A&A 392, 851-863 (2002)
DOI: 10.1051/0004-6361:20021014
H. Meusinger1,
-
R.-D. Scholz2,
- M. Irwin3 -
H. Lehmann1,
1 - Thüringer Landessternwarte Tautenburg, 07778 Tautenburg, Germany
2 -
Astrophysikalisches Institut Potsdam, An der Sternwarte 16, 14482
Potsdam, Germany
3 - Institute of Astronomy, Madingley Road, Cambridge CB3 1HA, UK
Received 5 June 2002 / Accepted 27 June 2002
Abstract
We present results of the spectroscopical follow-up
observations of QSO candidates from a combined variability
and proper motion (VPM) survey in a 10 square degrees region
centered on the globular cluster M 3. The search is based on a
large number of digitised Schmidt plates with a time-baseline
of three decades. This paper reviews the candidate selection,
the follow-up spectroscopy, and general
properties of the resulting QSO sample.
In total, 175 QSOs and Sey1s were identified among the
objects from the VPM survey, with 114 QSOs and 10 Sey1s
up to the pre-estimated 90% completeness limit of the survey at
.
The redshift range of the QSOs
is 0.4 <z<3. Among the 80 QSO candidates
of highest priority we confirm 75 QSOs/Sey1s and 2 NELGs.
We present magnitudes, colours, redshifts, and variability
indices for all 181 identified QSOs/Sey1s/NELGs and
spectra for the 77 QSOs/Sey1s/NELGs from our spectroscopic
follow-up observations. The VPM
survey uses selection criteria that are not directly relying
on the spectral energy distribution of QSOs. It is
therefore remarkable that the properties of the VPM QSOs
do not significantly differ from those of samples from
colour selection or slitless spectroscopy.
In particular, we do not detect a substantial number of
unusually red QSOs.
The total surface density of the brighter QSOs
(
)
in our search field is found to be by a factor of
1.8
larger than that derived from previous optical surveys.
Key words: galaxies: active - galaxies: Seyfert - galaxies: statistics - galaxies: quasars: general
The variability and proper motion (VPM) survey is a QSO search project
that is based on optical long-term variability and non-detectable
proper motions. Variability of flux densities and
stationarity of positions are two fundamental properties of quasars,
and therefore well suited as selection criteria of a QSO search
(e.g., Kron & Chiu 1981; Hawkins 1983; Majewski et al. 1991;
Véron & Hawkins 1995; Bershady et al. 1998). However,
due to the special demands on the number and the
time-baseline of the available observations such attempts must be limited to
comparatively small and confined areas.
We performed the VPM technique in two Schmidt fields of 10 square
degrees each on the basis of a large number of altogether more than 200 digitised
Tautenburg Schmidt plates in the B band with a time-baseline of three decades
(Meusinger et al. 1997, 2002).
It is not the primary aim of this project to increase the number of known QSOs by an insignificant fraction; the problems of detecting substantial numbers of QSOs have long been overcome. Over the last decade, among others, the Durham/AAT survey (Boyle et al. 1990), the Large Bright Quasar Survey (Hewett et al. 1995), the Edinburgh Quasar Survey (Goldschmidt & Miller 1998), and the Hamburg/ESO survey (Wisotzki et al. 2000) have been completed. Presently, the 2dF Quasar Survey (Croom et al. 2001) and the Sloan Digital Sky Survey (Schneider et al. 2002) are extremely efficient at identifying very large numbers of quasars. The INT Wide Angle Survey (Sharp et al. 2001) is expected to detect a statistically significant sample of high-redshift quasars. Very deep quasar samples were obtained in the Lockman hole via the X-ray satellite ROSAT (Hasinger et al. 1998) and in the optical domain with the Hubble Space Telescope (e.g., Conti et al. 1998), respectively. Further, the VLA FIRST Bright Quasar Survey (e.g., White et al. 2000) will define a radio-selected QSO sample that is competitive in size with current optically selected samples.
Most of the criteria for the selection of QSO candidates rely upon differences in the broad-band spectral energy distribution of QSOs and stars. Despite the large number of QSOs now catalogued, the selection effects of the conventional surveys are not yet fully understood. It is therefore important to perform QSO surveys that are based on different selection methods. The VPM survey does not directly invoke the spectral energy distribution as the primary selection criterion and provides therefore an interesting opportunity to evaluate the selection effects of more conventional optical QSO searches. For instance, a serious question concerns the possible existence of a substantial population of red QSOs. Extinction-reddened QSOs are suggested both from the AGN unification model (e.g., Antonucci 1993; Maiolino 2001) and from the hypothesis of ultra-luminous IR galaxies (ULIRGs) as QSOs in the making (Sanders et al. 1988). The vast majority of catalogued QSOs have uniform spectral energy distributions with a blue continuum and broad absorption lines. Over the last few years, QSOs with extreme red colours have been detected on the basis of their X-ray emission (e.g., Risaliti et al. 2001) or by radio surveys (Webster et al. 1995; Francis et al. 2000; White et al. 2000; Menou et al. 2001; Gregg et al. 2002). The fraction of unusually red objects among the whole QSO population is however unknown. When compared to other optical surveys, the VPM technique has the advantage that it can discover such red QSOs as long as (1.) they are not too faint in the B band and (2.) they are not much less variable than the conventional QSOs.
The VPM survey was started in the high-galactic latitude field
around M 3 (Meusinger et al. 1995). Half of this field
is covered by the CFHT blue grens survey (e.g., Crampton et al.
1990). The CFHT QSOs could serve as a training set and were
used to define the selection
thresholds for the VPM survey in such a way that
a 90% completeness is expected up to
.
The strategy, the observational material, and the data reduction
for the M 3 field were presented in detail in Paper I
(Scholz et al. 1997).
The procedures and results for the second search field, around M 92,
are described in a series of papers (Brunzendorf & Meusinger 2001,
2002; Meusinger & Brunzendorf 2001, 2002).
A brief review of the whole VPM project is given by Meusinger et al.
(2002).
The present paper presents the QSO sample in the M 3 field.
In Sect. 2, we briefly discuss the candidate selection.
The spectroscopic follow-up observations are
described in Sect. 3. Section 4 gives an overview of the properties
of the QSO sample. Conclusions are given in Sect. 5. As in the
previous papers, we adopt H0 = 50 km s-1 Mpc-1 and q0=0.
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Figure 1:
Proper motion index
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The VPM search is based on indices for star-like image structure, positional stationarity, overall variability, and long-term variability measured on 57 B plates taken with the Tautenburg Schmidt plates between 1964 and 1994, i.e. with a time-baseline of three decades. The B magnitudes given in the present paper are mean magnitudes from this database. The strategy for the QSO candidate selection of the VPM search in the M 3 field and the definitions of the indices are outlined at length in Paper I. Here we review only the basic ideas and describe the modifications in the selection procedures.
The proper motion index,
,
is expressed simply by the measured proper motion in units
of the proper motion error. The overall variability index,
,
is assessed by the deviation of the individual magnitudes about the mean
magnitude, and is normalised by the average magnitude scatter
for star-like objects in the same magnitude range. Finally, an
index for long-term variability,
,
is defined
by means of structure function analysis and is computed for all
star-like objects with
(see below) and B<20.
The selection thresholds for the indices were derived
from the statistics of the previously known QSOs in the field.
There are 90 such QSOs identified with star-like objects measured on
at least 7 of our B plates.
We found that a good compromise between the success rate
(i.e., the fraction of candidates that turn out to be QSOs)
and the completeness (i.e., the fraction of all QSOs found by
the survey) is achieved for the following set of constraints:
,
and
.
(In Paper I, the long-term variability
index was denoted RS100.) The pre-estimated values
for the success rate and the completeness are 90% and 40%,
respectively, for a limiting magnitude
.
(Note that the limiting magnitudes of the individual B plates
vary from 19.5 to 21.3.)
For stationary objects, the probability
to measure a
non-zero proper motion follows a Weibull-distribution and
depends only on the proper motion index (Brunzendorf & Meusinger
2001). An object with
has a probability
of
for non-zero proper motion.
As illustrated by Fig. 1, the proper motion
selection is in particular efficient for brighter magnitudes
where the proper motion errors are smaller. For B < 18.5,
the typical proper motion error is about 1 mas yr-1, and
83% of the star-like objects have
.
For 19<B<20, on the other hand, the typical proper motion
error is about 3 mas yr-1, and only about 21% of the star-like
objects have
.
In a flux-limited sample, most of the objects have magnitudes
close by the limit. Hence, the proper motion selection appears
not very efficient for the whole survey with a limit at
.
However, at brighter magnitudes, the number-magnitude relation
for QSOs is much steeper than for the foreground stars.
This means that the contamination of the variability-selected
QSO candidate sample by foreground stars is stronger at
brighter magnitudes where the proper motion selection works
more efficiently. At fainter magnitudes,
the zero proper motion constraint is important in particular
for the efficient rejection of nearby variable late-type
main sequence stars (see Paper I).
The selection starts with 32 700 objects detected on a deep master
plate. About 24 600 objects were identified on at least two further plates,
among them are about 21 500 objects with star-like images.
A basic object sample for the variability selection is defined
by the 12 800 star-like objects measured on at least 7 B plates.
After excluding
the objects in the crowded cluster region (distance to the centre
of M 3 less than 24'), this sample is reduced to 8582 objects
in total and to 4614 objects in the magnitude range
,
respectively. About 65% of the objects from this reduced sample
are rejected due to the zero proper motion constraint
.
Finally, the variability constraints strongly reduce the candidate
sample to a manageable size.
priority class | high | medium | low |
number of candidates | 80 | 95 | 607 |
already catalogued | 26 | 17 | 15 |
newly observed | 54 | 68 | 27 |
QSOs/Sey1s | 75 | 36 | 20 |
NELGs | 2 | - | 1 |
stars | 3 | 49 | 21 |
For practical reasons, the candidate sample is devided into
three subsamples of different priority. A similar approach was
used for the VPM search in the M 92 field
(cf. Brunzendorf & Meusinger 2001). However, the
variability indices defined there are slightly different from
those used in the present study, and the priority classification
in the two VPM fields are not completely identical.
Here, the priority depends mainly on the variability indices.
In addition, the B magnitudes and the crowding of the
field (expressed by the distance
from the centre
of M 3) are taken into account. For all priority classes,
star-like objects are considered with
,
and
arcmin. The high-priority subsample
consists of the strongly variable objects
with
.
The medium-priority subsample contains the objects with
smaller variability indices
and
.
In addition, we included the few objects
with somewhat higher variabiliy indices (
and
)
in the stronger crowded region
arcmin.
Finally, the low-priority subsample comprises
the objects having only one
of the two variability indices above the threshold
(i.e.,
or
).
In addition, we consider also objects with
and
arcmin as low-priority
candidates if at least one of the two variability indices
exceeds the threshold.
The variability selection is illustrated by Fig. 2.
As discussed in Paper I, the U band variability index may serve as an additional selection criterion. In practice, however, the fainter objects are measured on only a small number of U plates. Therefore, the U variability index was invoked only in one case: the QSO No. 51 from Table 3 has insignificant B variability indices but shows significant variability in the U band.
The numbers of selected candidates are 80, 95, and 607, respectively, for the subsamples of high, medium, or low priority (Table 1). It is expected that the fraction of QSOs/Sey1s strongly decreases with decreasing priority. In particular, the low-priority subsample is expected to be strongly contaminated by galactic stars with relatively enhanced photometric errors.
A cross-check of the candidate list against the
NED (2002, February)
yields the identification (identification radius 10 arcsec)
of 57 QSOs/Sey1s and one narrow emission line galaxie (NELG)
with catalogued redshifts.
Over the whole magnitude range, 104 objects with catalogued
redshifts z>0 were identified (100 QSOs/Sey1s, 4 NELGs).
The overwhelming fraction of the QSOs/Sey1s are from the CFHT
blue grens survey (e.g., Crampton et al. 1990)
which covers approximately half of our survey field.
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Figure 2:
Long term variability index
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Spectroscopic follow-up observations have been focussed on the candidates of high or medium priority. Most of the spectra were taken during five observing runs either with the 2.2 m telescope on Calar Alto or with the Tautenburg 2 m telescope. An overview of these observation runs is given in Table 2. In addition, three candidates with uncertain spectroscopic identification were re-observed in July 2001 with CAFOS on Calar Alto; this run is quoted as number 6 in Table 3. An additional 18 candidates of medium or low priority were proved to be foreground stars during several other campaigns with either the Tautenburg telescope or the Calar Alto 2.2 m telescope.
The brighter candidates (B<18) were observed with TAUMOK in the
Schmidt focus of the Tautenburg 2 m telescope.
TAUMOK allows to obtain simultaneously
spectra of up to 35 objects
within an area of
diameter (see Meusinger & Brunzendorf
2001 for more details).
The telescope was operated in a scanning mode prior to the
spectroscopic observations in order to determine the most accurate
positions of the fibres.
The wavelength coverage is approximately 3800-9000 Å, the
reciprocal linear dispersion is 400 Å mm-1 corresponding
to 9 Å per pixel.
The atmospheric conditions during the TAUMOK campaign were moderate.
In five of the seven nights, spectra of QSO candidates were taken.
Four different fibre configurations were necessary to cover the
VPM field. Several 1800 s exposures were taken for each configuration.
The total exposure time per field is between 1.5 and 3 hours.
Since the number of bright high-priority
candidates is much smaller than the total number of available
object fibres, most of the fibres were positioned at candidates
of lower priority or on non-priority objects. Five fibres were reserved for
template spectra from known QSOs with redshifts beteween z=0.6and 2.5. Internal spectral lamps were used for the wavelength calibration
prior and after the observation of a field.
For the reduction of the TAUMOK spectra we applied a
software package (Ball 2000) which is based on
IRAF standard procedures for multi-object spectroscopy.
observing run | 1 | 2 | 3 | 4 | 5 |
spectrograph | TAUMOK | CAFOS | CAFOS | CAFOS | CAFOS |
year/month | 1997/04 | 1998/04 | 1999/04 | 2000/04 | 2001/03 |
number of nights | 7 | 7 | 5 | 3 | 3 |
number of objects | 41 | 46 | 34 | 23 | 35 |
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Figure 3:
Field of the VPM survey centred on the globular cluster M 3.
Star-like objects are shown as grey dots. The panel on the left hand side
shows the distribution of the QSOs from the VPM search (![]() ![]() ![]() |
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The total integration time varied between 10 and 90 min, dependent primarily on the strength of the emission lines and on the weather conditions. Observations were made in a wide variety of atmospheric conditions. The weather was good in the 2001 observing campaign. In the previous runs, however, the fraction of observing time with good atmospheric transparency was rather low. Therefore, about half of all spectra have only a moderate signal-to-noise ratio. Several objects had to be observed in more than one run. Data reduction was performed using the long-slit spectroscopy package LONG of MIDAS. Wavelength calibration was done by means of calibration lamp spectra.
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Figure 4:
Absolute magnitude ![]() ![]() ![]() ![]() |
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Spectra were taken for a total of 198 objects: 1.) In particular, all 54 high-priority candidates without classification in the NED were observed. 2.) Among the 95 medium-priority candidates, 17 objects have a measured redshift in the NED. For an additional 68 medium-priority objects spectra were taken in the framework of the present study. The remaining 10 objects have variabilities near the selection thresholds (Fig. 2d) and/or are located at the borders of the field (Fig. 3) where the contamination by foreground stars is obviously stronger (see below). The chance to find QSOs among these remaining 10 candidates is substantially lower than for the median-priority subsample as a whole. 3.) The number of low-priority candidates is obviously too large to reach a substantial completeness with regard to spectroscopic follow-up observations. We selected therefore from this priority class mainly the brighter objects ( 18< B <19.5) and/or the objects with the strongest indication for variability. Note that the high fraction of QSOs/Sey1s among the observed low-priority objects is thus not representative for the whole subsample. There may be some undetected QSOs among the remaining low-priority objects, but their number is expected to be small.
The statistics of the observations for the various priority classes are summarised in Table 1. Note that the total number of observed candidates listed there is smaller than the number of all observed objects given in Table 2. The reasons for this apparent discrepancy are the following: (a) the criteria for the definition of priorities have slightly changed during the survey. (b) Many of the brighter objects observed with TAUMOK are not candidates in the sense of Table 1, but were selected to allow a good positioning of the fibres. (c) Several objects were observed in more than one observing run. (d) Since a variability survey is expected to be biased against low-variability QSOs, we selected also a few objects with quasar-typical colours, but with variability indices slightly below the selection thresholds. For instance, four objects were observed because they are X-ray sources. (e) A few strongly variable objects with 19.7<B<19.9 have been observed as well.
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Figure 5:
Normalised distribution of the redshifts (number of QSOs per 0.2
redshift bin) for
a) the subsample from the present follow-up spectroscopy
(Table3), and
b) the (nearly) complete subsample of all 114 QSOs with ![]() |
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The spectral classification is based on the emission and absorption line properties. Three catagories are considered: (1.) redshifted broad emission lines and/or absorption lines, (2.) redshifted narrow emission lines, and (3.) unredshifted typical stellar absorption lines.
The first category comprises QSOs and Sey1s, which are
discriminated by the usual luminosity threshold
.
The absolute magnitude
is computed for
H0 = 50 km s-1 Mpc
-1, q0 = 0 and the
-correction from Brunzendorf & Meusinger (2001).
The data for the 69 QSOs and 5 Sey1s from the present study are listed in
Table 3,
and in the following, objects will be quoted with
their number in this table. For most of these objects, redshifts
were derived from several emission lines; in particular, strong
narrow forbidden lines
(e.g., [O III]
)
were used, if present.
The wavelength of a single line was measured by Gaussian centroids.
When we selected the first QSO candidate list in 1996, this list has been checked against the NED (cf. Paper I) to reject all objects already catalogued with measured redshifts. A new check revealed that six QSOs from Table 3 are catalogued in the February 2002 release of the NED. For three of these objects (No. 16, 68, 70) redshifts were published later than 1996. (Number 68 is identical with FBQS J1348+2840, White et al. 2000.) The other three (No. 29, 36, 42) had uncertain positions in the 1996 NED and therewith too large position differences (>10 arcsec) for an unambiguous identification. Further, we note that the QSO No. 11 is identified with the radio source (without z in the NED) FIRST J133825.6+283637.
There are four narrow-emission line galaxies (NELGs)
among the identified objects from the NED. An additional three NELGs were
detected in the present study and are also listed in
Table 4.
The luminosities of the NELGs are
clearly below the QSO-Sey1 threshold. In general,
the class of the NELGs includes Seyfert 2s, narrow-line Seyfert 1s,
LINERs, and H II galaxies.
For this paper we have not attempted to separate the types of NELGs.
One of the new NELGs (No. 43) is a high-priority QSO candidate, another one (No. 26) is of low priority, but with a high overall
variability index
.
The third one (No. 15) has a high
proper motion index and is not a QSO candidate from the VPM survey, but
is one of the X-ray sources observed for completing the QSO sample.
Among all objects with z>0 from our basic sample, No. 15
is the only one with a proper motion index significantly larger
than the selection threshold
(Fig. 1),
perhaps indicating a wrong spectral classification from a noisy spectrum.
All 7 NELGs were classified as star-like objects
on the Schmidt plates; the infered redshifts are between 0.137 and 0.433.
In the frame of the VPM search in the M 92 field, a higher fraction of
NELGs was detected due to a less stringent star-galaxy separation
(Meusinger & Brunzendorf 2001). The high variability indices
measured for the NELGs were explained by increased photometric errors for
objects with image profiles deviating from stellar ones (Meusinger &
Brunzendorf 2002).
Finally, an object is classified as a foreground star if its spectrum unambiguously shows typical un-redshifted stellar absorption lines. At a first glance, most of these objects are normal stars without unusual spectral features. Contrary to the QSOs, the classified stars show a remarkably inhomogeneous distribution over the field (Fig. 3): their strong concentration towards the outer parts and the corners of the field indicates an increase of the instrumental variability at large distances from the plate centre. Such an effect is in principle expected since we have not corrected for a position-dependence of the magnitude scale (Paper I). This interpretation implies that a substantial fraction of the selected stars are not really variables. In this context we note that most of the stars have lower variability indices than the QSOs (Fig. 2d).
To summarise, we have plausibly classified all 198 objects from our spectroscopic follow-up observations as either QSOs/Sey1s, NELGs, or foreground stars. There are new redshifts for 68 broad-lined objects and 3 narrow-lined objects. For an additional 6 already catalogued QSOs/Sey1s redshifts were confirmed.
Table 3
lists redshifts, absolute magnitudes, colours,
proper motion indices, and the two variability indices of
the 77 QSOs, Sey1s, and NELGs from our follow-up spectroscopy.
In a similar style, Table 4
summarises the data for
the 104 QSOs, Sey1s, and NELGs identified in the NED. The distribution
of these types over the three priority classes from the VPM survey
is given in Table 4.
In the high priority subsample, 94% of the candidates were found
to be QSOs/Sey1s, while the contamination by foreground stars is
as low as 4%. For the combined sample of high-and-medium-priority
objects the success rate (i.e., the fraction of established QSOs/Sey1s among
all candidates) is still as high as 63%.
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Figure 6:
Cumulative QSO surface density ![]() ![]() |
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Figure 7:
Spectra (normalized flux
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Figure 2 illustrates that a high fraction of all QSOs
in the field are strongly variable. The B standard deviation due to
variability is about 0.2 mag for QSOs with B<19.7. In this magnitude
range, more than 60% of the QSOs/Sey1s show the strong variability of
high-priority VPM candidates. (A detailed analysis of the
variability properties will be deferred to a separate study.)
For 90% of QSOs/Sey1s, both variability indices are above the
selection thresholds. We find that 50 out of the 53 NED
QSOs/Sey1s with B<19.7 match the selection criteria of our survey,
corresponding to a completeness of 94% for the VPM survey.
Only for two objects both variability indices fall below the selection
thresholds; another one has a proper motion index slightly above the
threshold.
The subsample of the 114 QSOs with B<19.7 is considered nearly complete.
These QSOs are homogeneously distributed over the search field
(Fig. 3). In particular, the
QSO surface density in the northern half of the field, which
is not covered by the CFHT blue grens survey, is comparable to that in the
southern part where almost all known QSOs are in the CFHT survey.
An additional three QSOs were detected by the VPM search
in the CFHT field. Two of them (No. 5, 15) have "normal'' spectra and
were obviously ignored by chance in the CFHT survey; the other one (No. 7)
shows very strong broad absorption line (BAL) features.
The subsample is of course flux-limited, and
is therefore
strongly correlated with z (Fig. 4). Only for
,
the subsample is complete with regard to luminosities
(since B<19.7 for QSOs of such z). Note that most of the
objects with z<0.55 shown in Fig. 4 are Sey1s.
The redshift distribution is shown in Fig. 5
both for (a) the subsample from Table 3
and (b) the sample of
all identified QSOs with .
The shape of
the z distribution is roughly comparable with that from the SDSS Quasar
Catalogue I. Early Data Release (Schneider et al. 2002),
corroborating the result from VPM survey in the M 92 field
(Brunzendorf & Meusinger 2002).
This impression is confirmed by the two-tailed KS two-sample test.
According to this test on a significance level
,
we have
not to reject the null hypothesis that our subsamples (a) and (b)
and the SDSS sample were drawn from the same population.
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Figure 8:
Colour-colour diagram for the M3 field. QSOs/Sey1s are shown as
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Figure 9:
Colour-redshift relations for the the QSOs/Sey1s in the M3 field.
Panels a), b) show the objects from Table3
(![]() ![]() |
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The "completeness'', or absolute efficiency, of the survey can
be estimated by comparing the QSO surface densities, i.e. number counts
per solid angle, to the densities predicted by other surveys.
Figure 6 shows the surface density of all QSOs
(i.e.,
)
with B<19.7 in our search field,
compared with mean relations from
various data samples. The cumulative density N(<B) is simply computed by
dividing the number of QSOs brighter than a given magnitude by the
effective search area where the B magnitudes were corrected for an
interstellar extinction of
mag.
The size of the
Schmidt field is
3. After subtracting the
areas of the plate margins
(not shown in Fig. 3), the calibration wedge, the crowded inner
part of M 3, and the area covered by the images of the objects
in the remaining field, the effective search area amounts to 7.8 deg2.
The resulting number-magnitude relation is roughly described
by
with
for
17.5<B<18.5and
for
18.5<B<19.5, in agreement with
the result from the M 92 field
(Brunzendorf & Meusinger 2002). The surface densities
for our total QSO sample are higher than those
derived by Hartwick & Schade (1990), especially at brighter
magnitudes. There are 9 QSOs with B<18 in our search field,
corresponding to 1.15 QSOs deg-2 mag-1, i.e.
a factor of 1.8 more than in the Hartwick & Schade data.
More recently, La Franca & Cristiani (1997)
derived surface densities of 0.76 QSOs deg-2 0.5 mag-1for
and
,
to be compared with
1.28 QSOs deg-2 0.5 mag-1 for our sample.
The surface densities based on single-epoch observations are affected
by variability and cannot be compared directly to those
based on time-averaged magnitudes. It should be noticed however
that Hartwick & Schade corrected their data for such a
variability-induced over-completeness. Note also that
the different assumptions for q0 (both Hartwick & Schade and
La Franca & Cristiani adopted q0=0.5 while we used q0=0)
make no significant difference for the number counts.
We cannot exclude that the relative overabundance of apparently
bright QSOs is due to the limitations of small-number
statistics, but note that a similar result was
found for the VPM survey in the M 92 field
(Meusinger & Brunzendorf 2001).
The low-resolution spectra of the objects from Table 3
are shown in Fig. 7. The spectra are dominated by the typical
AGN-emission lines:
Ly+N V
,
Si IV+O IV]
,
C IV
,
C III]
,
Mg II
,
[O III]
,
and the Balmer series.
A few objects (Nos. 10, 19, 64, 50, 11, 69, 7) apparently have relatively
weak lines. Unfortunately, many of these spectra were taken at
relatively bad atmospheric transparency, and poorly
removed telluric lines may effect the equivalent widths of
the QSO emission lines.
Therefore we do not quantitatively discuss the
distribution of the equivalent widths in this paper.
The analysis of the QSOs from the VPM survey in the M 92 field
(where most of the spectra were taken under
better weather conditions) has shown that the sample-averaged
line equivalent widths for the VPM QSOs are in good agreement
with those from other samples of radio-quiet QSOs
(Meusinger & Brunzendorf 2001).
Broad absorption troughs are indicated in the spectra of the
QSOs Nos. 3, 49, 37, 57, 7, 44, 75, 33, 65, and 70. For some other QSOs
absorption features may be hidden due to the low
signal-to-noise. The fraction of BAL QSOs is about 10%, in good
agreement with the
BAL percentage in the SDSS Early Data Release (Schneider et al.
2002). There is only one object with an unusual spectrum:
the BAL QSO No. 7 where the emission lines are almost completely
masked by extremely broad absortion lines. The best guess for the
emission redshift is
,
compared to
for the strongest absorption lines
(C IV
,
Al III
,
and the
Fe II-multiplet at
). This object is a high-priority
VPM QSO candidate with quite red colours (see below). There is no entry in
the NED at this position. Objects like No. 7 are not very
likely to be recognized by most other optical QSO surveys.
For a few other QSOs/Sey1s, the spectra in Fig. 7
have unusually red continua (Nos. 42, 64, 48, 3).
However, the U-B indices (Table 3) of these objects
closely follow the mean colour-redshift relation (Fig. 9)
and thus the missing blue light in the spectra is interpreted by
the slit-loss effect due to atmospheric dispersion (Sect. 1).
We conclude that, up to the limit of the survey, the fraction of QSOs
with unusual spectra is at maximum a few percent. This conclusion is
again in agreement with the statistics from the (still incomplete) SDSS
data (Hall et al. 2002).
In Paper I, a colour-colour diagram of the QSO candidates was presented showing a broad scatter of their colour indices and a large fraction of red QSO candidates. The distribution of the spectroscopically classified objects on the U-B versus B-V plane is shown in Fig. 8. The most important result is the fact that all candidates with extremely red colours proved to be foreground stars, in agreement with what we found from the VPM survey in the M 92 field. Obviously, the QSOs from Table 3 populate essentially the same area as the QSOs from the CFHT grens survey. For z<2.2 this area is well defined by the selection criteria of classical colour surveys. There are only 7 low-redshift (z<2.2) QSOs located beyond the demarcation line for colour selection discussed in Paper I. A typical fluctuation of about 0.35 mag per colour index is expected due to photometric errors and variability (as the time-lines for the three colour bands are not identical). In addition, a scatter may be produced by intrinsic differences in the continuum slope and/or the strength of emision lines and/or absorption troughs. Remarkably, the strongest deviation from the colour selection line is measured for the two absorption line QSOs Nos. 7 and 75.
The same conclusion is reached from the colour-redshift relations (Fig. 9). Apart from the scatter due to variability and photometric errors, the QSOs from our sample closely follow the mean relation of QSOs from the Véron-Cetty & Véron (2001) catalogue. Among the QSOs from Table 3, the strongest deviation is measured again for Nos. 7 and 75. The QSO No. 28, which is the faintest object in Table 3, shows a strong deviation in B-V. Fainter QSOs tend to have larger colour indices B-V (Fig. 9d).
Data from the 2 Micron All Sky Survey (2MASS; Skrutskie
et al. 1997), March 2000 data release are available
for 25% of the field. With an identification radius of 10 arcsec
we identified six QSOs from our whole sample with catalogued 2MASS sources.
For all six
objects the
colour index is smaller than 4, i.e.
smaller than for the red QSOs found by Webster et al. (1995)
among the flat-spectrum radio-loud QSOs.
For the M 92 field we have estimated that the fraction of
QSOs with unusually red B-V colour indices must be less than 3% up
to B=19.8 (Brunzendorf &
Meusinger 2002). From the data in the M 3 field we
estimate a similar fraction of about 2% up to B=19.7.
Although a survey in the B-band is obviously not an ideal
approach to derive strong conclusions about the underlying population of
possibly highly reddened QSOs, the unbiased VPM QSO sample
provides a constraint of its properties.
Let us assume for simplicity that there are
two QSO populations of comparable size: normal QSOs and reddened QSOs with an
intrinsic dust reddening equivalent to
EB-V = 0.5 - a not
unreasonable level in a dusty system - implying an extinction of about
2 mag in the B-band (assuming a galactic extinction curve). Using
the number-magnitude relation from Fig. 6, we would expect to
detect about 5-7 strongly reddened QSOs up to
in each
search field. This is clearly more than what we found,
indicating that the red QSOs are either redder on average or less frequent.
A more detailed
discussion of this question has to be deferred to a separate study.
We performed a VPM QSO survey with a limiting magnitude of
in a 10 square degrees field at high galactic latitude. The VPM
technique proved to be an efficient method for finding QSOs.
As the result of the spectroscopic follow-up observations of
198 candidates and the identification of further candidates in the NED,
a sample of 175 QSOs/Sey1s with 0.4<z<3 is available.
With a stellar contamination of only about 4%, the high-priority
QSO subsample from the VPM search is very clean.
For the combined sample of high-and-medium-priority
objects the fraction of established QSOs/Sey1s among
all candidates is still as high as 63%.
The completeness of the VPM QSO sample with
is estimated to be
94%. The number-magnitude relation for that sample is in good agreement
with the one expected from the relation derived by Wisotzki (1998)
from various QSO samples. At brighter magnitudes (B<18.5), we find a
somewhat higher QSO surface density.
The optical broad-band colours and the spectra of the
VPM-selected QSOs are not significantly different from those of QSOs
selected by other optical surveys, in agreement with what we found
in the M 92 field (Meusinger & Brunzendorf 2001;
Brunzendorf & Meusinger 2002).
Such a result can not be a priori expected since the selection criteria of the
VPM survey are completely different from those in
most other optical surveys.
Although there is a large fraction of objects with red colours among the
VPM QSO candidates, all candidates with extremely red colours were proved to be
stellar contaminants. We estimate that the fraction of QSOs with unusualy
red optical colours is at most a few per cent up to the limit of the survey,
provided that their variability properties are not significantly different from those
of the other QSOs. Some BAL QSOs are known to be considerably redder
than the targets of most QSO surveys (e.g.,
Weymann et al. 1991;
Menou et al. 2001;
Hall et al. 2002).
The fraction of such unusual QSOs
in the (incomplete) SDSS Early Data Release is less than 1%
(Hall et al. 2002), in good agreement with our result.
In this context it is notable that all VPM QSOs with indication
for substantial absorption
are strongly variabel (
). A VPM search is thus
expected to be essentially unbiased against strongly absorbed QSOs,
apart from the bias introduced by the band-pass of the search.
The general agreement of the properties of the VPM QSO sample with
those from more conventional optical surveys
suggests that the latter do obviously
not ignore a substantial number of red QSOs up to
.
On the
other hand, we can conclude that the VPM survey can be combined with
colour search criteria in order to reach a very high efficiency without
a significant loss of completeness. Of course, we can not exclude the
existence of substantial numbers of obscured red QSOs that are fainter
than the current survey limit. Such objects can be found by a deeper
VPM survey.
Acknowledgements
This research is based on observations made with the 2.2 m telescope of the German-Spanish Astronomical Centre, Calar Alto, Spain. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
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Figure 7: continued. |
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Figure 7: continued. |
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Figure 7: continued. |
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Figure 7: continued. |
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Figure 7: continued. |
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