E. Janknecht - R. Baade - D. Reimers
Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112, 21029 Hamburg, Germany
Received 15 April 2002 / Accepted 6 July 2002
Spectroscopy with HST/STIS Echelle and VLT/UVES of the bright QSO HE 0515-4414 ( ) offers for the first time the opportunity to study the Lyman forest in the redshift range 0.9 < z < 1.7 at a resolution 10 kms-1. The number density evolution of the Lyman lines is well described by the power law approach d . We derive for the strong lines ( ), in agreement with the Lyman forest evolution for z > 1.7. The expected slow-down in their evolution does not appear earlier than . For the weak lines ( ) we find that the HE 0515-4414 data for z > 1 follow the trend with known from z > 1.7 observations, i.e. we confirm the difference in evolution between weak and strong lines. We use the two-point velocity correlation function (TPCF) to search for clustering of the Lyman lines, yet we detect no excess in the TPCF on scales up to 10000 kms-1.
Key words: cosmology: observations - intergalactic medium - quasars: Ly forest - quasars: individual: HE0515-4414
The evolution of the Lyman absorption lines per unit redshift d has been a subject of observational studies since many years. At high redshift (z>1.6) the evolution is steep with dn/d and -3 for strong Lyman lines (i.e. log ; Rauch 1998). Observations of the Lyman forest at low redshift with HST/FOS, at first in 3C273 (Bahcall et al. 1991), showed that the number of Lyman lines was far in excess of the expected number according to an extrapolation from high z. As a final result from the HST QSO absorption line key project Weymann et al. (1998) found -0.3 for z < 1.5. They also claimed a break in the evolutionary behaviour from a steep evolutionary law for z > 1.6 to a flat one for z < 1.5. The apparently rather abrupt break in the evolutionary law just at the transition from high-resolution optical data to low-resolution UV data immediately raised the suspicion that this behaviour might not be real. Owing to the insufficient FOS resolution (230- ) the number counts might be underestimated due to line blending (Weymann et al. 1998). It is obvious, that this open question can be addressed only by UV observations of very bright QSOs at Echelle resolution. A further unsettled question which requires high-resolution UV spectra is the different evolutionary behaviour of strong compared to weak Lyman lines. It has been found from high-resolution optical spectra for z > 1.6 that apparently weak lines evolve much slower than strong lines (Kim et al. 1997). Is that also true for z < 1.5?
In this paper we use the first UV spectra of the bright intermediate redshift QSO HE 0515-4414 (z = 1.73, B = 15.0, Reimers et al. 1998) at STIS/Echelle resolution (10 kms-1) for addressing the above discussed open questions.
HE0515-4414 was observed with STIS between January 31 and February 2, 2000 with the medium-resolution NUV echelle mode (E230M) and a aperture. The overall exposure time was 31500 s resulting in a typical signal-to-noise ratio of 10 depending on the order and on the position within the orders. The resolution of the spectra is FWHM 10 kms-1. The data reduction was performed by the HST pipeline completed by an additional inter-order background correction and by coadding the separate subexposures.
The optical spectra were obtained between October 7, 2000 and January 3, 2001 using the UV-Visual Echelle Spectrograph (UVES) at the VLT/Kueyen telescope. The overall exposure time was 31500 s. The slit width was 0.8 arcsec resulting in a spectral resolution of FWHM 6 kms-1. After reduction by the UVES pipeline and conversion to vacuum baryocentric wavelengths, the individual spectra were coadded and exhibit a S/N -50 in the investigated spectral region.
The combined HST and VLT data provide the spectral range of the Lyman forest of HE0515-4414 from z=0.87 up to z=1.73, the quasar's Lyman emission redshift. To avoid the proximity effect we exclude a region of about 5000 km s-1from the quasar leading to an investigated spectral range Å or z = 0.87-1.68. We normalize the spectrum fitting polynomials to line-free regions and dividing the flux by this background continuum.
The spectrum of HE0515-4414 is strewn with metal lines and lines of molecular hydrogen (H2) from its damped Lyman system which has been studied in detail by de la Varga et al. (2000). The main difficulty in the line identification was the extraction of the Lyman lines from this real H2 forest dominating the short-wavelength region of the HST spectrum and thus suggesting to ignore this region in our analysis of the weak Lyman lines (see below). The molecular hydrogen of the DLA will be the topic of a forthcoming paper.
In a first approximation, we detected about 400 lines as Lyman candidates in the whole spectrum. These lines were fitted with the FITLYMAN code in the MIDAS package (Fontana & Ballester 1995) using Voigt profiles convolved with the instrumental profile. FITLYMAN adjusts three independent parameters per line by minimization. These parameters are the redshift of an absorption line z, its H I column density , and its Doppler parameter b, comprising the thermal and the turbulent broadening of the lines. The general fitting strategy, especially for blends, was to start with a single line and to add a further component to the ensemble if the decreased with this second line. For each of the Lyman candidates we calculated the significance level , where W denotes the observed equivalent width of the line and the error of W implying both the fit error and the continuum error. With the selection criteria and kms-1, we reduced the original sample to 235 Lyman lines. The full fit parameter list for all recognized hydrogen absorption lines is available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (22.214.171.124) via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?/A+A/391/L11.
An important fitting constraint is given for the stronger Lyman
lines for which we tried to detect the higher order lines of the Lyman series
followed by a simultaneous fit, if possible. We compared the H I
parameters inferred from Ly
profile fitting with the parameters
determined with a simultaneous fit with the accompanying Ly
counterparts. Figure 1 shows a rather small systematic effect
in the column densities. We derive
for the mean proportion of the Ly /Ly
fit column densities
and the column densities based solely on Ly
The same holds for b
This is in contrast to the recently suggested strong dependence of the fit
parameters on the fit strategy (single-component fit versus simultaneous
fit; e.g., Hurwitz et al. 1998; Shull et al. 2000).
Our 15 per cent deviation of the inferred column densities is at variance
with the above cited studies. Obviously, the improved data quality
leads to more consistent fit results. The remaining discrepancy may be
attributed to the unphysical assumption of Voigt profile
fitting. Indeed, the interpretation of the line-broadening velocity
as unresolved stochastic motions is probably an oversimplification
(e.g., Levshakov & Kegel 1997).
|Figure 1: Column densities of individually treated Lyman lines versus column densities of simultaneous Ly /Ly fits. We present the results for all detected pairs.|
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The differential column density distribution function
is defined as the number of Lyman
absorption lines per unit
column density and per unit absorption distance path (Tytler 1987):
|Figure 2: The column density distribution function for the Lyman lines. For the best fit only lines with log were included due to the limited S/N of the spectra.|
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Usually, is fitted by a power law of the form . The distribution function for all 232 lines with log is plotted in Fig. 2. The squares show the observed log f values, while the solid line represents the best fit for all lines with log . We have chosen this lower boundary because the distribution follows the power law down to this value. Assuming the general validity of the power law the sample is obviously not complete below log . The best fit yields log and . Considering exclusively the STIS lines in the same column density range, the result is very similar: log and . In contrast, the slope of the distribution function for the UVES lines is much flatter (log , ). The distribution of the UVES lines can be approximated very well with a single power law for an even wider column density range ( ) due to the better resolution of UVES. The flatter slope of the higher redshifted UVES lines indicates that stronger absorbers have evolved away faster than weaker ones. This will be discussed in more detail in Sect. 4.3.
Our result is in accordance with other analyses in comparable redshift ranges. For example, Dobrzycki et al. (2002) found -1.7, deriving the exponent from a curve of growth analysis. Hu et al. (1995) obtained for , while Kim et al. (2001) determined for various column density ranges to 1.70-1.74.
The evolution of the number density per unit redshift of
clouds can be well approximated by the power law
|Figure 3: The number density evolution of the Lyman forest for weak lines ( ). The data points are binned with . The best fit was obtained by minimization. The dotted curves represent the 95% confidence band.|
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In Figs. 3 and 4 we present the line numbers per unit redshift plotted over the redshift for the weak and for the strong lines, respectively. While for the strong lines we could exploit the whole wavelength region, we omit the spectral range Å for the weak lines to avoid misidentification due to the H2 lines, retaining an effective redshift range . The diagrams show the data points and the best fit for which we obtain for , suggesting that there is little evolution in the weak lines in the redshift interval 1.0 < z < 1.7. Considering a broader redshift range (Fig. 5, upper part) demonstrates that our STIS data follow the earlier optical observations (z > 1.6), i.e. the number density evolution is consistent with over the whole redshift range . It should be noted that lies within the 2 confidence band of our data points (see Fig. 3). The transition to a flat ( ) evolution curve probably occurs around z = 1. In contrast, the strong Lyman lines show a steeper gradient in the evolution diagram (Fig. 4). We detect an obvious correlation between the evolution and the line strength, i.e., the high column density absorbers evolve with . This disagrees with the results of Penton et al. (2000) and Dobrzycki et al. (2002) who found no or only marginal evidence for a different evolution, respectively. We cannot recognize a slow-down in the evolution of the stronger absorbers. Therefore, we conclude that this break does not occur earlier than at rather than at -1.7 as previously claimed (Impey et al. 1996; Weymann et al. 1998; Dobrzycki et al. 2002). The large spread of our data points result from the poor statistics of a single line of sight. For example, omitting the two outliers in the plot for the strong lines lying beyond the 95% confidence limit our result ( ) becomes more robust. The lower panel of Fig. 5 demonstrates the difference between the Weymann et al. (1998) data points and our values. Indeed, the former ones do not indicate any change in the evolution until , while the results of HE 0515-4414 suggest a change of the slope at much lower z.
|Figure 5: The number density evolution of the Lyman forest, comparison of different studies. Shown are the results for the column density range (upper panel) and (lower panel). The filled circles are from our study, for the other symbols see legend. It should be noted that the data points from Penton et al. (2000) and Savaglio et al. (1999) in the lower panel apply to .|
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To study the clustering properties of the Lyman
we introduce the two-point velocity correlation function
where the number of observed line pairs in a given velocity
separation bin ,
is compared with the number of
determined in the same velocity difference bin
in a randomly produced spectrum:
|Figure 6: Two-point correlation function (solid line), in 100 kms-1 bins, for higher column density absorbers ( ). Dashed and dot-dashed lines represent the 1 and 2 Poisson errors, respectively.|
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We have analyzed the evolution of the Lyman forest in the redshift range 0.9 < z < 1.7 using combined high-resolution HST/STIS and VLT/UVES data. The main results are summarized as follows:
The evolution of strong and weak lines is distinctly different. The high column density ( ) absorbers evolve according to with for 0.9 < z < 1.7, and the expected slow-down in the evolution does not appear down to . The evolution of the weaker lines over the same redshift range is consistent with , thus we have a continuation of the trend seen at higher redshifts. Again, the transition to non-evolution probably occurs around z=1. More lines of sight are necessary to confirm our results.
We detect no significant clustering of neither the weak nor the strong Lyman lines on scales up to 10000 kms-1, so we are unable to confirm most of the previous studies reporting a weak clustering signal for kms-1.
This research has been supported by the Verbundforschung of the BMBF/DLR under Grant No. 50 OR 9911 1. We thank the anonymous referee for his very helpful report.