The RAdial Velocity Experiment (RAVE): Third Data Release
ABSTRACT We present the third data release of the RAdial Velocity Experiment (RAVE) which is the first milestone of the RAVE project, releasing the full pilot survey. The catalog contains 83,072 radial velocity measurements for 77,461 stars in the southern celestial hemisphere, as well as stellar parameters for 39,833 stars. This paper describes the content of the new release, the new processing pipeline, as well as an updated calibration for the metallicity based upon the observation of additional standard stars. Spectra will be made available in a future release. The data release can be accessed via the RAVE Web site.
arXiv:1104.3576v1 [astro-ph.GA] 18 Apr 2011
The RAdial Velocity Experiment (RAVE): Third Data Release
A. Siebert1, M. E. K. Williams2, A. Siviero2,3, W. Reid4, C. Boeche2, M. Steinmetz2, J.
Fulbright5, U. Munari3, T. Zwitter6,7, F. G. Watson8, R. F. G. Wyse5, R. S. de Jong2, H.
Enke2, B. Anguiano2, D. Burton8,9, C. J. P. Cass8, K. Fiegert8, M. Hartley8, A. Ritter4,
K. S. Russel8, M. Stupar8, O. Bienaym´ e1, K. C. Freeman9, G. Gilmore10, E. K. Grebel11,
A. Helmi12, J. F. Navarro13, J. Binney14, J. Bland-Hawthorn15, R. Campbell16, B.
Famaey1, O. Gerhard17, B. K. Gibson18, G. Matijeviˇ c6, Q. A. Parker4,8, G. M. Seabroke19,
S. Sharma15, M. C. Smith20,21, E. Wylie-de Boer9
– 2 –
We present the third data release of the RAdial Velocity Experiment (RAVE)
which is the first milestone of the RAVE project, releasing the full pilot survey.
1Observatoire astronomique de Strasbourg, Universit´ e de Strasbourg, CNRS, UMR 7550, 11 rue de
l’universit´ e, 67000, Strasbourg, France
2Leibniz-Institut f¨ ur Astrophysik Potsdam (AIP), An der Sternwarte 16, D-14482, Potsdam, Germany
3INAF Osservatorio Astronomico di Padova, 36012 Asiago (VI), Italy
4Department of Physics and Astronomy, Faculty of Sciences, Macquarie University, NSW 2109, Sydney,
5Johns Hopkins University, Departement of Physics and Astronomy, 366 Bloomberg center, 3400 N.
Charles St., Baltimore, MD 21218, USA
6University of Ljubljana, Faculty of Mathematics and Physics, Jadranska 19, 1000 Ljubljana, Slovenia
7Center of excellence SPACE-SI, Aˇ skerˇ ceva cesta 12, 1000 Ljubljana, Slovenia
8Australian Astronomical Observatory, P.O. box 296, Epping, NSW 1710, Australia
9Research School of Astronomy and Astrophysics, Australian National University, Cotter Rd., ACT,
10Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge, CB3 OHA, UK
11Astronomisches Rechen-Institut, Zentrum f¨ ur Astronomie der Universit¨ at Heidelberg, M¨ onchhofstr. 12-
14, D-69120, Heidelberg, Germany
12Kapteyn Astronomical Institut, University of Groningen, Landleven 12, 9747 AD, Groningen, The
13Department of Physics and Astronomy, University of Victoria, P.O. box 3055, Victoria,BC V8W 3P6,
14Rudolf Peierls Center for Theoretical Physics, University of Oxford, 1 Keeble Road, Oxford, OX1 3NP,
15Sydney Institute for Astronomy, School of Physics A28, University of Sydney, NSW 2006, Australia
16Western Kentucky University, Bowling Green, Kentucky, USA
17Max-Planck-Institut f¨ ur extraterrestrische Physick, Giessenbachstrasse, D-85748, Garching, Germany
18Jeremiah Horrocks Institute, University of Central Lancashire, Preston, PR1 2HE, UK
19Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking, RH5 6NT,
20Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing, China
21National Astronomical Observatories, Chinese Academy of Sciences, Beijing, China
– 3 –
The catalog contains 83072 radial velocity measurements for 77461 stars in the
southern celestial hemisphere, as well as stellar parameters for 39833 stars. This
paper describes the content of the new release, the new processing pipeline, as well
as an updated calibration for the metallicity based upon the observation of addi-
tional standard stars. Spectra will be made available in a future release. The data
release can be accessed via the RAVE webpage: http://www.rave-survey.org.
Subject headings: catalogs, surveys, stars: fundamental parameters
A detailed understanding of the Milky Way, from its formation and subsequent evolu-
tion, to its present-day structural characteristics, remains key to understanding the cosmic
processes that shape galaxies. To achieve such a goal, one needs access to multi-dimensional
phase space information, rather than restricted (projected) properties - for example, the
three components of the positions and the three components of the velocity vectors for a
given sample of stars. Until a decade ago, only the position on the sky and the proper
motion vector was known for most of the local stars. Thanks to ESA’s Hipparcos satel-
lite (Perryman et al. 1997), the distance to more than 100000 stars within a few hundred
parsecs has been measured, allowing one to recover precise positions in the local volume (a
sphere roughly 100 pc in radius centered on the Sun). However, the 6thdimension of the
phase space was still missing until recently, when Nordstr¨ om et al. (2004) and Famaey et al.
(2005) released radial velocities for subsamples of respectively 14000 dwarfs and 6000 giants
from the Hipparcos catalog.
In recent years, with the availability of multi-object spectrometers mounted on large
field-of-view telescopes, two projects aiming at measuring the missing dimension have been
initiated: RAVE and SEGUE, the Sloan Extension for Galactic Understanding and Explo-
ration. SEGUE uses the Sloan Digital Sky Survey (SDSS) instrumentation and acquired
spectra for 240000 faint stars, 14 < g < 20.3, in 212 regions sampling three quarters of the
sky. The moderate resolution spectrograph (R∼ 1800) combined with coverage of a large
spectral domain (λλ = 3900 − 9000˚ A) allows one to reach a radial velocity accuracy of
σRV∼ 4kms−1at g ∼ 18 and 15kms−1at g = 20 as well as an estimate of stellar atmospheric
parameters. The SEGUE catalog was released as part of the SDSS-DR7 and is described
in Yanny et al. (2009). Altogether, the SDSS-I and II projects provide spectra for about
490000 stars in the Milky Way. As of January 2011, the SDSS Data Release 8 marks the first
release of the SDSS-III survey (Eisenstein et al. 2011). This release (SDSS-III collaboration
2011) provides 135040 more spectra from the SEGUE-2 survey targeting stars in the Milky
– 4 –
RAVE commenced observations in 2003 and has thus far released two catalogs : DR1
in 2006 and DR2 in 2008 (Steinmetz et al. 2006; Zwitter et al. 2008), hereafter Papers I
and II, respectively. The survey targets bright stars compared to SEGUE, 9 < I < 12,
in the southern celestial hemisphere, making the two surveys complementary. The RAVE
catalogs contain respectively 25000 and 50000 measurements of radial velocities plus stellar
parameter estimates for about half the catalog for DR2. RAVE uses the 6dF facility on
the Anglo-Australian Observatory’s Schmidt telescope in Siding Spring, Australia. This
instrument allows one to collect up to 150 spectra simultaneously at an effective resolution
of R = 7500 in a 385˚ A wide spectral interval around the near-infrared calcium triplet
(λλ8410 − 8795˚ A). The CaII triplet being a strong feature, RAVE can measure radial
velocities with a median precision of about 2kms−1.
RAVE is designed to study the signatures of hierarchical galaxy formation in the Milky
Way and more specifically the origin of phase space structures in the disk and inner Galac-
tic halo. Within this framework, Williams et al. (2011) discovered the Aquarius stream,
while Seabroke et al. (2008) studied the net vertical flux of stars at the solar radius and
showed that no dense streams with an orbit perpendicular to the Galactic plane exist in
the solar neighborhood, supporting the revised orbit of the Sagittarius dwarf galaxy by
Fellhauer et al. (2006). On the other hand, Klement et al. (2008) looked directly at stellar
streams in DR1 within 500 pc of the Sun and identified a stream candidate on an extreme
radial orbit (the KFR08 stream), in addition to three previously known phase space struc-
tures (see also Kiss et al. 2011, for an analysis of known moving groups). A later analysis
of the DR2 catalog by the same authors, using the newly available stellar atmospheric pa-
rameters in the catalog, revised their detection of the KFR08 stream, the stream being now
only marginally detected (Klement et al. 2011).
If RAVE is designed to look at cosmological signatures in the Milky Way, it is also well-
suited to address more general questions. For example, Smith et al. (2007) used the high
velocity stars in the RAVE catalog to revise the local escape speed, refining the estimate
of the total mass of the Milky Way. Co¸ skunoˇ glu et al. (2011) used RAVE to revise the
motion of the Sun with respect to the LSR, while Siebert et al. (2008) measured the tilt
of the velocity ellipsoid at 1 kpc below the Galactic plane. Veltz et al. (2008) combined
RAVE, UCAC2, and 2MASS data towards the Galactic poles to revisit the thin-thick disk
decomposition and Munari et al. (2008) used RAVE spectra to confirm the existence of the
λ8648˚ A diffuse interstellar band and its correlation with extinction.
RAVE, being a randomly-selected, magnitude-limited survey, possesses content repre-
sentative of the Milky Way for the specific magnitude interval, in addition to peculiar and
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rare objects within the same interval. Together, this makes RAVE a particularly useful cat-
alog to study the origin of the Milky Way’s stellar populations. For example, Ruchti et al.
(2010) studied the elemental abundances of a sample of metal-poor stars from RAVE to
show that direct accretion of stars from dwarf galaxies probably did not play a major role
in the formation of the thick disk, a finding corroborated by the study of the eccentricity
distribution of a thick disc sample from RAVE (Wilson et al. 2011). Also, Matijeviˇ c et al.
(2010) used RAVE to study double lined binaries using RAVE spectra while Fulbright et al.
(2010) used RAVE to detect very metal poor stars in the Milky Way. It also happens that
bright objects from nearby Local Group galaxies are observed; Munari et al. (2009), for
example, identified eight luminous blue variables from the Large Magellanic Cloud in the
So far RAVE has released only radial velocities and stellar atmospheric parameters. To
really gain access to the full 6D phase space, the distance to the stars remains a missing, yet
important, parameter, unless one focuses on a particular class of stars, such as red clump
stars (see for examples Veltz et al. 2008; Siebert et al. 2008). Combining the photometric
magnitude from 2MASS and RAVE stellar atmospheric parameters, Breddels et al. (2010)
derived the 6D coordinates for 16,000 stars from the RAVE DR2, allowing a detailed in-
vestigation of the structure of the Milky Way. This effort of providing distances for RAVE
targets was later improved by Zwitter et al. (2010), taking advantage of stellar evolution
constraints, and by Burnett et al. (2011), by using the Bayesian approach described in
Burnett & Binney (2010). The distance estimates have been used by Siebert et al. (2011)
to detect non-axisymmetric motions in the Galactic disk. These works will be extended to
DR3, distributed in a separate catalog, and will provide a unique sample to study the details
of the formation of the Galaxy. Moreover, for the bright part of the RAVE sample, the
signal-to-noise ratio per pixel allows one to estimate fairly accurate elemental abundances
from the RAVE spectra. This catalog containing of order 104stars (Boeche et al., in prep)
will provide a unique opportunity to combine dynamical and chemical analyses to understand
In this paper we present the 3rddata release of the RAVE project, releasing the radial
velocity data and stellar atmospheric parameters of the pilot survey program that were
collected during the first three years of operation, therefore DR3 includes the data collected
for DR1 and DR2. The spectra are not part of this release. These data were processed using
a new version of the processing pipeline. This paper follows the first and second data releases
described in Papers I and II. The pilot survey release is the last release relying on the original
input catalog, based on the Tycho-2 and SuperCosmos surveys. Subsequent RAVE releases
will be based on targets selected from the DENIS survey I-band. The paper is organized as
follows: Section 2 presents the new version of the processing pipeline which calculates the
– 6 –
radial velocities and estimates the stellar atmospheric parameters. Section 3 presents the
validation of the new data, as well as the updated calibration relation for metallicity, while
Section 4 describes the DR3 catalog.
2.A revised pipeline for stellar parameters
In Papers I and II we described in detail the processing pipeline used to compute the
radial velocities and the stellar atmospheric parameters, making use of a best-matched tem-
plate to measure the radial velocities and set the atmospheric parameters reported in the
catalog. This pipeline performs adequately for well-behaved spectra, permitting the mea-
surement of precise radial velocities, and we showed in Paper II that the stellar atmospheric
parameters Teff, logg, and [m/H] can be estimated. However, to compare the RAVE [m/H] to
high resolution measurements [M/H]1, a calibration relation must be used. Also, in the case
where a RAVE spectrum suffers from (small) defects, the stellar atmospheric parameters are
less well-constrained. We therefore set out to improve the pipeline, while still maintaining its
underlying computational techniques. This section reviews the modifications of the RAVE
pipeline, which is otherwise fully described in Paper II.
The RAVE pipeline for DR1 and DR2 relied on the Munari et al. (2005) synthetic
spectra library based on ATLAS 9 model atmospheres. This library contains spectra with
three different values for the micro-turbulence µ of 1, 2, and 4kms−1. However, the library is
well-populated only for the µ = 2kms−1value, about 3000 spectra having µ = 1 or 4kms−1,
compared to ∼ 55000 having µ = 2kms−1.
For this new data release (DR3), new synthetic spectra for intermediate metallicities
were added in order to provide a more realistic spacing towards the densest region of the
observed parameter space and so remove biases towards low metallicity. The new grid has
[m/H] = −2.5, −2.0, −1.5, −1.0, −0.8, −0.6, −0.4, −0.2, 0.0, 0.2, 0.4, and 0.5dex.
We also restricted the library to µ = 2kms−1, discarding all other micro-turbulence
values. This does not impact the quality of the measured stellar parameters as, at our
S/N level and resolution, we are unable to constrain the micro-turbulence, and the pipeline
1Throughout this paper, [m/H] refers to the metallicity obtained using the RAVE pipeline while [M/H]
refers to metallicity obtained using detailed analyses of high resolution echelle spectra.
– 7 –
usually converges on the most common micro-turbulence value in the library (µ = 2kms−1).
Furthermore, since the nominal resolution of the 6dF instrument does not allow us to
measure precisely the rotational velocity of the star, we chose to restrict the Vrotdimension,
removing six of the lower Vrotvalues (0, 2, 5, 15, 20, and 40kms−1), retaining only the 10,
30, 50kms−1, and higher, velocities.
Removing one dimension of the parameter space and reducing the rotational velocity
dimension helps to stabilize the solution and allows us to lower the number of neighboring
spectra used for the fit. We lower this number from 300 to 150. As for Paper II, the Laplace
multipliers for the penalisation terms were set using Monte Carlo simulations. We increased
the Laplace multiplier handling the penalisation on the sum of the weights, which constrains
the level of the continuum to unity for continuum normalized spectra, as a 0.3% offset was
not uncommon in the previous pipeline.
2.2. Signal-to-noise estimation
To date, the processing pipeline used S/N estimates as described in Paper I. However,
this S/N estimate tends to underestimate the true S/N and is less dependent on the true
noise than it is on the weather conditions or spectrum defects, such as fringing (see Paper
II). In Paper II, a new S/N estimate, S2N, was presented based on the best fit template
but was not used by the pipeline as it was an a posteriori estimate. We showed that S2N is
closer to the true S/N.
Because of the new continuum correction procedure (see Section 2.3), the S/N must be
computed correctly before the continuum correction is applied. Therefore, it must be known
prior to the processing. We thus developed an algorithm to measure the S/N of a spectrum
in which no flux information is used. This new S/N estimate, STN, is obtained using the
observed spectrum (no continuum normalization applied) as follows:
1 Smooth the observed spectrum s(i), with i the pixel index, to produce a smoothed
spectrum f(i). This smoothing is done with a smoothing box three pixels long.
2 Compute the residual vector R(i) = f(i) − s(i) and its rms σ.
3 Remove from s pixels that diverge from f by more than 2σ.
4 Smooth the clipped spectrum as above to form a new smoothed spectrum f and repeat
the clipping process until convergence.
– 8 –
5 Compute the local standard deviation σl(i) using pixels i − 1, i and i + 1.
6 Compute STN= median(s(i)/σl(i))/1.62.
The factor of 1.62 is set using numerical realizations of a Poisson noise. As shown in the
left panel of Figure 1, S/N and STN are on a 1:1 relation. However, in a real spectrum,
instrument noise also contributes to the residuals and we expect an additional normalization
factor. The S2N value as computed in Paper II follows closely the true S/N. Hence, to assess
the validity of the STN measurement, we compared it to the S2N in Paper II (Figure 1 right
panel). A correction factor of 0.58 for S2N is found to produce a 1:1 relation between the
two measurements, a correction that we apply in the pipeline.
0 50 100 150
0 50 100 150
3635 noisy synthetic spectra
3752 RAVE spectra
Fig. 1.— Comparison of the various signal-to-noise estimates. Left panel: signal-to-noise
STN compared to the original RAVE S/N. Right panel: comparison of the scaled STN to
S2N, the signal-to-noise estimator constructed for DR2.
In the low S/N regime (S/N < 10), the metallic lines are no longer visible. In this case,
[m/H] measurements converge to the highest allowed value ([m/H] = +0.5 dex) which gives
the lowest possible χ2value, i.e. the algorithm fits the noise. In this regime, the stellar
– 9 –
parameters are not reliable and are therefore not published. In the intermediate regime
10 < S/N < 50, a correlation between [m/H] and S/N is observed in the RAVE data.
While some of the above correlation is understood and arises from the change of the
underlying stellar content as one moves further away from the plane and the S/N simulta-
neously decreases2, some part of this correlation arises from to the continuum normalization
failing to recover the proper continuum level. The former pipeline uses the IRAF continuum
task with asymmetric rejection parameters (1.5σ for the low rejection level and 3.0σ for the
high rejection level). While these parameters are well-suited for the high S/N regime (> 60),
at low S/N they tend to produce an estimated continuum that is too high. This is due to
the routine considering the spikes below the continuum as spectral lines when, in fact, they
are mainly due to noise.
We ameliorate this problem by using a low rejection value that is a function of S/N. This
rejection level must be close to 1.5 for high S/N spectra and larger for low S/N. Numerical
tests indicate that using the following formula
lowrej= 1.5 + 0.2exp
with σSTN = 16, from the top left panel of Figure 2, reduces significantly the continuum
normalization problem. The top panels in Figure 2 show the mean residual between the
observed continuum-normalized spectra and best fit template as a function of S/N, before
and after the change in the low rejection level, while the bottom panels present the resulting
distributions of [m/H] as a function of S/N.
The new continuum normalization reduces significantly the correlation between metal-
licity and S/N, while no trend in the residual as a function of S/N remains. This indicates
that the new continuum normalization algorithm performs adequately, although a weak cor-
relation is still seen in the metallicity versus S/N (∼ 0.1dex per 100 in S/N).
2.4.Masking bad pixels
Approximately 20% of RAVE spectra suffer from defects such as fringing or residual
cosmic rays, which cannot be removed by the automatic procedure we use to reduce our
data. While residual cosmic rays do not affect the determination of the stellar atmospheric
2The exposure time being fixed, a lower S/N indicates a fainter magnitude.
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Fig. 2.— Top panels: average residuals best fit template−observed spectra for 4,684 RAVE
spectra as a function of S/N. Bottom panels: [m/H] distributions as a function of S/N. The
left columns are for the previous version of the continuum normalization algorithm while the
right column includes the low rejection level being a function of S/N. The gain from the new
continuum normalization is clear from these figures: the correlation between metallicity and
S/N is strongly reduced, while the residuals do not show any correlation with S/N. The thick
black line represents the STN limit below which atmospheric parameters are not published
in the RAVE catalog.
– 11 –
parameters (these are similar to emission lines, which are not taken into account in the tem-
plate library), fringing results in poor local continuum normalization, leading to inaccurate
Regions strongly affected by fringing are difficult to detect prior to the processing, but
we can make use of the best fit template to estimate whether a spectrum suffers from such
a continuum distortion and therefore whether the atmospheric parameter determination is
likely to be in error.
To estimate the fraction of a spectrum contaminated by continuum distortions, we
compute the reduced χ2(i) along the spectrum in a box 21 pixels wide centered on the
pixel i. We then also compute the mean difference S(i) between the best-fit template and
the observed spectrum in the same box. If χ2(i) > 2 and S(i) > 2/STN, a systematic
difference between the template and the observed spectrum exists. The corresponding region
of the spectrum is then flagged as a defect. The fraction of good pixels in each spectrum
is then recorded and given in the RAVE catalog (see MaskFlag in Table 12). From visual
inspection, we find that when the number of bad pixels is larger than 30% then the spectrum
is problematic and the stellar parameters should be treated with caution. Figure 3 shows
different examples of real RAVE spectra where a significant fraction of the spectrum is
marked as defect.
2.5. Improving the zero-point correction
As explained in previous papers (e.g., Paper I), thermal instabilities in the spectrograph
room induce zero point shifts of the wavelength solution that depend on the position along
the CCD (e.g., fiber number). This results in instabilities of the radial velocity zero-point.
To correct the final radial velocities for this effect, the processing pipeline uses available
sky lines in the RAVE window and fits a low-order polynomial (3rdorder) to the relation
between sky radial velocity and fiber number. This 3rdorder polynomial defines the mean
trend of zero point offsets and provides the zero point correction as a function of fiber
number3. However, in some cases, a low-order polynomial is not the best solution and a
3The zero-point correction could in principle be obtained directly from the radial velocity of the sky lines.
However the radial velocity measured from the sky lines suffers from significant errors while the trend of
the zero-point offset with respect to the fiber number due to thermal changes is expected to be a smooth
function of fiber number. Therefore, using a smooth function to recover the mean trend is better suited to
correct for zero point offsets. Tests have shown that using a 3rdorder polynomial provides in most cases the
best solution (see paper I).
– 12 –
Fig. 3.— Example of five RAVE spectra with regions marked as problematic by the MASK
code. The regions marked in grey are recognized as suffering from poor continuum normal-
ization. If more than 30% of the spectrum is marked by the code, the observation is flagged
as problematic by the pipeline. The normalized fluxes are in arbitrary units and a vertical
offset is added between the spectra for clarity.
– 13 –
constant shift should be used instead. In former releases, these cases were corrected by hand
in the catalog. In this release, we introduced a new zero-point correction routine to the
processing pipeline that is able to select which correction should be applied, automatically.
The zero-point correction now computes both the cubic correction, using the 3rdorder
polynomial, and the constant correction. It then computes the mean and standard deviation
between the measured sky radial velocities and the corrections for the entire field and for three
regions in fiber number that are contiguous on the CCD (fibers 1−50, 51−100, 101−150).
For each region, the cubic fit is used unless any of these four conditions apply:
- there are less than two sky fibers in that region, to avoid under-constrained fits,
- the mean in that region for the constant correction is better than the corresponding
mean for the cubic fit,
- the standard deviation for the cubic correction is greater than 5kms−1, which is the
case for noisy data,
- the maximum difference between the constant correction and the cubic correction is
larger than 7kms−1.
We tested the new procedure, together with other options, against pairs of repeat ob-
servations. The results are presented in Table 1. They show clearly that the new procedure
performs better than the previous version in terms of dispersion, while the mean difference
is unchanged. While the constant term correction appears better in this table, the left panel
in Fig. 4 shows that the distribution of the residuals is less peaked than for the cubic correc-
tion. In addition, the mean-square-error, defined as MSE = E[(RV − RVfit)2], shows a net
decrease with the new fitting procedure compared to a constant shift. This indicates that
for the general case, a constant correction for the entire field will result in a larger dispersion
and hence a larger zero-point offset residual. This gives us confidence in the use of the new
3.Calibration and validation
3.1.1. Internal error distribution
RAVE obtains its radial velocity from a standard cross-correlation routine. For each
radial velocity measurement the associated error, eRV, gives the internal error due to the
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Mean residual between fit and sky RVs (km/s)
MSE between fit and sky RVs (km/s)^2
Fig. 4.— Left: mean residual between the fit and the sky radial velocity for three different
fitting functions. A constant shift (black histogram), the cubic fit used in DR1 and DR2
(red histogram), and the new fitting procedure (blue histogram). Right: associated mean-
Table 1: Radial velocity difference between pairs of repeat observations using different zero-
point correction solutions. The old correction is a combination of cubic fit and corrections
applied by hand. The number of pairs used is 25,172.
Methodµ (kms−1)σ (kms−1)
No correction0.38 2.74
Old correction0.23 2.49
95% ( kms−1)
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fitting procedure. Figure 5 presents the distribution of eRV per 0.2kms−1bin for the data
new to each RAVE release. While first year data are of lower quality due to the second-
order contamination of our spectra, second and third year data are of equal quality with
a mode at 0.8kms−1, a median radial velocity error of 1.2kms−1, and 95% of the sample
having internal errors better than 5kms−1. Comparing these values to the old version of
the pipeline used for DR1 and DR2 (see Table 2 and Fig. 9 of Paper II), the new pipeline
marginally improves the internal accuracy with a gain of ∼ 0.1kms−1for the mode and the
median radial velocity error.
0 2 4 6 8 10
0 2 4 6 8 10
Number per 0.2 km/s bin
Fig. 5.— Distribution of the radial velocity error (eRV) in the 3rddata release. Top: number
of stars with eRV in 0.2kms−1bins for first-year data (dash-dotted line), second-year data
(dashed line), and third-year data (full line). Bottom: cumulative distribution of the eRV.
The dotted lines mark respectively 50, 68 and 95% of the samples.
The aforementioned error values represent the contribution of the internal errors to the
RAVE error budget. External errors are also present and are partially due to the zero-point
correction which corrects only a mean trend, not including the fiber-to-fiber variations. The
contribution of the external errors is obtained using external datasets and is discussed in