() |
Figure B.1: Overview of the Galactic coordinates. The Sun is found at (x,y,z)=(-8,0,0). l and b are the Galactic sky coordinates. |
Open with DEXTER | |
In the text |
![]() |
Figure B.2:
Overview of Galactic coordinate systems. U,V,W velocities are with respect to the Sun and are aligned with the x',y',z' coordinate system.
vx,vy,vz are Cartesian velocities, and
|
Open with DEXTER | |
In the text |
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