Issue 
A&A
Volume 552, April 2013



Article Number  A77  
Number of page(s)  25  
Section  Cosmology (including clusters of galaxies)  
DOI  https://doi.org/10.1051/00046361/201220042  
Published online  29 March 2013 
The evolution of H i and C iv quasar absorption line systems at 1.9 < z < 3.2 ^{⋆,}^{⋆⋆}
^{1}
LeibnizInstitut für Astrophysik Potsdam,
An der Sternwarte 16,
14482
Potsdam,
Germany
^{2}
Department of Astronomy, University of
WisconsinMadison, 475 N. Charter
St., Madison,
WI
53706,
USA
email:
kim@astro.wisc.edu
^{3}
Institute of Astronomy, Madingley Road, Cambridge
CB3 0HA,
UK
Received:
17
July
2012
Accepted:
10
February
2013
We have investigated the distribution and evolution of ~3100 intergalactic neutral hydrogen (H i) absorbers with H i column densities log N_{H i} = [12.75,17.0] at 1.9 < z < 3.2, using 18 high resolution, high signaltonoise quasar spectra obtained from the ESO VLT/UVES archive. We used two sets of Voigt profile fitting analysis, one including all the available highorder Lyman lines to obtain reliable H i column densities of saturated lines, and another using only the Lyα transition. There is no significant difference between the Lyαonly fit and the highorder Lyman fit results. Combining our Lyαonly fit results at 1.7 < z < 3.6 with highquality literature data, the mean number density at 0 < z < 4 is not well described by a single power law and strongly suggests that its evolution slows down at z ≤ 1.5 at the high and low column density ranges. We also divided our entire H i absorbers at 1.9 < z < 3.2 into two samples, the unenriched forest and the C ivenriched forest, depending on whether H i lines are associated with C iv at log N_{C iv} ≥ 12.2 within a given velocity range. The entire H i column density distribution function (CDDF) can be described as the combination of these two wellcharacterised populations which overlap at log N_{H i} ~ 15. At log N_{H i} ≤ 15, the unenriched forest dominates, showing a similar powerlaw distribution to the entire forest. The C ivenriched forest dominates at log N_{H i} ≥ 15, with its distribution function as ∝N_{H i}^{~−1.45}. However, it starts to flatten out at lower N_{H i}, since the enriched forest fraction decreases with decreasing N_{H i}. The deviation from the power law at log N_{H i} = [14,17] shown in the CDDF for the entire H i sample is a result of combining two different H i populations with a different CDDF shape. The total H i mass density relative to the critical density is Ω_{H i} ~ 1.6 × 10^{6} h^{1}, where the enriched forest accounts for ~40% of Ω_{H i}.
Key words: quasars: absorption lines / cosmology: observations / largescale structure of Universe
The data used in this study are taken from the ESO archive for the UVES at the VLT, ESO, Paranal, Chile.
Appendix A is available in electronic form at http://www.aanda.org
© ESO, 2013
1. Introduction
The resonant Lyα absorption by neutral hydrogen (H i) in the warm (~10^{4} K) photoionised intergalactic medium (IGM) produces rich absorption features blueward of the Lyα emission line in highredshift quasar spectra known as the Lyα forest. The Lyα forest contains ~90% of the baryonic matter at z ~ 3 and can be observed in a wide range of redshifts up to z ~ 6. Gasdynamical simulations and semianalytic models have been very successful at explaining the observed properties of the Lyα forest mainly at low H i column densities N_{H i} ≤ 10^{16} cm^{2}. These models have shown that the Lyα forest arises by mildly nonlinear density fluctuations in the lowdensity H i gas, which follows the underlying dark matter distribution on large scales. This interpretation also predicts that the Lyα forest provides powerful observational constraints on the distribution and evolution of the baryonic matter in the Universe, hence the evolution of galaxies and the largescale structure (Cen et al. 1994; Rauch et al. 1997; Theuns et al. 1998; Davé et al. 1999; Schaye et al. 2000b; Schaye 2001; Kim et al. 2002). In addition, the discovery of triply ionised carbon (C iv) associated with some of the forest absorbers suggests that the forest metal abundances can be utilised to probe early generations of star formation and the feedback between highredshift galaxies and the surrounding IGM from which galaxies formed (Cowie et al. 1995; Davé et al. 1998; Aguirre et al. 2001; Schaye et al. 2003; Oppenheimer & Davé 2006).
The physics of the Lyα forest is mainly governed by three competing processes, the Hubble expansion, the gravitational growth and the ionizing ultraviolet (UV) background radiation. The Hubble expansion which causes the gas to cool adiabatically and the gravitational growth are fairly wellconstrained by the cosmological parameters and the primordial power spectrum from the latest WMAP observations (Jarosik et al. 2011). On the other hand, the ionizing UV background radiation controls the photoionisation heating and the gas ionisation fraction, thus determining the fraction of the observable H i gas compared to the unobservable H ii gas. The UV background is assumed to be provided primarily by quasars and in some degree also by starforming galaxies (Shapley et al. 2006; Siana et al. 2010) and Lyα emitters (Iwata et al. 2009). However, the intensity/spectral shape of the UV background and the relative contribution from quasars and galaxies as a function of redshift are not well constrained (Bolton et al. 2005; FaucherGiguère et al. 2008). One of the common methods to measure the UV background and its evolution is the quasar proximity effect (Dall’Aglio et al. 2008). Unfortunately, measurements of the UV background through the proximity effect are biased by the large scale density distribution around the quasars which cannot be easily quantified observationally (Partl et al. 2010, 2011).
Two commonly explored quantities to constrain the properties of the Lyα forest are the number of absorbers for a given H i column density range per unit redshift, dn/dz, and the differential column density distribution function (CDDF, the number of absorbers per unit absorption path length and per unit column density, an analogue to the galaxy luminosity function). Compared with simulations, detailed structures seen in an overall powerlawlike CDDF (∝) such as a flattening or a steepening at different column density ranges constrain various forest physical and galactic feedback processes (Altay et al. 2011; Davé et al. 2010). The CDDF is also one of the main observables required in calculating the mass density relative to the critical density contributed by the forest (Schaye 2001). The shape of the CDDF at lower N_{H i} ≤ 10^{12.5−12.7} cm s^{2} (a typical detection limit for most available highquality data) is of particular importance, since the lower N_{H i} absorbers are much more numerous than higher N_{H i} absorbers, thus they can trace a significant fraction of baryons, depending on the steepness of the CCDF at the low N_{H i} limit.
On the other hand, dn/dz provides an additional way to study the UV background radiation and its evolution. The gas density decreases with decreasing redshift due to the Hubble expansion. A lower gas density results in a strong reduction of the recombination rate, allowing the gas to settle in to a photoionisation equilibrium with a higher ionisation fraction. With the nondecreasing background radiation, this causes a steep number density evolution. However, the decrease of the quasar number density at z < 2.5 also decreases the available ionising photons (Silverman et al. 2005). This changes the ionisation fraction in the gas and also counteracts the gas density decrease, and hence slows down the number density evolution (Theuns et al. 1998; Davé et al. 1999; Bianchi et al. 2001).
The result from the HST/FOS Quasar Absorption Line Key project shows such a slow change in the dn/dz evolution at z < 1.5 (Weymann et al. 1998), compared to a much steeper dn/dz evolution shown at z > 2 (Kim et al. 1997, 2001, 2002). Cosmic variance also seems to increase at lower z (Kim et al. 2002). Unfortunately, recent work based on betterquality HST data at z < 1.5 (or the observed H i Lyα at <3050 Å) have shown rather ambiguous dn/dz results with a large scatter along different sightlines (Janknecht et al. 2006; Lehner et al. 2007; Williger et al. 2010). The only certain observational fact is that all of these newer studies show a factor of ~2−3 lower number densities than the Weymann et al. values at z < 1.5. Considering a lack of results from goodquality data at 1 < z < 1.5 in the literature, the redshift evolution of dn/dz can be considered as a single power law without any abrupt change in dn/dz at 0 < z < 3.5.
Here we present an indepth Voigt profile fitting analysis of 18 high resolution (R ~ 45 000), high signaltonoise (~35−50 per pixel) quasar spectra obtained with the UVES (Ultraviolet Visible Echelle Spectrograph) on the VLT, covering the Lyα forest at 1.9 < z_{forest} < 3.2. Our main scientific aims are to derive the redshift evolution of the absorber number density and the column density distribution function from a large and homogeneous set of data available at z > 2, since most previous highquality forest studies at z > 2 have been based on less than 5 sightlines. Even with few sightlines, the statistics for the weak forest lines is robust due to the large number of weak absorbers with N_{H i} = 10^{13−15} cm^{2} (about 150 absorbers at z ~ 2.5 per sightline, i.e. in the wavelength range between the quasar’s Lyα and Lyβ emission lines). However, for the stronger forest systems with N_{H i} ≥ 10^{15} cm^{2}, more sightlines are required since there are only about 10 absorbers per sightline at z ~ 2.5. Cosmic variance also plays an important role at lower redshifts, especially for stronger absorbers (Kim et al. 2002). Therefore, increasing the sample size at z ~ 2 is critical in addressing the N_{H i} evolution for the Lyα forest.
In addition to the increased sample size, we have improved previous results in two ways. First, most previous studies on the forest from groundbased observations at z > 1.7 have been based on the Lyαonly profile fitting analysis. This approach does not provide a reliable N_{H i} for saturated lines, N_{H i} ≥ 10^{14.5} cm^{2} for the present UVES data. To derive a more reliable N_{H i} of saturated lines, we have included all the available highorder Lyman series in this study.
Second, strong evidence have been accumulated in recent studies that metals associated with the highredshift Lyα forest are within ~100 kpc of galaxies as in the circumgalactic medium rather than in the intergalactic space far away from galaxies (Adelberger et al. 2005; Steidel et al. 2010; Rudie et al. 2012). This implies that the H i absorbers containing metals might show different properties than the ones without detectable metals. Taking C iv as a metal proxy, we have divided our data into two samples, one with C iv (the C ivenriched forest) and another without C iv (the unenriched forest), in order to test this scenario of the circumgalactic medium. Since our study lacks the imaging survey around the quasar targets, we cannot claim that the C ivenriched forest is indeed located within ~100 kpc from a nearby galaxy. However, this study provides complementary results to galaxyabsorber connection studies at high redshifts (Steidel et al. 2010; Rudie et al. 2012).
This study is also very timely since the Cosmic Origins Spectrograph (COS), a highsensitivity farultraviolet spectrograph onboard HST has started to produce many highquality quasar spectra at z < 1 (Green et al. 2012; Savage et al. 2012). These COS quasar observations have opened a new tool to study the lowz Lyα forest. Combined with results at high redshifts such as our study, COS observations will make it possible to characterise the dn/dz evolution at 0 < z < 3.5 in a more robust way, thus a stringent constraint on the UV background evolution.
This paper is organised as follows. Section 2 describes the analysed data and two Voigt profile fitting methods. Comparisons with previous studies based on the Lyαonly fit are shown in Sect. 3. The analysis based on the highorder Lyman fit is presented in Sect. 4. Column density distribution and evolution of the Lyα forest containing C iv are presented in Sect. 5. Finally, we discuss and summarise the main results in Sect. 6. All the results on the number density and the differential column density distribution from our analysis are tabulated in Appendix A. Throughout this study, the cosmological parameters are assumed to be the matter density Ω_{m} = 0.3, the cosmological constant Ω_{Λ} = 0.7 and the current Hubble constant H_{0} = 100 h km s^{1} Mpc^{1} with h = 0.7, which is in concordance with latest WMAP measurements (Jarosik et al. 2011). The logarithm N_{H i} is defined as log N_{H i} = log (N_{H i}/1 cm^{2}).
Analysed quasars.
2. Data and Voigt profile fitting
Table 1 lists the properties of the 18 highredshift quasars analysed in this study. The redshift quoted in Col. 2 is measured from the observed Lyα emission line of the quasars. Note that the redshift based on the emission lines is known to be underestimated compared to the one measured from other quasar emission lines such as C iv (Tytler & Fan 1992; Vanden Berk et al. 2001). The spectrum of Q1101−264 is the same one as analysed in Kim et al. (2002), while the rest of spectra are from Kim et al. (2007). The raw spectra were obtained from the ESO VLT/UVES archive and were reduced with the UVES pipeline. All of these spectra have a resolution of R ~ 45 000 and heliocentric, vacuumcorrected wavelengths. The spectrum is sampled at 0.05 Å. A typical signaltonoise ratio (S/N) in the Lyα forest region is 35–50 per pixel (hereafter all the S/N ratios are given as per pixel). Readers can refer to Kim et al. (2004, 2007) for the details of the data reduction. To avoid the proximity effect, the region of 4000 kms^{1} blueward of the quasar’s Lyα emission was excluded.
In order to obtain the absorption line parameters (the redshift z, the column density N in cm^{2} and the Doppler parameter b in kms^{1}), we have performed a Voigt profile fitting analysis using VPFIT^{1}. Details can be found in the documentation provided with the software, Carswell, Schaye & Kim (2002) and Kim et al. (2007). Here, we only give a brief description of the fitting procedure.
First, a localised initial continuum of each spectrum was defined using the CONTINUUM/ECHELLE command in IRAF. Second, we searched for metal lines in the entire spectrum, starting from the longest wavelengths toward the shorter wavelengths. We first fitted all the identified metal lines. When metal lines were embedded in the H i forest regions, the H i absorption lines blended with metals are also included in the fit. Sometimes the simultaneous fitting of different transitions by the same ion reveals that the initial continuum needs to be adjusted to obtain acceptable ion ratios. In this case, we adjusted the initial continuum accordingly. The rest of the absorption features were assumed to be H i.
After fitting metal lines, we have fitted the entire spectrum including all the available higherorder Lyman series in the UVES spectra. This is absolutely necessary to obtain reliable N_{H i} for saturated lines, as our study deals with saturated lines and relies on line counting. A typical z ~ 3 IGM absorption feature having b ~ 30 km s^{1} starts to saturate around N_{H i} ≥ 10^{14.5} cm^{2} at the UVES resolution and S/N. Unfortunately, N_{H i} and b values of saturated lines are not well constrained. In order to derive reliable N_{H i} and b values, higherorder Lyman series, such as Lyβ and Lyγ, have to be included in the fit, as higherorder Lyman series have smaller oscillator strengths and start to saturate at much larger N_{H i}.
During this process, another small amount of continuum readjustment was often required to achieve a satisfactory fit, i.e. a reduced χ^{2} value of ~1.2. With this readjusted continuum, we refitted the entire spectrum. This iteration process of continuum readjustments and refitting was then repeated several times until satisfactory fitting parameters were obtained. This produces the final set of fitted parameters for each component of the highorder Lyman fit analysis.
In addition to this highorder fit, we have also performed the same analysis using only the Lyα transition region, i.e. the wavelength range above the restframe Lyβ and below the proximity effect zone. This additional fitting analysis was done, since most previous studies on the IGM N_{H i} analysis based on Voigt profile fitting utilised only the Lyα region. For the Lyαonly fit, we kept the same continuum used in the highorder Lyman fitting process. In principle, the difference between two sets of fitted parameters occurs only in the regions where saturated absorption features are included. In both fitting analyses, we did not tie the fitting parameters for different ions.
Fig. 1 Numbers of absorption lines as a function of N_{H i} at 2.2 < z < 2.6 and 2.8 < z < 3.2. The Lyαonly fits are shown as solid lines, while the highorder Lyman fits are marked as dashed lines. Solid errors indicate the 1σ Poisson errors of the Lyαonly fits. 

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Figure 1 shows the numbers of absorption lines as a function of N_{H i} for both fitting analyses at the two redshift ranges, 2.2 < z < 2.6 and 2.8 < z < 3.2, in order to illustrate the differences at high and low redshifts. The differences between the two samples occurs mostly at N_{H i} ≥ 10^{14.5} cm^{2} and at N_{H i} ≤ 10^{12} cm^{2}. This difference in the line numbers at N_{H i} ≥ 10^{14.5} cm^{2} seems to be stronger at 2.8 < z < 3.2, although it is still within 2σ Poisson errors. The line numbers at N_{H i} ≤ 10^{12} cm^{2} are more susceptible to the incompleteness which depends on the local S/N than the difference between the two fitting methods. The difference at other column density ranges is smaller, which in turn leads us to expect that there is no significant difference between the Lyαonly fit and the highorder Lyman fit.
We restrict our present analysis to log N_{H i} = [12.75,17] at all redshifts. As clearly seen in Fig. 1, the incompleteness becomes quite severe for log N_{H i} ≤ 12.5 and redshifts z > 3 (Kim et al. 1997, 2002). Therefore, the lower N_{H i} limit was chosen to be log N_{H i} = 12.75. We chose log N_{H i} = 17 as the upper N_{H i} limit since we wanted to analyze only the Lyα forest whose traditional definition is an absorber with log N_{H i} < 17.2 (above which it is referred to as a Lyman limit system Tytler 1982). Additionally, absorbers at log N_{H i} > 17 are very rare (Fig. 1).
Note that the availability of the highorder Lyman series depends on the redshift of the quasar and whether the sightline contains a Lyman limit system. In addition, the amount of blending affects whether a reliable column density can be measured. At high redshifts z_{em} > 3, line blending becomes severe. However, most UVES spectra also covers down to 3050 Å where Lyman lines higher than Lyδ are available. On the other hand, at z_{em} < 2.5 the available highorder Lyman lines are rather limited, with mostly Lyβ and Lyγ available. However, line blending is less problematic than at higher redshifts. We have generated tens of saturated artificial absorption lines and fitted them including and excluding highorder Lyman lines. These simulations show that unblended absorption features at N_{H i} ≤ 10^{17} cm^{2} can be reasonably well constrained with Lyα and Lyβ only. This indicates that our N_{H i} can also be considered reliable even at z < 2.5 with Lyα and Lyβ only.
Fig. 2 Total absorption distance X(z) covered with our sample of 18 highredshift quasars. The solid line is for the Lyαonly fit, while the dashed one is for the highorder fit. 

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The absorption distance is obtained by integrating the Friedmann equation for a Ω_{m} = 0.3 and Ω_{Λ} = 0.7 universe, and is given by (1)(Bahcall & Peebles 1969), where H_{0} is the Hubble constant at z = 0. The total absorption distance X(z) covered by the spectra for both Lyαonly and highorder fits is shown in Fig. 2. The redshift coverage of our sample steadily increases with decreasing redshift until it reaches its maximum at z ~ 2.1. For redshifts below z < 1.9 the coverage decreases rapidly and our sample ends at z = 1.7. Note that the lowest redshift possible for the highorder Lyman line analysis is z ~ 1.97, while the Lyαonly fit analysis is possible down to z ~ 1.7. Due to the reduced redshift coverage in the highorder Lyman range of individual sight lines caused by intervening Lyman limit systems, the sample coverage of the highorder fit analyses is reduced between 2.4 < z < 2.7. At the high redshift end z > 3.22, the number of available forest lines decreases and the sample consists of only one line of sight. The low redshift limit for the highorder fit was set to be the lowest redshift without any saturated lines when no Lyβ is available for each quasar. This criterion restricts our highorder Lyman fit analysis to 1.9 < z < 3.2. Since the redshift coverage of lowz quasars for the highorder fit is shorter than the one for the Lyαonly fit and the highN_{H i} forest clusters stronger at lower z (Kim et al. 2002), the quasarbyquasar dn/dz at z ~ 2 from the highorder fit analysis is expected to suffer from the low number statistics.
In Table 1, Cols. 3–6 summarise the redshift range used for the different analysis. Column 3 lists the redshift range of the Lyα forest region analysed for the number density evolution in the Lyαonly fit. For the differential column density evolution of the Lyαonly fit, we used the redshift range listed in Col. 5. Columns 4 and 5 list the redshift range for which the highorder Lyman fit can be performed and the one for which the highorder Lyman fit was done, respectively. The region is listed only when it is different from the Lyαonly fit region in Col. 3. Since there are no strongly saturated Lyα lines at 1.90 < z < 1.98 for some lowz quasar sightlines, we used a lower redshift range than the one listed in Col. 4 for the highorder Lyman fit analysis for these sightlines. Column 6 shows the redshift range excluded for the C ivenriched H i study in Sect. 5. Due to the wavelength gaps caused by the UVES dichroic setup, the covered C iv redshift ranges are smaller than the Lyα forest coverage listed in Col. 3. The region of ± 200 km s^{1} from the gap was excluded, and only the redshift range covering both C iv doublets was included in the analysis. The blank entries mean that the analyzed z_{C iv} is the same as the forest z_{Ly, high−order}. Q0055−269 and J2233−606 are excluded in the Lyα–C iv forest study due to their lower S/N in the C iv region.
In the HE2347−4342 Lyα forest region, there are very strong O vi absorptions mixed with the two saturated Lyα absorption systems at 4012–4052 Å (Fechner et al. 2004). Since the fitted line parameters for these Lyα systems cannot be well constrained (their corresponding Lyβ is below the partial Lyman limit produced by the z ~ 2.738 systems), we excluded this forest region toward HE2347−4342. In the J2233−606 sightline, there are two partial Lyman limit systems at 3489 Å (z ~ 1.870) and 3558 Å (z ~ 1.926) and several high column density forest absorbers at 3400–3650 Å. To derive a robust N_{H i}, we included the HST/STIS echelle spectrum of J2233−606^{2} at 2280–3150 Å. The resolution in this wavelength region is ~10 km s^{1} and its S/N is ~8 per pixel (0.05 Å).
Table 1 also lists the S/N of each quasar spectrum in Col. 7. The number outside the bracket is a S/N of the H i forest region. The first number inside the bracket is a typical S/N of the C iv region at 1.9 < z < 2.4, while the second is for 2.4 < z < 3.2. The dotted entries inside the bracket indicate that no C iv forest region is available for a given redshift range. The low redshift bin of the C iv forest covers the wavelength region where the different CCDs from two dichroic settings were used at ~4780 Å (or z ~ 2.1). This leads to a much lower S/N at ≤4780 Å (z < 2.1). When the lower S/N region is larger than 20% of the whole C iv forest range, two numbers were listed inside the parentheses. The first number corresponds to the lower S/N at 1.9 < z < 2.1, while the second number is for the higher S/N at 2.1 < z < 2.4.
Fig. 3 Number of H i absorbers with log N_{H i} = [12.75,17] as function of redshift in our our sample of 18 highredshift quasars. The solid line is for the Lyαonly fit. The dashed line is for the highorder fit, while the red heavy dotdashed line is for the Lyαonly fit for the redshift range used for the highorder fit. 

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In addition, regions within ± 50 Å to the center of a subdamped Lyα (DLA) system (N_{H i} ≥ 10^{19} cm^{2}) are excluded, since they are associated directly with intervening highz galactic disks/halos and could have a possible influence on the apparent line densities in the forest. The sightline toward Q0453−423 includes a subDLA, which introduces a gap in the Lyα redshift range. All the calculations toward Q0453−423 account for this redshift gap correctly. However, they are plotted as a single data point and their plotted redshift range is the whole Lyα redshift range without showing a gap. The sightlines toward PKS2126−158 and Q0420−388 also contain an intervening subDLA, which shortens the continuously available redshift coverage for the highorder fit. Column 8 of Table 1 lists the observed wavelength of a Lyman limit (LL, 912 Å in the restframe wavelength) of each quasar, which is defined as the wavelength below which the observed flux becomes 0. The values are taken from Kim et al. (2004). When a Lyman limit is not detected within available data, it is denoted to be less than the lowest available wavelength. Column 9 of Table 1 notes information on subDLAs along the sightline. The total number of H i lines for log N_{H i} = [12.75,17] at 1.9 < z < 3.2 is 3077 for the highorder Lyman fit sample. The Lyαonly fit sample has 3778 H i lines at the total redshift range listed in the 3rd column of Table 1.
In Fig. 3 the number of H i absorbers with log N_{H i} = [12.75,17] from both fitting methods is shown as a function of redshift. The number of absorbers obtained from each fitting analysis is roughly proportional to the absorption distance coverage. Therefore, our sample shows the highest H i absorber numbers around redshift z ~ 2 for each fitting analysis, where the sample absorption distance coverage also reaches its maximum. Sometimes the highorder fit analysis (dashed line) reveals a slightly higher number of absorbers between 2 < z < 3. This is because what appear to be single saturated Lyα lines may have more than one component present in the corresponding higher order Lyman lines. At z ~ 2, the number of the Lyαonlyfit absorbers (heavy dotdashed line) is slightly larger than the highorderfit absorbers. This is caused by the fact that some simple saturated lines with log N_{H i} < 17 in the Lyαonly fit analysis are actually absorbers with log N_{H i} > 17 in the highorder fit analysis. Since the Lyαonly fit gives a lower N_{H i} limit for a saturated line, these lines are included in the Lyαonly fit sample, but excluded in the highorder fit sample in Fig. 3.
3. Comparison with previous studies using Lyα only
In Sect. 2 we have shown that including higher order transitions in the fitting process slightly alters the column density statistics at log N_{H i} > 15.0. In order to compare our quasar sample with previous studies based only on the Lyα transition, we briefly present the column density distribution and evolution derived from the Lyαonly fit in this section. A large redshift coverage is very important in the study of the absorber number density. Therefore we used all Lyα lines found in the whole available Lyα redshift ranges listed in Col. 3 of Table 1 in this section. On the other hand, the differential density distribution function is not sensitive to a large redshift coverage. Thus, only the Lyα lines at 1.9 < z < 3.2 are analysed for the distribution function study. A detailed analysis using the highorder fit is presented in Sect. 4. All the results from this section are tabulated in Appendix A.
3.1. Absorber number density evolution dn/dz
The absorber number density n(z) is measured by counting the number of H i absorption lines for a given column density range for each line of sight. The line count n is then divided by the covered redshift range Δz to obtain dn/dz. If forest absorbers have a constant size and a constant comoving number density, its number density evolution due to the Hubble expansion can be described as (2)where R is the size of an absorber, N_{0} is the local comoving number density and c is the speed of light (Bahcall & Peebles 1969). For our assumed cosmology, Eq. (2) becomes (3)At 1 < z < 4.5, Eq. (3) has an asymptotic behaviour of dn/dz ∝ (1 + z)^{~0.6}, while at z < 1 it becomes dn/dz ∝ (1 + z)^{~1.15}. For higher redshifts the asymptotic behaviour becomes dn/dz ∝ (1 + z)^{0.5}. Any differences in the observed exponent from what is expected from Eq. (3) indicate that the absorber size or/and the comoving density are not constant.
Empirically, dn/dz is described as dn/dz = A(1 + z)^{γ}. It has been known that dn/dz evolves more rapidly at higher column densities. At z > 1.5, a γ ~ 2.9 is found for N_{H i} = 10^{14−17} cm^{2}, and γ ~ 1.4 for N_{H i} = 10^{13.1−14} cm^{2} (Kim et al. 2002). At z < 1.5, Weymann et al. (1998) found γ ~ 0.16 and A ~ 35 for absorbers with a restframe equivalent width greater than 0.24 Å from HST/FOS data. Later studies on dn/dz based on the profile fitting or curve of growth analysis using betterquality data from HST/STIS and HST/GHRS show a factor of ~2–3 lower dn/dz than the one found by Weymann et al. (1998). These studies also show a larger scatter in dn/dz at z < 0.2 with A ~ 5–22 (Lehner et al. 2007; Williger et al. 2010). Part of this scatter is thought to be caused by inhomogeneous data quality, analysis methods, and cosmic variance. Unfortunately highquality data lack a complete z coverage at z < 1.5, missing mostly at 0.4 < z < 1.0. Keep in mind that the FOS result and most available groundbased results at z > 1.5 in the literature are based on the Lyα lines only, while most spacebased results at z < 1.5 are using the available highorder Lyman series. Therefore, it is not possible to derive a robust powerlaw slope γ of dn/dz at 0 < z < 3.5. Strictly speaking, a fair comparison should be made on the data with similar qualities and uniform analyses.
Fig. 4 Number density evolution of the Lyα forest in the column density range log N_{H i} = [14,17] of the Lyαonly fits. Black filled circles show results from our data set, which is tabulated in Table A.1. Other data points indicate various results obtained from the literature. The vertical error bars give the 1σ Poisson error, while the xaxis error bars show the redshift range covered by each sightline. The solid line shows the fit to our data only. Dashed line is the result including the literature data for z > 1 (log (1 + z) > 0.3). The dotted line gives the fit given in Kim et al. (2002). The green dotdashed curve shows the predicted dn/dz evolution based on a quasaronly UV background by Davé et al. (1999). The red dotted and the blue dotdotdotdashed curves at z < 2 illustrate the predicted dn/dz based on momentumdriven wind and nowind models with a UV background by quasars and galaxies, respectively (Davé et al. 2010). 

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The number density evolution is illustrated in Figs. 4–6 for two different column density ranges: log N_{H i} = [14,17], and [13.1,14]. Data compiled from the literature are indicated in the figures: Hu et al. (1995), Lu et al. (1996), Kim et al. (1997), Kirkman & Tytler (1997), Weymann et al. (1998), Savaglio et al. (1999), Kim et al. (2001), Sembach et al. (2004), Williger et al. (2006), Aracil et al. (2006)^{3}, Janknecht et al. (2006)^{4}, Lehner et al. (2007) and Williger et al. (2010). To be consistent with our definition of the proximity effect zone, we applied the same 4000 km s^{1} exclusion within the quasar’s Lyα emission line for all the literature data, whenever the line lists from the literature include all the Lyα lines below the Lyα emission line of the quasar. When the published line lists are only for the shorter wavelength region than the entire, available forest region outside the 4000 km s^{1} proximity zone, such as the ones by Hu et al. (1995), no such an exclusion is required. We used all the reported H i lines in the literature mentioned above, without any preselection imposed on N_{H i} or b parameters. The latest study on the lowredshift IGM by Williger et al. (2010) found that the number density from the HST/STIS results is a factor of 2–3 lower than the HST/FOS results by Weymann et al. (1998). They applied the same selection criteria on H i absorbers used by Lehner et al. (2007), i.e. measurement errors less than 40% and b < 40 km s^{1}. As H i absorbers tend to have a larger b parameter at lower redshift (Lehner et al. 2007) and larger measurement errors in general, selecting H i absorbers at b > 40 km s^{1} has a larger impact on dn/dz at lower redshift. In addition, as the HST/FOS results are based on the H i sample without any imposed selection criteria, using the full H i lines provides a more straightforward comparison to the HST/FOS result.
Fig. 5 Number density evolution of the Lyα forest in the column density range log N_{H i} = [13.1,14.0]. All the symbols have the same meaning as in Fig. 4. 

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We have performed a linear regression to our data in logarithmic space for the various column density bins, using the maximum likelihood method described in Ripley & Thompson (1987). This method accounts for the uncertainties in the number density and incorporates the weighting using the uncertainties. Errors of the fit parameters were obtained using the maximum likelihood method. Linear regressions were once obtained from our data including the literature data and once without them. Since for redshifts z ≲ 1 (or log (1 + z) ≲ 0.3) the number density evolution could remain constant with redshift, cf. Weymann et al. (1998), only the literature data with redshift z > 1 was used for the fit. The resulting parameters are given in Tables 2.
Figure 4 shows the dn/dz evolution for the column density interval of log N_{H i} = [14,17]. Our results (filled circles) agree well with previous findings at z > 1.5 (log (1 + z) > 0.4), confirming that there is a real sightline variation in dn/dz. Kim et al. (2002) notes that the scatter between different sightlines increases as z decreases down to z ~ 2. In fact, the data of Janknecht et al. (2006) at redshifts below z ~ 2 (log (1 + z) ~ 0.45) indicate that the scatter might well increase at lower z, although the errors are still very large to draw any firm conclusions. Considering that the FOS result is based on the equivalent width measurement, and the conversion from the equivalent width to the column density requires the b parameters of individual absorbers, which are illconstrained at the FOS resolution, the full HST/STIS H i sample toward some sightlines is in good agreement with the HST/FOS result (blue open triangles), although there still is a large sightline variation. The full H i sample at z < 0.4 strongly supports the previous conclusion obtained by the HST/FOS result, that dn/dz flattens out at z ≤ 1.5.
Fig. 6 Mean number density evolution of the Lyα forest. The vertical error bars give the 1σ Poisson error, while the xaxis error bars show the redshift range covered by each data point. The data point with the dotted error bar indicate the Janknecht et al. (2006) data. The dashed straight lines mark the fit to the mean data excluding the Janknecht et al. (2006) data. 

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Linear regression results for dn/dz.
The linear regression to our results only (the solid line) with γ = 3.40 ± 0.36 is different at 3σ from the fit to all the available data at z > 1 (log (1 + z) > 0.3) which yields γ = 2.16 ± 0.14 (the dashed line). This discrepancy is mainly due to the sparse data of our sample at higher redshift z > 3.5 (log (1 + z) > 0.65) and the missing constraints at z < 2.0. The discrepancy is also in part caused by how the powerlaw fit is performed. Our maximum likelihood fit does the weighted fit. This gives a higher weight on higherz data points where the 1σ Poisson error is usually smaller. The nonweighted fit for our UVES data only results in a steeper powerlaw slope, (−0.84 ± 0.24) × (1 + z)^{5.02 ± 0.76}. The nonweighted fit for all the data at z > 1 is (0.76 ± 0.18) × (1 + z)^{2.00 ± 0.25}.
Interestingly, some earlier numerical simulations and theories with a quasaronly UV background have shown that there should be a break in the dn/dz evolution at z ~ 2 due to the decrease in the quasar number density, thus less available H i ionising photons (Theuns et al. 1998; Davé et al. 1999; Bianchi et al. 2001). The green dotdashed curve in Fig. 4 shows one of such predicted dn/dz evolutions by Davé et al. (1999), which outlines the Weymann et al. dn/dz reasonably well. However, more recent simulations by Davé et al. (2010) predict different dn/dz evolutions. These simulations are based on the various galactic wind models and the UV background contributed both by quasars and galaxies. The red dotted and the blue dotdotdotdashed curves at z < 2 illustrate their predicted dn/dz based on momentumdriven wind and nowind models, respectively. These newer simulations predict that dn/dz continuously decreases with decreasing redshift. Their momentumdriven wind model agrees reasonably well with the observations by HST/STIS with the H i absorber selection imposed (measurement errors less than 40% and b < 40 km s^{1}), but not with the Weymann et al. data. A better, uniform dataset from HST/COS observations should resolve this discrepancy at z < 0.5.
For the column density interval for stronger absorbers log N_{H i} = [14.5,17.0], our data shows that the evolution continues to follow the empirical powerlaw with γ = 3.02 ± 0.48 (see Table 2). However, the scatter between different sightlines is large as stronger absorbers are rare at all redshifts (Davé et al. 2010). There are more than 3σ difference between the lowest dn/dz sightline and the highest dn/dz sightline at z ~ 2. Kim et al. (2002) discuss the possibility on whether the column density evolution flattens out at z < 2.5 (log (1 + z) < 0.55) for this column density interval. Even though more data points are available in this study, this question cannot be conclusively answered and more data covering lower redshifts are required.
The line number density evolution for low column density systems in the range of log N_{H i} = [13.1,14.0] is presented in Fig. 5. Similar to Fig. 4, it suggests that the flattening of dn/dz at z < 1.5 might continue at the lower column density range. However, the sightline variation at z < 0.4 is larger at this column density range. This is in part caused by different analysis methods and different S/N STIS data used by different studies. For example, the number density measured in the STIS spectrum toward PKS0405−123 is different between the Williger et al. (2006) work (filled purple upsidedown triangles) and the Lehner et al. (2007) work (two of open red upsidedown triangles). Again the results from our data agree well with previous results found in the literature at z > 1.5. The linear regression to our data at z > 1.5 gives γ ≈ 1.67, comparable to the fit including all available literature data points at z > 1.0. However, these results do not compare well with the linear regression obtained by Kim et al. (2002) with γ = 1.18 ± 0.14 (the dotted line), a shallower dn/dz evolution. This discrepancy arises due to their rather small sample size at z < 2.5 and more severe line blending at higher redshifts. Given a larger cosmic variance at lower redshifts, the sample size becomes more important. At the same time, line blending at high redshifts makes the detection of weak absorbers difficult. This incompleteness effect has been shown to underestimate the line number density of low column density systems at log N_{H i} = [13.3,13.6] by ~17% at z ~ 3 (log (1 + z) ~ 0.6) and by ~35% at z ~ 4 (Giallongo et al. 1996). Both effects tend to flatten the evolution observationally from its true value. In addition, the robust estimate of the exponent γ requires a large z leverage.
Even though there are not many sightlines covering 0.5 < z < 1.5, we calculated the mean dn/dz from all the combined H i fitted line lists including the literature data in Fig. 6. This mean dn/dz is not an averaged value of the individual sightlines. The literature data used in the combined line list include all the quasar sightlines shown in Figs. 4 and 5, except the HST/FOS Weymann et al. (1998) data, the Williger et al. (2006) data and the Savaglio et al. (1999) data. The HST/FOS data was excluded since they were based on the equivalent width measurements, while the Williger et al. (2006) data suffered from noise features. The Savaglio et al. (1999) result is from a single sightline and provides the only data point besides the Janknecht et al. (2006) data at z ~ 1. Although the Janknecht et al. (2006) data also suffer from noise, they were from 9 sightlines. We opted to use a result based on the analysis of multiple sightlines from a single study. This helps to reduce any systematics caused by combining results from different studies at z ~ 1. For z < 0.4, the systematic uncertainty is larger since the line lists used are produced by different studies.
At log N_{H i} = [13.1,14.0], there might occur a flattening at z ~ 1, if the Janknecht data were included. At log N_{H i} = [14,17], a single power law with γ = 1.61 ± 0.12 does not give a good fit at 0 < z < 4, regardless of the inclusion of the Janknecht et al. data. It remains to be seen whether a single power law fits the dn/dz evolution for both high and low column density ranges at z = 0.4. It should be noted that the dn/dz of Lyman limit systems with a column density of log N_{H i} = [17.2,19.0] does not fit to a single power law. It shows a slower evolution at z < 2 and evolves rapidly at z > 2 (Prochaska et al. 2010), while the dn/dz of DLA systems with log N_{H i} = [20.3,22.0] shows a single powerlaw evolution with a slope γ = 1.27 ± 0.11 at 0 < z < 4.5 (Rao et al. 2006) .
Our results indicate that higher column density forest systems evolve more rapidly than low column density systems and the number density of high column density systems decreases faster with decreasing redshift. The increase in the scatter at redshifts z < 2.5 might indicate the transition point where the evolving number density changes into a nonevolving one, as is predicted in earlier numerical simulations by Theuns et al. (1998) and Davé et al. (1999).
3.2. The differential column density distribution function
The differential column density distribution function (CDDF) is defined as the number of absorbers per unit absorption distance X(z) and per unit column density N_{H i}. The absorption distance is calculated using Eq. (1). Empirically, the differential distribution function is reasonably well described by a single power law at z ~ 3 at log N_{H i} = [13,22] as (4)where gives the normalisation point of the distribution function and β denotes its slope. However, the detailed shape of the differential column density distribution function is dependent on the N_{H i} column density range (Prochaska et al. 2010; Altay et al. 2011). It shows a flattening around the transition from the forest to the Lyman limit systems at N_{H i} at log N_{H i} ~ 17. Then it shows a steepening at log N_{H i} ~ 20 where a transition occurs from the subDLA systems to the DLA systems.
Fig. 7 Differential column density distribution at 1.9 < z < 3.2 using the Lyαonly fits. Both black (log N_{H i} ≥ 12.75) and grey (log N_{H i} < 12.75) data points show the results from our quasar sample. The grey data points mark the column densities that are affected by incompleteness. The stars are the data points obtained by Petitjean et al. (1993). The filled circles at log N_{H i} > 20.1 and at 19 < log N_{H i} < 20.1 are from the SDSS II DR7 at ⟨ z ⟩ = 3.02 by Noterdaeme et al. (2009) and from O’Meara et al. (2007) at ⟨ z ⟩ = 3.1, respectively. The solid line gives the power law fit to our data for log N_{H i} = [12.75,14]. The dotted line represent the fit obtained by Hu et al. (1995), while the dashed line represents a theoretical prediction at z ~ 3 by Altay et al. (2011). The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. Gas overdensities on the top xaxis are computed using Eq. (10) from Schaye (2001) at z = 2.55 (see text for details). 

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In Fig. 7 we present the results using the Lyαonly fits at 1.9 < z < 3.2. Note that the redshift range used for the CDDF analysis is different from the one used for the dn/dz analysis in Sect. 3.1. The total absorption distance X(z) at 1.9 < z < 3.2 is 21.8165. The binsize of log N_{H i} = 0.25 is used at log N_{H i} = [12.0,15.0], then the binsize of 0.5 at log N_{H i} = [15.0,18.0]. To increase the column density coverage, we include results from Noterdaeme et al. (2009) and O’Meara et al. (2007) for log N_{H i} > 19 at z ~ 3 from the SDSS II DR7 data^{5}. The top xaxis is in units of the gas overdensity δ which was computed according to Eq. (10) by Schaye (2001)(5)Here, the gas temperature T is assumed to be T = T_{4} × 10^{4}K, the photoionisation rate Γ = Γ_{12} × 10^{12}s^{1}. The parameter f_{g} denotes the fraction of mass in gas. The IGM gas temperature is assumed to be governed by the effective equation of state T = T_{0}(1 + δ)^{γ−1}, where T_{0} is the temperature at the cosmic density (Hui & Gnedin 1997). For Γ_{12} and γ, we interpolated results obtained by Bolton et al. (2008). We assumed that T_{0} is 2 × 10^{4}K, f_{g} = 0.16, and Ω_{b}h^{2} = 0.0227 (Schaye 2001). As the same overdensity corresponds to a different N_{H i} at different z, the overdensity plotted in Fig. 7 is at the mean redshift, z = 2.55.
We compare our results with the observations by Petitjean et al. (1993) and Hu et al. (1995). Our results are in good agreement with the Petitjean et al. (1993) data over the whole column density range down to log N_{H i} ~ 13.5, following a power law at log N_{H i} = [12.75,14]. At smaller column densities log N_{H i} < 12.75, the CDDF starts to deviate from a power law due to the sample incompleteness for weak absorbers (Kim et al. 1997). From the linear regression, we find log ^{(}dn/(dN_{H i} dX)^{)}_{0} = 7.34 ± 0.42 and a slope of β = −1.43 ± 0.03 for the log N_{H i} = [12.75,14] range (the solid line). This result is slightly lower than β = −1.46 (no errors given) by Hu et al. (1995) (the dotted line) or β = −1.49 ± 0.02 by Petitjean et al. (1993).
The distribution function becomes steeper at log N_{H i} > 15, then becomes shallower at higher log N_{H i}, as previously observed (Petitjean et al. 1993; Kim et al. 1997; Prochaska et al. 2010). This result agrees well with the theoretical prediction at z ~ 3 (the dashed line) by Altay et al. (2011) at log N_{H i} ≤ 16, but starts to show a noticeable disagreement at the 1–3σ level at log N_{H i} = [16,18], in part due to the lack of enough highcolumn density systems in our small sample. We will address the shape of the CDDF in more detail in the next section using results from the highorder fit sample.
4. Analysis using higherorder Lyman lines
In the last section, we checked the Lyα absorber number density evolution and the differential column density distribution obtained from the Lyαonly fits for consistency with previous studies. The analysis is now revisited with the results from the Voigt profile analysis including the higher order transitions at 1.9 < z < 3.2, hence a sample with a more reliable N_{H i}. Therefore, it can be established whether the dip seen in the differential column density distribution at log N_{H i} between 14.5 and 18 is a physical feature or just an imprint of uncertainties in N_{H i}. All the results from this section are tabulated in Appendix A.
4.1. The mean number density evolution
We now revisit the line number density evolution using the highorder Lyman sample, as described in the previous section. On a quasar by quasar analysis we determine dn/dz for a low column density range of log N_{H i} = [12.75,14.0] and for high column densities of log N_{H i} = [14,17]. The lower column density range is chosen in such a way that the part of the differential column density distribution function which follows a powerlaw is covered, whereas the log N_{H i} = [14,17] interval covers those systems responsible for the dip in the column density distribution function.
The results are presented in Fig. 8. Linear regressions from the data are obtained and the resulting parameters are summarised in Table 2. Similar to the previous analysis, the line number density shows a decrease with decreasing redshift. No significant differences between the two different fits are present, even though the total redshift coverage used for the highorder fit is about 20% smaller. In the case of the log N_{H i} = [14,17] interval, the slope of the power law steepens from the Lyαonly slope of γ = 3.40 ± 0.36 to γ = 4.91 ± 0.53 for the highorder fit. This is in part caused by that the number of high column density absorbers is larger in the highorder fit sample. However, the slopes of the two samples are still in the 2σ uncertainty range, rendering the two results consistent to each other. Similar results are obtained for the log N_{H i} = [12.75,14.0] range. The slope for the highorder fit increases from γ = 1.13 ± 0.16 for the Lyαonly fit to γ = 1.38 ± 0.22. Again, the results from the two samples agree within the 1σ uncertainty range.
Fig. 8 Line number density evolution derived on a quasar by quasar analysis using the highorder Lyman sample for column density intervals of log N_{H i} = [12.75,14.0] and log N_{H i} = [14,17]. The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the redshift range covered by each sightline. The straight lines denote results from a linear regression to the data with parameters given in Table 2. The data are tabulated in Table A.1. 

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Fig. 9 Mean line number density evolution of the combined sample as a function of redshift using the highorder Lymanseries sample for column density intervals of log N_{H i} = [12.75,14.0] and log N_{H i} = [14,17]. The sample is binned in redshift with Δz = 0.26, starting from z = 1.90. The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the redshift range covered by each data point. For comparison, the results of the Lyαonly fits (grey open circles) are shown for the log N_{H i} = [14,17] interval. The straight solid lines denote results from a linear regression to the binned data. Two dashed lines represent the mean number density evolution of the Lyαonly fit sample for log N_{H i} = [14,17] (log dn/dz = (−0.41 ± 0.35) + (4.14 ± 0.63) × log (1 + z)) and for log N_{H i} = [12.75,14] (log dn/dz = (1.96 ± 0.13) + (1.12 ± 0.24) × log (1 + z)), respectively. Exactly same redshift range was used for both fit samples. The data are tabulated in Table A.2. The parameters of the fits are given in Table 2. 

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In previous studies the number density evolution has been usually derived on a quasar by quasar analysis. Previous studies did not have enough quasar sight lines available to sample the number density evolution at smaller redshift interval Δz, without suffering from small number statistics. Our sample of 18 highredshift quasars is characterised by a large redshift distance coverage in the redshift range of 1.9 < z < 3.2 (see Fig. 2). As a result, a large number of absorption lines is available for small redshift intervals to combine the individual quasar line lists into one big sample. However, due to the larger cosmic variance at low redshifts from the structure formation, the redshift bin size should not be too small. From this combined sample, the evolution of the mean number density is derived in redshift bins of Δz = 0.26, starting from z = 1.90.
Results of the combined line number density evolution are shown in Fig. 9 for identical column density ranges as used in the quasar by quasar analysis. Error bars have been determined using the bootstrap technique. For comparison, results using the Lyαonly fits are overplotted as grey open circles for the high column density bin.
The high column density results are similar to the ones obtained from the Lyαonly fits. The number density itself is higher in the highorder fits, since some strongly saturated systems break up into multiple, strong components in the highorder Lyman transition. In addition, three absorbers (two toward HE0940−1050 and one toward Q0420−388) were found to be a Lyman limit system with log N_{H i} > 17 in the Lyαonly fit. Therefore, these systems were not included in the Lyαonly results. However, these Lyman limit systems break up into multiple weaker components in the highorder fit and contribute to the number count in the high order fit analysis. However, the differences between the two samples are smaller than the statistical uncertainties.
At low column densities, no noticeable differences between the two samples are observed, as expected.
Again, linear regressions have been determined and their parameters are given in Table 2. At log N_{H i} = [12.75,14.0], the slope of our combined sample is γ = 1.28 ± 0.24, similar to γ = 1.38 ± 0.22 from the quasar by quasar analysis. At log N_{H i} = [14,17], the slope of the combined sample γ = 4.65 ± 0.66 is also similar to γ = 4.91 ± 0.53 obtained from the quasar by quasar analysis. The slopes from both analyses of our highorder fit sample at log N_{H i} = [14,17] are steeper than the ones obtained from the Lyαonly fit sample. In particular, the ones from the combined sample differ more than 3σ. This difference is mainly caused by that the redshift range used for the combined sample is different for two analyses. For the Lyαonly fit, the mean dn/dz is derived for 0 < z < 4, while for the highorder fit it is restricted to 1.9 < z < 3.2.
Fig. 10 Differential column density distribution at 1.9 < z < 3.2 of our quasar sample using the highorder Lyman fit. Black and grey data points show the results from our quasar sample. The grey data points below log N_{H i} < 12.75 mark the column densities that are affected by incompleteness. The grey data points above log N_{H i} > 12.75 represent the results from the Lyαonly fit. The dashed line represents a theoretical prediction at z ~ 3 by Altay et al. (2011). The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. The filled circles at log N_{H i} > 20.1 and at 19.0 < log N_{H i} < 20.1 are from the SDSS II DR7 at ⟨ z ⟩ = 3.02 by Noterdaeme et al. (2009) and from O’Meara et al. (2007) at ⟨ z ⟩ = 3.1, respectively. The solid line gives the power law fit to our data for log N_{H i} = [12.75,14.0]. The overdensity plotted on the top xaxis is calculated at z = 2.55. 

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4.2. The differential column density distribution function
Using the highorder fits, we have derived the differential CDDF for 1.9 < z < 3.2, analogous to Sect. 3.2. In Fig. 10 we show the results for the entire redshift range. As in Fig. 7, the binsize of log N_{H i} = 0.25 is used at log N_{H i} = [12.0,15.0], then the binsize of 0.5 at log N_{H i} = [15.0,18.0]. The total absorption distance is X(z) = 21.8165, the same value used for the Lyαonly fit CDDF analysis. As with the Lyαonly fits, we included observations by Noterdaeme et al. (2009) and O’Meara et al. (2007).
The highorder fit results show a power law relation which is almost identical to the results of the Lyαonly fits. As with the Lyαonly fits, the differential column density distribution function shows a deviation from the empirical power law at column densities between 14 < log N_{H i} < 19. Since the column density distribution deviates from a single power law at log N_{H i} ~ 14, we have individually fitted power laws to four column density intervals of log N_{H i} = [12.75,14.0], [14,15], [15,18], and [12.75,18.0] at 1.9 < z < 3.2, characterising the shape of the distribution function. The resulting parameters are listed in Table 3.
At log N_{H i} = [12.75,14.0], the linear regression yields a normalisation point of log ^{(}dN/(dN_{H i} dX)^{)}_{0} = 7.41 ± 0.42 and a slope of β = −1.44 ± 0.03. This result is almost identical to the Lyαonly fit, since differences between the Lyαonly and the highorder fits start to be significant at log N_{H i} > 15 (see Fig. 10). The highorder fits show a larger number of absorbers at 15 < log N_{H i} < 17 than the Lyαonly fits. However, at higher column densities, the number of absorbers is lower for the highorder fits than for the Lyαonly fits. This again indicates the breaking up of high column density systems into multiple lowerN_{H i} ones when including higher transitions than Lyα. For the entire redshift sample, the slope becomes steeper from ~–1.44 to ~–1.67 at log N_{H i} = [14,15]. Then at the higher column density range log N_{H i} = [15,18], the slope becomes shallower to ~–1.55, a trend shown in the numerical simulation (the dashed line) by Altay et al. (2011) in Fig. 10.
Linear regression results for the differential column density distribution as a function of redshift and column density using the highorder fit.
Fig. 11 Upper panels: differential column density distribution as a function of redshift. Both black and grey data points show the results from the highorder Lyman sample. The grey data points at log N_{H i} < 12.75 mark the column densities that are affected by incompleteness. The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. The black solid line gives the power law fit for log N_{H i} = [12.75,14.0] at each redshift bin, whereas the dashed line is the fit to the z = [1.9,3.2] redshift range (see Fig. 10). The overdensity plotted on the top xaxis is calculated at the mean z for each redshift bin. Lower panels: residuals from the power law fit from the entire redshift range at log N_{H i} = [12.75,14.0]. 

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In order to determine the redshift evolution of the differential column density distribution, we split the sample into two redshift bins: z = [1.9,2.4] and [2.4,3.2]. Figure 11 shows the CDDF at two different redshift bins, where we overplot the powerlaw fit at log N_{H i} = [12.75,14.0] for each redshift bin as the solid line. We also overplot the results of the powerlaw fit to the entire redshift range 1.9 < z < 3.2 at the same column density range as the dashed line. For the redshift intervals of z = [1.9,2.4] and [2.4, 3.2], the absorption distance is X(z) = 11.8049 and 10.0116, respectively.
Unfortunately, the uncertainties in the powerlaw fit parameters at each redshift bin do not allow us to constrain the shape of the distribution as a function of redshift reliably. Comparing the slope of the linear relations shows that the CDDF becomes slightly steeper at low redshift for log N_{H i} = [12.75,14.0], from γ = −1.38 ± 0.04 at high z to γ = −1.49 ± 0.04 at low z. However, the slopes are still consistent within 2σ, i.e. no significant CDDF evolution, cf. Williger et al. (2010). They are also consistent with the result from the entire redshift range within 1σ.
Let us now focus on column densities above log N_{H i} > 14.0. From Fig. 10 we have seen that the differential column density distribution deviates from the power law form for column densities log N_{H i} > 14.0. The lower panels of Fig. 11 show the difference between the observed CDDF and the powerlaw fit to the CDDF for the entire redshift range (the dashed lines). The entire redshift fit was used since the comparison requires an absolute reference. From the lower panels, it is clear that the deviation from the power law is stronger for the low redshift bin. At the same time, the deviation column density above which the deviation starts to be noticeable is lower at low redshift, from log N_{H i} ~ 14.7 at 2.4 < z < 3.2 to log N_{H i} ~ 14.0 at 1.9 < z < 2.4.
Note that no such break in the CDDF has been seen in the log N_{H i} = [12.5,16] at z < 2, cf. Fig. 5 of Williger et al. (2010) at z ~ 0.08 and Fig. 9 of Ribaudo et al. (2011) at z < 2. Both works also found a steeper CDDF slope of ~1.75. Some of the discrepancy is caused by the different fitting methods, the H i selection criterion discussed in Sect. 3.1 and the column density range over which the power law was performed. On the other hand, Prochaska et al. (2010) found a more significant dip in the column density distribution function at log N_{H i} = [14,19] at z ~ 3.7 (similar to the Altay simulation at z ~ 3 indicated by the dashed curve in Figs. 7 and 10). However, the dip shown at z ~ 3 in Fig. 11 (the high redshift bin) is not as strong as the one predicted by the Altay simulation, although both results are still considered to be consistent within 2σ. These differences could be simply due to our small sample size, or due to the different analysis method or due to the strong CDDF evolution between z ~ 4 and z ~ 0.
Note that the dip shown in Fig. 11 is not caused by selfshielding. Selfshielding causes the number density of absorbers to increase. Selfshielding becomes important at log N_{H i} ≥ 16 and its effect becomes evident at log N_{H i} ≥ 17 with a shallower slope than the extrapolated one at the lower log N_{H i} (Altay et al. 2011). However, the dip in discussion occurs at log N_{H i} = [14.5,17.0] compared to the extrapolated powerlaw slope at log N_{H i} = [12.75,14.5]. In addition, the deviation N_{H i} from this single power law starts at log N_{H i} ~ 14.5, where selfshielding has no effect.
5. Characteristics of the metal enriched forest
The discovery of metal lines which are associated with H i absorber in the Lyα forest, such as C iv or O vi (Cowie et al. 1995; Songaila 1998; Schaye et al. 2000a), have raised the question of how the IGM has been metal enriched. As the forest has a high temperature and a low gas density, it is not likely to form stars insitu. Metals should be transferred from galaxies by e.g. galactic outflows (Aguirre et al. 2001; Schaye et al. 2003; Oppenheimer & Davé 2006). In recent years, studies on galaxygalaxy pairs at high redshift have revealed some evidence that metals associated with the Lyα forest reside in the circumgalactic medium (Adelberger et al. 2005; Steidel et al. 2010; Rudie et al. 2012). In this interpretation, the metalenriched forest cannot be called the IGM in the conventional sense and is likely to show a different evolutionary behaviour compared to the metalfree forest. In order to learn more about these enriched hydrogen absorbers, we characterise C iv enriched H i absorbers in this section by determining their number density evolution and differential column density distribution. Note that we excluded Q0055−269 and J2233−606 for both the C iv enriched forest and the unenriched forest samples in this section, as their C iv region has a much lower S/N of ~ 40 per pixel compared to the other 16 quasar spectra whose S/N is greater than 100 per pixel in most C iv regions. Due to the wavelength gap caused by the UVES dichroic setup, the C iv redshift coverage is shorter than the H i coverage for Q0420−388, HE0940−1050 and HE2347−4342. We excluded the ± 200 km s^{1} region from the wavelength gap and included the C iv region only when it covered both doublets. The excluded C iv redshift range for these three quasars is listed in Table 1. In this section, we used the column density and b parameter of H i from the highorder Lyman fit, unless stated otherwise. All the results from this section are tabulated in Appendix A.
Fig. 12 Example of a velocity plot (a relative velocity vs normalised absorption profile plot) of H i and associated C iv detected in the z = 2.352743 absorber in the spectrum of HE1122−1648. The strongest C iv component is set to be at the zero velocity. The observed spectra are plotted as a histogram, while Voigtprofile fits are as a smooth curve. Thick red curves are the combined fit from individual components. The heavy tick marks above the profiles indicate the velocity centroid of each component. Nonnegligible blends by other ions are indicated in gray. The b value (in km s^{1}) and log N_{H i} with the VPFIT fitting errors are displayed next a tick mark indicating the center of the component. 

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5.1. Method
Unfortunately there is no onetoone relation between H i lines and C iv lines. Figure 12 shows a velocity plot (the relative velocity centered at the redshift of an absorber vs normalised flux) of a typical C ivenriched H i absorber in the spectrum of HE1122−1648. The vertical dashed lines indicate the velocity of individual H i components. Not all H i lines can be directly assigned to one or only one C iv component. For example, the H i component at −36.02 km s^{1} could be associated either with the first C iv component at −51.72 km s^{1} or with the second one at −19.97 km s^{1}, or with both. A general trend is that the associated C iv features show an increased number of velocity components as N_{H i} increases. The absorption line centers of H i and C iv lines often show velocity differences as well, indicating that the H iabsorbing gas might not be cospatial with the C ivproducing gas. Therefore, we apply a simple assigning method to our fitted absorber line lists, in order to determine if an H i absorption line is associated with C iv.
We consider an H i absorber to be metal enriched if a C iv line with N_{C iv} greater than a threshold value exists within the velocity range ± Δv_{C iv} centered at each identified H i line. The threshold N_{C iv} should be large enough not to be affected by the incompleteness of weak C iv detection, but not too large so that there are enough C iv enriched absorbers to have a meaningful statistics. This method can assign one H i component with multiple C iv components and vice versa. As we are not concerned with the onetoone relation between N_{C iv} and N_{H i} of each H i component, but the existence of the C iv line for a given search velocity range, the multiple assigning of the same component does not affect the results.
Two arbitrary choices of Δv_{C iv} are considered: a conservative narrow range of ± 10 km s^{1} (a minimum b value of a single Lyα absorption line is roughly 20 km s^{1}) and a more generous interval of ± 100 km s^{1}.
Fig. 13 C iv column density distribution. Filled circles are our results at the redshift range used for the highorder fit C ivenriched H i sample at 1.9 < z < 3.2. The CCD gap in the C iv region was accounted for. Red filled triangles and blue filled diamonds are from Pichon et al. (2003) at 1.5 < z < 2.3 and 2.3 < z < 3.1, respectively. Green open squares are taken from Songaila (2001) at 2.90 < z < 3.54. The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. The black dotted line shows the linear regression to filled circles at log N_{C iv} = [12.25,15.5]: log dn_{C iv}/(dN_{C iv}dX) = (11.41 ± 1.61) + (−1.85 ± 0.13) × log N_{C iv}. The solid line is the power law fit at log N_{C iv} = [12.25,13.5]: log dn_{C iv}/(dN_{C iv}dX) = (6.60 ± 1.23) + (−1.47 ± 0.10) × log N_{C iv}. The turnover at log N_{C iv} ~ 12.5 shown in green data is simply due to the incompleteness for weak C iv. Similarly, the turnover seen at log N_{C iv} ~ 12.1 in our data is also due to the incompleteness. 

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Figure 13 shows the C iv column density distribution function at 1.9 < z < 3.2 from our sample (black filled circles). For comparison, other results from the literature are also included: red filled triangles and blue filled diamonds from Pichon et al. (2003) at 1.5 < z < 2.3 and 2.3 < z < 3.1, respectively, and green open squares from Songaila (2001) at 2.90 < z < 3.54. The turnover seen in green open squares is due to the incompleteness effect, i.e. not all weak C iv can be detected due to noise.
Similar to the H i density distribution, the C iv CDDF does not fit with a single power law over a large N_{C iv} range. The Pichon et al. result even suggests that the C iv density distribution might have a nonlinear functional form. At log N_{C iv} = [12.25,13.5], a single powerlaw fit gives log dN/(dN_{C iv}dX) = (6.60 ± 1.23) + (−1.47 ± 0.10) × log N_{C iv} (the solid line). At log C iv = [12.25,15.5], a single power law is log dN/(dN_{C iv}dX) = (11.41 ± 1.61) + (−1.85 ± 0.13) × log N_{C iv} (the dotted line). If the solid line is taken as a reasonable CDDF since it fits the lowN_{C iv} CDDF better, our C iv detection can be considered complete at log N_{C iv} ≥ 12.2.
Another way to look at whether our N_{C iv} completeness limit is reasonable is with the column densityb value diagram. As seen in the 7th column of Table 1, the S/N differs for different sightlines, and changes even along a single spectrum. This makes it extremely difficult to quantify the correct 3σ detection limit for a dataset containing spectra with different S/N.
Fig. 14 Line width vs. column density for the C iv absorption lines along 16 sightlines excluding Q0055−269 and J2233−606 at the two redshift bins. Error bars are fitting errors from the VPFIT profile fitting. In the upper panel, two heavy dashed lines delineate a 3σ detection limit for a spectrum with S/N = 120 and S/N = 90 per pixel. At 2.4 < z < 3.2, most spectra show S/N greater than 90. In the lower panel, the heavy dashed line shows a 3σ detection limit for S/N = 120. Broader and weaker absorption lines at the left of the detection limit are missed in lower S/N spectra. The vertical dotted line indicates the adopted low N_{C iv} bound of log N_{C iv} = 12.2 above which the incompleteness does not affect the C iv detection significantly. The histogram shown with the base at log N_{C iv} = 12.2 is the number of C iv lines as a function of b_{C iv} with the b_{C iv} binsize of 2 km s^{1}. Thick ticks above the number distribution mark the median b_{C iv} for log N_{C iv} ≥ 12.2. The total number of C iv lines is 194 and 171 at 2.4 < z < 3.2 and 1.9 < z < 2.4, respectively. Among them, 138 and 122 lines have log N_{C iv} ≥ 12.2 at the same redshift range. 

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Figure 14 shows the log N_{C iv}–b_{C iv} diagram at the two redshift bins. The vertical heavy dotdashed lines mark log N_{C iv} = 12.2. In the upper panel, two heavy dashed lines show a 3σ detection limit for a spectrum with S/N = 120 (the left side) and 90 (the right side, an approximate lowest S/N) per pixel, respectively. In the lower panel, the heavy dashed line is a 3σ detection limit for S/N = 120. Absorption lines at the leftside of the detection limit, i.e. broader and weaker lines, can be only detected for S/N greater than the given S/N. Overlaid as a histogram is the distribution of the number of C iv lines with log N_{C iv} ≥ 12.2 as a function of b_{C iv}. For the distribution, the zero base is set to be log N_{C iv} = 12.2. Thick ticks above the distribution mark the median b_{C iv}. There is no correlation between N_{C iv} and b_{C iv} above the S/N detection limit at all of the reasonable expected b_{C iv} values.
At 2.4 < z < 3.2 (the upper panel), the 3σb_{C iv} detection limit is 23.6 (13.4) km s^{1} for S/N = 120 (90) at log N_{C iv} ~ 12.2. The total wavelength coverage of C iv at the high redshift bin is ~3192 Å. For about half of the spectra there is contamination from weak telluric lines in ≤10% of the C iv region. This contamination prevents isolated weak C iv lines from being detected, however, can be treated as a lowerS/N region. Including the telluriccontaminated region, the wavelength coverage with S/N ≤ 120 is about 1018 Å. In the C iv wavelength region with S/N ≥ 120, the total number of C iv lines with log N_{C iv} = [12.2,12.3] is 8. Out of those 8, none has b_{C iv} ≥ 23.6 km s^{1}. It is possible that a large fraction of C iv has a b_{C iv} value greater than 23.6 km s^{1}, and therefore, would be completely missed even in the highS/N spectra analysed here. However, as clearly seen in the upper panel of Fig. 14, the b_{C iv} distribution at log N_{C iv} ≥ 12.2 shows that only 9% of C iv has b_{C iv} ≥ 23.6 km s^{1}. If a large fraction of C iv lines were broader regardless of N_{C iv}, the region around log N_{C iv} ~ 12.4 and b_{C iv} ~ 25 km s^{1} in Fig. 14 should have been more crowded. Therefore, it is not likely that many weak C iv lines with b_{C iv} ≥ 23.6 km s^{1} have been missed for S/N ≥ 120.
Only 2 out of 8 have b_{C iv} ≥ 13.4 km s^{1} at log N_{C iv} = [12.2,12.3]. In other words, these 2 C iv lines would have been missed in the S/N ≤ 120 region. One is a single isolated line, while the other is part of a multicomponent C iv complex. We assumed that the number of C iv lines with log N_{C iv} ~ 12.2 and b_{C iv} ≥ 13.4 km s^{1} is 2 in the wavelength range of 2174 Å, i.e. the total wavelength range with S/N ≥ 120. If we assume that weak C iv lines have a negligible clustering, about 1 (or 2 × 1018/(3192−1018) = 0.9) C iv line with log N_{C iv} ~ 12.2 and b_{C iv} ≥ 13.4 km s^{1} could have been missed in the C iv forest region with S/N ≤ 120.
A total of 5 H i lines with log N_{H i} = [12.8,17.5] is found within ±100 km s^{1} centered at these two C iv lines. The total number of highorderfit H i lines in the H i forest region corresponding to the S/N ≥ 120 C iv forest region is [265, 363, 233, 120, 50, 27, 5] for log N_{H i} = [12.75–13.00, 13.0−13.5, 13.5−14.0, 14.0–14.5, 14.5–15.0, 15.0–16.0, 16.0–17.0], respectively. Among them, a negligible number of H i lines, [0, 0, 2, 0, 1, 0, 1], is associated with these two C iv lines for the same N_{H i} range, or less than 2%. The remaining one H i line has log N_{H i} ≥ 17.0 as the associated C iv line belongs to a C iv complex of a partial Lyman limit system. Although the number of undetected weak and broad C iv lines in the S/N ≤ 120 region is a very rough estimate, less than 2% of the H i lines would be misclassified as the unenriched forest due to the incompleteness at log N_{C iv} ~ 12.2.
The situation becomes more complicated in the low redshift bin, where the variance of the S/N limits of individual spectra is much higher than in the high redshift bin. If a similar logic were applied to, the total C iv coverage is 5485 Å, and the one with S/N ≤ 120 is 2719 Å. In the S/N ≥ 120 C iv region, there is a total of 10 C iv lines with log N_{C iv} = [12.2,12.3]. Out of 10, 6 lines have b_{C iv} ≥ 13.4 km s^{1}, the maximum b_{C iv} value to be detected for a line with log N_{C iv} = [12.2,12.3] in a S/N = 90 spectrum. Among those 6 C iv lines, two C iv lines are part of a twoisolatedcomponent complex, with the rest being part of a multicomponent complex. Since stronger H i lines tend to be associated with a C iv complex, using all these 6 C iv lines to calculate the associated H i fraction leads to a biased result. Therefore, we used 4 C iv lines which are part of a C iv complex with less than 3 components in order to estimate the missed enriched H i fraction.
There is a total of 11 H i lines at log N_{H i} = [12.75,16.00] within 100 km s^{1} centered at the 4 weak C iv lines. The ratio of the C iv enriched H i lines and the total H i lines in the wavelength regions corresponding to the S/N ≥ 120 C iv forest is [2/231, 4/315, 3/174, 1/59, 1/28, 0/10] for log N_{H i} = [12.75−13.00, 13.0–13.5, 13.5–14.0, 14.0–14.5, 14.5–15.0, 15.0–16.0], respectively, or ≤3%. Again the fraction of missed C iv is negligible even at the low redshift bin.
Note that our estimate on the true undetected C iv fraction is uncertain. However, from Fig. 13, the incompleteness at log N_{C iv} = 12.2 is less than 10% or within the 1σ Poisson error.
While it is clear that the incompleteness does not play a significant role in the H i detection down to log N_{H i} = 12.75 and the C iv detection down to log N_{C iv} = 12.2, the combination of the H i and C iv detection could introduce a bias in the C iv assigning method. The pixel optical depth method which correlates the optical depth of H i (τ_{H i}) and C iv (τ_{C iv}) at the same redshift shows that at z ~ 3 there is a onetoone positive correlation between the median τ_{H i} and the median τ_{C iv} down to log τ_{H i} ~ 0.15 or log N_{H i} ~ 13.73 for b_{H i} = 28 km s^{1} (a median b_{H i} of the forest at z ~ 2.5) (Schaye et al. 2003). Below log τ_{H i} ~ 0.15, the τ_{H i} signal is blended with noise at log τ_{C iv} ~ 0.001 or log N_{C iv} ≤ 11.0 for b_{C iv} = 9.5 km s^{1} (a median b_{C iv} of all the C iv lines in our UVES sample).
This result suggests that many lowN_{H i} absorbers might be misassigned as unenriched H i absorber in our C iv assigning method. Unfortunately, the lower log N_{C iv} ~ 11 limit that a typical optical depth analysis explores is an order of magnitude lower than our adopted low N_{C iv} limit of log N_{C iv} = 12.2. This log N_{C iv} ~ 11 limit cannot be obtained even in the highest S/N C iv region with S/N ≥ 220 in our UVES spectra. Therefore, our C iv analysis can not confirm, nor refute the results from the optical depth method.
Figure 15 shows the N_{H i}–N_{C iv} diagram for the Δv_{C iv} = ±100 km s^{1} sample (the upper panel) and for the Δv_{C iv} = ±10 km s^{1} sample (the lower panel). Since one H i line can be associated with several C iv lines, data points at the same N_{H i} represent the same H i absorber. Open circles show all the H i absorbers associated with all the possible C iv lines. Red filled circles indicate H i absorbers associated with only one closest C iv within the search velocity range. With a larger search velocity range, the Δv_{C iv} = ±100 km s^{1} sample has more lines. The number of the red filled circles increases abruptly at log N_{H i} ≤ 15.0 at both redshift ranges, more prominently at the high redshift bin. This is simply due to the fact that the number of weaker H i absorbers is larger than stronger H i absorbers.
Fig. 15 N_{H i} − N_{C iv} diagram for log N_{H i} = [12.0,17.8] from the highorder fit sample at 1.9 < z < 3.2. The upper panel is for the Δv_{C iv} = ±100 km s^{1} sample, while the lower panel for the Δv_{C iv} = ±10 km s^{1} sample. Open circles represent H i absorbers associated with all the possible C iv components, since a single H i line could be assigned to several C iv lines. On the other hand, red filled circles indicate a H i absorber associated with only one closest C iv. The total number of open (filled) circles is 1082 (451) and 184 (163) for the Δv_{C iv} = ±100 km s^{1} and Δv_{C iv} = ±10 km s^{1} sample, respectively. 

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If our C iv assigning method were biased due to our failure to detect C iv lines toward lower N_{H i} values, there should be a correlation in N_{H i} and N_{C iv}, such that a lower N_{H i} line tends to be associated with a lower N_{C iv} line (cf. the relation between the median τ_{H i} and the median τ_{C iv}) or the number of the C ivenriched H i lines at lower N_{H i} is smaller. No such correlations are seen in Fig. 15. Note that our method deals with the fitted individual lines, while the optical depth analysis works with statistical, median values. The optical depth analysis is not sensitive to any minor C iv population, such as highmetallicity absorbers (Schaye et al. 2007).
In reality, the detection of weak C iv is dependent on the local S/N as well as the combination of b_{C iv} and N_{C iv}. The S/N of a spectrum does not change in a way to satisfy a higher S/N at strong H i absorbers and a lower S/N at weaker H i absorbers or vice versa. Usually the S/N changes over a larger wavelength interval than the wavelength interval between typical strong H i lines. In addition, strong and weak H i lines do not occupy a portion of a spectrum separately, but exist mixed along the spectrum. If a weak C iv were detected associated with a highN_{H i} line, a similar strength of C iv, if exists, should be detected for lowN_{H i} lines nearby or in a similar S/N region. Therefore, unless a majority C iv fraction at lower N_{C iv} and/or lower N_{H i} has a very large b_{C iv} value, i.e. high gas temperature, our C iv assigning method does not introduce a serious selection bias within the adopted N_{C iv} limit.
5.2. Results
5.2.1. Number density evolution of the C ivenriched absorbers
In a similar way to the analysis of all the H i absorbers, we calculate the absorber number density evolution dn_{H i + C iv}/dz on a quasar by quasar analysis for all the C ivenriched H i absorbers. The resulting dn_{H i + C iv}/dz evolution is shown in Fig. 16 for the Δv_{C iv} = ±100 km s^{1} and Δv_{C iv} = ±10 km s^{1} interval from the highorder Lyman fit samples. For the Δv_{C iv} = ±100 km s^{1} sample, the Q1101−264 sightline does not show any C iv in the redshift range of interest due to its short redshift coverage. For the Δv_{C iv} = ±10 km s^{1} sample, 7 sightlines (HE2347−4342, Q0002−422, PKS0329−255, HE1347−2457, Q0109−3518, Q0122−380 and Q1101−264) out of 16 have no C ivenriched H i absorbers at log N_{H i} = [12.75,14.0], while only only sightline (Q1101−264) has no C ivenriched H i absorber at log N_{H i} = [14,17]. This is caused by the combination of two facts that C iv tends to be associated with strong H i absorbers and that the small search velocity is not adequate due to the velocity difference between H i and C iv observed in many enriched absorbers. For these sightlines, dn_{H i + C iv}/dz is 0. Therefore, their log n_{H i + C iv}/dz is set to be 0 with a downward arrow in Fig. 16.
Fig. 16 Quasar by quasar line number density evolution of C ivenriched H i absorbers. The left panels are derived from the Δv_{C iv} = ±100 km s^{1} sample, while the right panels represent the Δv_{C iv} = ±10 km s^{1} one. The open circles in the upper panel represent C ivenriched absorbers having log N_{H i} = [12.75,14.0] and the filled circles in the lower panel represent log N_{H i} = [14,17]. The vertical error bars mark 1σ Poisson errors, while the xaxis error bars show the redshift range covered by each sightline. Sightlines having no C ivenriched H i absorbers for a given velocity range are plotted at log ddn_{C iv}/ddz = 0. The solid lines represent linear regressions to the data, using the parameters summarised in Table 4. Sightlines with no C ivenriched absorbers, log dn_{H i + C iv}/dz is plotted to be 0 with a downward arrow. 

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Fig. 17 Fraction of the C ivenriched H i absorber number density to the total absorber number density as a function of redshift. The left panel is derived from the Δv_{C iv} = ±100 km s^{1} sample, while the right panel represents the Δv_{C iv} = ±10 km s^{1} sample. The black open circles represent a column density interval of log N_{H i} = [12.75,14.0] and the filled circles represent log N_{H i} = [14,17]. The vertical error bars mark 1σ Poisson errors, while the xaxis error bars show the redshift range covered by each sightline. In the left panel for log N_{H i} = [12.75,14.0] (gray open circles), two lowest data points at log (1 + z) ~ 0.5 (or z ~ 2) including an upper limit are from Q1101−264 and Q0122−380. Both have a short redshift coverage, therefore become more susceptible to cosmic variance. Sightlines with no C ivenriched H i absorbers are plotted as upper limits with an arbitrary value of 0.05 and 0.002 for log N_{H i} = [14,17] and log N_{H i} = [12.75,14.0], respectively. 

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As for the entire Lyα forest analysis, the dn_{H i + C iv}/dz evolution resembles a power law. Therefore, linear regressions have been obtained from the data set and its results are summarised in Table 4. Sightlines showing no C ivenriched H i absorbers were not included in the regression. Similar to the entire highorderfit H i sample, the Δv_{C iv} = ±100 km s^{1} sample shows a decline in the C ivenriched absorber number density with decreasing redshift. This behaviour is present in both column density ranges. Comparing these results with the quasarbyquasar dn/dz of the entire highorder fit sample at 1.9 < z < 3.2 shows that the C ivenriched absorbers at log N_{H i} = [14,17] has a steeper slope (5.27 ± 0.99), but completely consistent within 1σ. The robust result on the dn_{H i + C iv}/dz evolution requires a large redshift coverage and more sightlines per redshift coverage, especially at high column density range. With a lack of more C iv forest data at z > 3, dn_{H i + C iv}/dz derived in this study at the high column density range should be considered less robust compared to the entire H i dn/dz. Similarly, the dn_{H i + C iv}/dz slope (0.78 ± 0.92) at log N_{H i} = [12.75,14.0] is also consistent with the one (1.38 ± 0.22) of the entire highorderfit forest sample, given the rather large uncertainty. The actual number densities are lower at both column density ranges.
This becomes apparent in the left panel of Fig. 17, where the ratios of the number densities of the C ivenriched systems dn_{H i + C iv}/dz and the number density of the entire sample dn/dz are shown. The results for the Δv_{C iv} = ±100 km s^{1} sample (filled circles) show that there is no significant evolution of the C iv enrichment fraction for log N_{H i} = [12.75,14.0]. For log N_{H i} = [14,17], the enrichment fraction is consistent with no redshift evolution, considering a large scatter at z ~ 2 and a lack of data at z > 3. For the low column density log N_{H i} = [12.75,14.0] sample we find that around 5% of all the H i absorbers show C iv enrichment. The C iv enrichment fraction is higher for larger column densities of log N_{H i} = [14,17], where around 40% of the absorbers are C ivenriched.
Linear regression results for the number density evolution dn_{H i + C iv}/dz of the C ivenriched H i forest absorbers in the quasar by quasar analysis.
This picture changes slightly for the Δv_{C iv} = ±10 km s^{1} sample. For the high column densities, the dn_{H i + C iv}/dz evolution is less strong compared to the one of the Δv_{C iv} = ±100 km s^{1} sample. However, both are still consistent within 1σ due to a large uncertainty. Only the number density itself decreases by a factor of 1.7. The enrichment fractions in the right panel of Fig. 17 show that now around 20% to 30% of the high column density H i absorbers are C ivenriched.
On the other hand, dn_{H i + C iv}/dz increases with decreasing redshift for the low column densities. Its negative slope of γ = −2.49 ± 1.94 shows an opposite behaviour from the one (γ = 0.78 ± 0.92) of the Δv_{C iv} = ±100 km s^{1} sample. This negative slope is in part caused by the inadequacy in our C iv assigning method at the small search velocity, and in part by the fact that the number of highmetallicity absorbers increases at low redshift (Schaye et al. 2007). However, due to several sightlines containing no C ivenriched weak H i absorbers which are not included in the powerlaw fit, the negative slope should not be taken literally. The fraction of enriched absorbers increases from ~0.5% at z ~ 3 to ~1.5% at z ~ 2.1, as expected from dn_{H i + C iv}/dz at the low H i column density. However, keep in mind that the cosmic variance is large as some sightlines show no enriched weak H i absorbers.
Fig. 18 A velocity plot of a highly enriched C iv absorber at z = 2.030069 toward HE1122−1648. The zero velocity is centered at z = 2.030069. Although there is no obvious Lyα absorption seen at the zero velocity, both C iv and N v doublets are present to secure the existence of this absorber. Note that the yaxis range for each ion is different: from the normalised flux 0 to 1 for H i and from 0.5 to 1 for the rest of the ions. 

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There are two distinct groups of C iv absorbers assigned to the low H i column density. One group is associated with strong, saturated high column density H i absorbers. These absorbers are sometimes accompanied by lower N_{H i} absorbers within a velocity range of Δv < 200 km s^{1}. In these systems, the C iv absorption is usually found within 20 km s^{1} to the strongest H i lines (Kim et al. 2013, in prep.). Therefore, these accompanied low H i column density systems get associated with the C iv absorbers if the velocity range Δv_{C iv} is large. With a small velocity search interval, however, only H i systems that have C iv in their direct vicinity are flagged as C ivenriched. This means that the aforementioned low column density systems around strong absorbers are not considered C ivenriched in a small velocity search interval.
Another C ivenriched group consists of usually isolated, low column density H i absorbers associated with strong C iv absorption, i.e. the same highmetallicity forest population studied by Schaye et al. (2007). An example of such a system toward HE1122−1648 is shown in Fig. 18. In this velocity plot, an H i absorption feature is hardly recognisable, while strong C iv and N v doublets are present. The existence of both doublets makes the identification of this absorber secure. Due to the low N_{H i} and high N_{metals}, these systems show a higher ionisation and a higher metallicity compared to a typical absorber with similar N_{H i} (Carswell et al. 2002; Schaye et al. 2007). Schaye et al. (2007) speculate that these systems could be responsible for transporting metals from galaxies to the surrounding IGM. As the velocity difference between H i and metal lines for these systems are usually very small, they dominate the weaker C ivenriched forest at log N_{H i} < 14 for the Δv_{C iv} = ±10 km s^{1}. In addition, the highmetallicity absorbers are more common at low redshift.
The different characteristics of these two C iv groups explains the different dn_{C iv}/dz behaviour between the Δv_{C iv} = ±100 km s^{1} and Δv_{C iv} = ±10 km s^{1} samples at log N_{H i} = [12.75,14.0]. With recent observational evidence that metals are only found close to galaxies in the circumgalactic medium at 2 < z < 4 and not far away from galaxies (Adelberger et al. 2005; Steidel et al. 2010), our results could provide further theoretical constraints for this interpretation. It could well be that the highmetallicity forest population is completely different from the typical, lowmetallicity forest and resides in a different intergalactic space. However, due to the low gas density and high temperature, the Lyα forest does not have insitu star formation. Metals associated with the H i forest should have been transported from nearby galaxies. In other words, all the C ivenriched absorbers are close to galaxies.
Fig. 19 Distribution function for C ivenriched H i lines for Δv_{C iv} = ±100 km s^{1} (red filled squares) and for Δv_{C iv} = ±10 km s^{1} (blue stars) at 1.9 < z < 3.2. Also shown is the differential column density distribution function for all H i Lyα absorbers excluding Q0055−269 and J2233−606 (black filled circles) in the same redshift range analysed for the C ivenriched forest. The solid line indicates the fit to filled circles for log N_{H i} = [12.75,14.0]: log dN/(dN_{H i}dX) = (7.43 ± 0.44) + (−1.44 ± 0.03) × log N_{H i}. The vertical errors indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. All the grey data points indicate that the data are incomplete at log N_{H i} < 12.75. 

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5.2.2. Differential column density distribution function of the C ivenriched forest
Figure 19 shows the differential column density distribution function for C ivenriched H i absorbers for 1.9 < z < 3.2. Red filled squares and blue stars represent the search velocity ranges of Δv_{metal} = ±100 km s^{1} and Δv_{metal} = ±10 km s^{1}, respectively. Black filled circles are for all H i lines (excluding J2233−606 and Q0055−269), regardless of their metal association. As in Figs. 7 and 10, the binsize of log N_{H i} = 0.25 is used at log N_{H i} = [12,15], then the binsize of 0.5 at log N_{H i} = [15,18]. The solid line indicates the fit to filled circles for log N_{H i} = [12.75,14.0]: log dN/(dN_{H i}dX) = (7.43 ± 0.44) + (−1.44 ± 0.03) × log N_{H i}. The total absorption distance is X(z) = 19.6652 for the redshift ranges analysed in this subsection.
For log N_{H i} > 15, the CDDF of the enriched forest is not sensitive to our choice of the search velocity and the Δv_{metal} = ±100 km s^{1} CDDF becomes almost identical with the CDDF of the entire H i sample. For the column densities log N_{H i} = [14,17], the CDDF functional form of the enriched forest shows a powerlaw with a similar slope obtained for the entire H i absorbers at log N_{H i} = [12.75,14.0], but with a smaller normalisation value.
At log N_{H i} < 15, the distribution function of the C ivenriched forest starts to deviate significantly from the CDDF of the entire H i sample. The CDDF of the C ivenriched H i forest starts to flatten out toward lower N_{H i} at both search velocity ranges. Furthermore the flattening of the enriched forest depends strongly on the choice of Δv_{C iv}. The large search velocity results in a steeper slope with a less fluctuation than the small one. This is due to the Δv_{C iv} = ±10 km s^{1} sample being predominantly sensitive to highly enriched absorbers at log N_{H i} < 14 and less sensitive to misaligned broad C iv lines with b ≥ 10 km s^{1}. Note that our method to associate H i with C iv is only dependent on the relative velocity difference between the line centers, but not the C iv profile shape. The large velocity range includes broader C iv lines up to b ~ 100 km s^{1} as well as narrow, highly enriched absorbers. The Δv_{C iv} = ±100 km s^{1} velocity range is a better filter to associate H i and C iv.
The flattening of the distribution function seen at log N_{H i} < 15 by C ivenriched absorbers cannot be caused by the incompleteness of the H i sample. The H i incompleteness would result in a similar flattening as is seen at log N_{H i} < 12.75 for the entire sample (as seen in Fig. 7). However, our sample of H i absorbers is complete for column densities larger than log N_{H i} > 12.75.
Fig. 20 Differential column density distribution function for enriched and unenriched absorbers in our highorder fit sample using Δv_{C iv} = ±100 km s^{1} (upper panels) and Δv_{C iv} = ±10 km s^{1} (lower panels) at the two different redshift ranges. Black filled circles and gray stars mark unenriched absorbers. Red filled squares and blue stars indicate the C ivenriched forest for the Δv_{C iv} = ±100 km s^{1} sample and the Δv_{C iv} = ±10 km s^{1} sample, respectively. Both Q0055−269 and J2233−606 are excluded in the analysis. The solid black line indicates the fit to the entire H i sample and the whole redshift range at log N_{H i} = [12.75,14.0] as in Fig. 19. Red and blue dashed lines represent the fit to each C ivenriched forest sample at log N_{H i} = [14.5,17]. The vertical errors indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. The lower parts of the panels show the difference between the observed CDDF and the expected CDDF from the powerlaw fit obtained for the entire H i samples (black solid lines). 

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As discussed in Sect. 5.1, the flattening of the C ivenriched forest at log N_{H i} < 15 could be in part caused by the missed weak, broad C iv lines. However, Fig. 13 shows that the C iv CDDF at log N_{C iv} > 12.2 is not strongly affected by the C iv incompleteness. The number ratio of the entire H i forest lines and the C ivenriched forest lines at log N_{H i} ~ 13 is ~25 for the Δv_{C iv} = ±100 km s^{1} sample. This ratio increases to ~260 for the Δv_{C iv} = ±10 km s^{1} sample. Even if we took a maximum correction for the C iv incompleteness of 50%, roughly consistent with the results by Giallongo et al. (1996) (see their Sect. 2.3), the CDDF flattening of the enriched forest toward lower N_{H i} is still present. Therefore, this flattening is real and physically related to the characteristics of the C ivenriched absorbers only with N_{C iv} > 12.2.
Linear regression results for the differential column density distribution of the C ivenriched forest at log N_{H i} = [14.5,17.0].
The observation that the differential column density distribution of the C ivenriched forest flattens at low column densities can be easily explained by the fact that the enrichment fraction with log N_{C iv} > 12.2 becomes smaller as N_{H i} decreases.
At log N_{H i} = [13.0,13.5,14.0,14.5,15.0,15.5], the fraction of the metal enriched forest for the Δv_{C iv} = ±100 km s^{1} sample is roughly [4,6,11,31,49,78]%, respectively. This enrichment fraction can be roughly inferred from the difference between the entire H i CDDF and C ivenriched CDDF in Fig. 19.
The different CDDF shape between the C ivenriched absorbers and unenriched absorbers strongly supports that the C ivenriched absorbers arise from the different physical environment, i.e. the circumgalactic medium, while the unenriched forest has its origin as the intergalactic medium. The fact that the number of C ivenriched absorbers decreases with decreasing N_{H i} is also consistent with the picture of IGM metal enrichment models by galactic winds (Aguirre et al. 2001). The lower the H i column density of absorbers is, the farther they are from highdensity gas concentrations where galaxies are formed. As galactic winds have a limited life time and outflow velocity to transport metals in to the lowdensity IGM, weaker absorbers will not be likely to be metal enriched.
The redshift evolution of the distribution function of C ivenriched and unenriched absorbers (not the entire H i absorbers) is shown in the upper panels of Fig. 20 for the Δv_{C iv} = ±100 km s^{1} sample and in the lower panels for the Δv_{C iv} = ±10 km s^{1} sample at the redshift ranges z = [1.9,2.4] and [2.4, 3.2]. The total absorption distance is X(z) = 11.0940 and 8.57061 at z = [1.9,2.4] and [2.4, 3.2], respectively, excluding Q0055−269 and J2233−606. To increase the absorber number for each N_{H i} bin, the binsize of log N_{H i} = 0.4 is used at log N_{H i} = [12.75,17.95]. Black filled circles and gray stars mark absorbers without C iv. Red filled squares and blue stars indicate the C ivenriched forest for the Δv_{C iv} = ±100 km s^{1} sample and the Δv_{C iv} = ±10 km s^{1} sample, respectively. The solid black line indicates the powerlaw fit to the entire H i sample at 1.9 < z < 3.2 and at log N_{H i} = [12.75,14.0] as in Fig. 10. Red and blue dashed lines represent the powerlaw fit to each C ivenriched forest sample at log N_{H i} = [14,17] (see Table 5).
The upper panel of Fig. 20 suggests that the entire absorber population can be considered as the combination of two populations of wellcharacterised absorbers, the enriched absorbers and the unenriched absorbers. The C ivenriched absorbers dominate at log N_{H i} > 15. Their CDDF is welldescribed as a power law. The slope β ~ −1.45 obtained at log N_{H i} = [14.5,17] (red dashed lines) is similar to the slope β ~ −1.44 for the entire H i sample at log N_{H i} = [12.75,14.0] at both redshifts. The normalisation value for the C ivenriched forest is smaller, with about 10 times lower absorber numbers. The enriched absorbers do not show any strong redshift evolution at log N_{H i} > 15, while the CDDF flattening at log N_{H i} < 15 seems to be weaker at the low redshift. The unenriched absorbers dominate at log N_{H i} < 15 with a powerlaw CDDF. At higher N_{H i}, the unenriched absorbers become significantly deviated from the extrapolated power law obtained at lower N_{H i}. There are no unenriched absorbers at log N_{H i} ≥ 16.0.
The lower panels for the Δv_{C iv} = ±10 km s^{1} sample show similar results. There is no strong redshift evolution for the C ivenriched absorbers at log N_{H i} > 15. Again, there is a suggestion that the flattening at log N_{H i} < 15 becomes less significant at lower redshifts. The unenriched forest starts to dominate at log N_{H i} < 15. However, the Δv_{C iv} = ±10 km s^{1} sample shows a lesssmooth CDDF. The highestN_{H i} data point at z = [2.4,3.2] illustrates the inadequacy of our assigning method of C iv to H i when a small search velocity was used. Absorbers contributing this data point are part of multicomponent high column density systems. Their associated C iv shows a rather simple, broad, but misaligned profile from the H i center of saturated Lyα profiles. The H i line center of some components resolved at highorder Lyman lines is sometimes at ≥10 km s^{1} from the closest C iv component and thus they are flagged as the unenriched forest.
In both samples, the two populations overlap at log N_{H i} ~ 15. The deviation from the power law starting at log N_{H i} ~ 14.0–14.5 shown in the CDDF for the entire H i sample in Fig. 10 is a result of combining two different populations which show a different CDDF shape.
H i density relative to the critical density at log N_{H i} = [12.75,17.0]: the entire H i forest Ω_{H i}, the enriched forest Ω_{H i + C iv} and the unenriched forest Ω_{H i − C iv}.
To obtain a rough idea on the H i density relative to the critical density of the entire forest (Ω_{H i}), the enriched forest (Ω_{H i + C iv}) and the unenriched forest (Ω_{H i − C iv}), we used Eq. (A12) from Schaye (2001) at log N_{H i} = [12.75,17.0], by directly integrating the observed CDDF. The ionisation fraction of H i to obtain the total hydrogen density Ω_{H} was not corrected, since it is highly uncertain. The resulting mass fractions for log N_{H i} = [12.75,17.0] are given in Table 6. The derived CDDF is not wellconstrained at log N_{H i} ≥ 16 due to the low number statistics. Therefore, the derived Ω values in Table 6 are only rough numbers. The ratio of Ω_{H i + C iv} and Ω_{H i} might decrease by a factor of 2 at low redshift for both Δv_{C iv} samples. However, a high uncertainty in deriving Ω values does not allow to assure this decrease. The C ivenriched forest accounts for ~40% of the entire forest in mass at 1.9 < z < 3.2 for the Δv_{C iv} = 100 sample.
6. Conclusions
Based on an indepth Voigt profile fitting analysis of 18 highredshift quasars obtained from the ESO VLT/UVES archive, we have studied ~3100 H i absorbers to investigate the number density evolution and the differential column density distribution function at 1.9 < z < 3.2 and for log N_{H i} = [12.75,17.0]. Two methods of the Voigt profile fitting analysis have been applied, one by fitting absorption profiles only to the Lyα transition and another by including higher order Lyman transitions such as Lyβ and Lyγ. These higher order transitions provide a more reliable column density measurement of saturated absorption systems, since saturated and blended lines often become unsaturated at higher order transitions. This also enables us to resolve the structure of absorbers more reliably. This study has increased the sample size by a factor of 3 from previous similar studies at z > 2. In addition, we have investigated whether there exist any differences in the N_{H i} evolution between the C ivenriched forest and the unenriched forest.
We have found that the results based on the Lyαonly fit are in good agreement with previous results on a quasar by quasar analysis. For our data only (values in parenthesis indicate results including highquality data from the literature at z > 1), the number density dn/dz is dn/dz = (1.46 ± 0.11) × (1 + z)^{1.67 ± 0.21} ((1.52 ± 0.05) × (1 + z)^{1.51 ± 0.09}) and dn/dz = (0.03 ± 0.20) × (1 + z)^{3.40 ± 0.36} ((0.72 ± 0.08) × (1 + z)^{2.16 ± 0.14}) at log N_{H i} = [13.1, 14.0] and [14, 17], respectively. The noticeable difference in the exponent between our sample and the sample including the data from the literature for stronger absorbers is caused by the fact that our sample does not cover a large redshift range and that the evolution of dn/dz is more significant for stronger absorbers. The scatter between different sightlines becomes larger at lower redshifts and stronger absorbers due to the evolution of the largescale structure.
Combining our Lyαonly fit analysis at 1.9 < z < 3.6 with the highquality literature data at 0.0 < z < 4, the mean number density evolution is not well described by a single power law and strongly suggests that its evolution slows down at z ≤ 1.5 at both high and low column density ranges. Although a single power law does not give a good description, the number density is dn/dz ∝ (1 + z)^{0.89 ± 0.06} and dn/dz ∝ (1 + z)^{1.61 ± 0.12} at log N_{H i} = [13.1,14.0] and [14,17], respectively.
The differential column density distribution function (CDDF) from the Lyαonly fit analysis is also consistent with previous results. The single powerlaw exponent is −1.44 ± 0.02 at 1.9 < z < 3.2 and log N_{H i} = [12.75–14.0], with a deviation from the power law at log N_{H i} > 14.0−14.5.
The highorder Lyman fits do not show any significantly different results from the ones based on the Lyαonly fits. The dn/dz evolution based on a quasar by quasar analysis yields a very similar result to the Lyαonly fit. The mean dn/dz based on the combined sample from our quasars at 1.9 < z < 3.2 is dn/dz = (1.89 ± 0.13) (1 + z)^{1.28 ± 0.24} and dn/dz = (−0.65 ± 0.36) (1 + z)^{4.65 ± 0.66} at log N_{H i} = [12.75, 14.0] and [14, 17], respectively.
Using the highorder fits, we have derived the differential column density distribution function at 1.9 < z < 3.2 and confirm the existence of a dip at log N_{H i} = [14,18] as seen in the Lyαonlyfit CDDF analysis. At 1.9 < z < 3.2, the powerlaw exponent of the differential column density distribution function is −1.44 ± 0.03, −1.67 ± 0.09 and −1.55 ± 0.08 at log N_{H i} = [12.75,14.0], [14, 15] and [15, 18], respectively.
By obtaining the differential column density distribution function for two redshift bins z = [1.9,2.4] , and [2.4, 3.2], we observe that a deviation from the expected powerlaw at log N_{H i} = [14.0,18.0] is more prominent at lower redshifts. In addition, the powerlaw seems to be slightly steeper at the low redshift for the column density range log N_{H i} = [12.75,14.0] in which the distribution function follows a perfect single power law. However, the CDDF at two redshift bins is consistent with no redshift evolution within 2σ.
Further, we have split the entire H i absorbers excluding 2 quasars with a lower S/N C iv region into two samples: absorbers associated with C iv tracing the metal enriched forest, and absorbers associated with no C iv tracing the unenriched forest. A H i absorber is considered C ivenriched, if a C iv line with log N_{C iv} greater than a threshold value is found within a given search velocity interval centered at each H i absorption center. The threshold log N_{C iv} = 12.2 was used since the C iv distribution function and the N_{C iv}–b_{C iv} diagram show that the C iv detection is reasonably complete down to log N_{C iv} = 12.2 for a typical b_{C iv} value found at log N_{C iv} ≥ 12.2 in our sample. We used two arbitrarily chosen search velocity intervals, Δv_{C iv} = ±100 km s^{1} and Δv_{C iv} = ±10 km s^{1}.
At log N_{H i} = [14,17], the dn_{H i + C iv}/dz of the C ivenriched H i absorbers show a similar evolution compared to the one of the entire Lyα forest, with a powerlaw decrease in number density with decreasing redshift. The powerlaw slope is [0.78 ± 0.92,5.27 ± 0.99] for log N_{H i} = [12.75,14.0] and [14, 17] at 1.9 < z < 3.2 for the Δv_{C iv} = ± 100 km s^{1} sample.
The enriched fraction is fairly constant with redshift at 1.9 < z < 3.2. About 5% of all absorbers show an association with C iv at log N_{H i} = [12.75,14], while about 40% are metal enriched at log N_{H i} = [14,17] for the Δv_{C iv} = ±100 km s^{1} sample.
For Δv_{C iv} = ±10 km s^{1} sample, the low column density enriched absorber suggests that dn_{H i + C iv}/dz increases as redshift decreases, i.e. a negative slope of −2.49 ± 1.94. Part of this behaviour is caused by the fact that highmetallicity absorbers which are more sensitive to the small search velocity become more abundant at low redshift. However, this negative evolution should not be taken literally since about a half of sightlines does not show enriched absorbers at log N_{H i} = [12.75,14.0].
The differential column density distribution function for the enriched and unenriched systems show a significant difference. However, each shows a wellcharacterised CDDF. At log N_{H i} ≤ 15.0, the unenriched forest dominates and its distribution shows a power law similar to the entire forest sample. On the other hand, the C ivenriched forest is found to flatten out at log N_{H i} ≤ 15. Depending on the search velocity interval, the number of enriched systems is a factor of 25 (Δv_{C iv} = ±100 km s^{1}) to 260 (Δv_{C iv} = ±10 km s^{1}) lower than the one of the unenriched systems at log N_{H i} = 13. This flattening is mainly caused by the fact that the enriched fraction of the Lyα forest decreases as log N_{H i} decreases.
At the higher N_{H i} range, the C ivenriched forest dominates. Its distribution function can be described as a power law with its slope of −1.45 ± 0.08 similar to the powerlaw slope (−1.44 ± 0.03) of the entire H i forest at log N_{H i} = [12.75,14.0], but a lower normalisation value, i.e. ~10 times lower in the absorber number. The unenriched forest disappears very rapidly as log N_{H i} increases.
The distribution function of the entire H i forest can be described as the combination of these two wellcharacterised populations, overlapping at log N_{H i} ~ 15. The deviation from the power law at log N_{H i} = [14,17] seen in the CDDF for the entire H i sample is a result of combining two different H i populations with a different CDDF shape. This result supports other observational evidence from absorbergalaxy studies at z ~ 3, namely that metals associated with the highredshift Lyα forest are within ~100 kpc of galaxies (Adelberger et al. 2005; Steidel et al. 2010). Absorbergalaxy studies suggest that the C ivenriched and unenriched forest would arise from the different spatial and physical locations, therefore having a different physical/evolutionary behaviour suggested by the different CDDF shape. Therefore, our results combined with absorbergalaxy studies indicate that the C ivenriched forest is the circumgalactic medium, while the unenriched forest has its origin as the intergalactic medium.
At 1.9 < z < 3.2, the C ivenriched forest contributes ~40% of the entire forest mass to the H i density relative to the critical density for the Δv_{C iv} = ± 100 km s^{1} sample.
Carswell et al.: http://www.ast.cam.ac.uk/~rfc/vpfit.html
The STIS spectrum is taken from http://www.stsci.edu/ftp/observing/hdf/hdfsouth/hdfs.html (Savaglio et al. 1999).
The fitted line parameters by Janknecht et al. (2006) show many H i lines with b < 20 km s^{1}, about 25% of all lines. They attribute this to their low signaltonoise data of less than 10 per resolution element. Although there are 9 sightlines analysed, one sightline has a long wavelength coverage from VLT/UVES and HST/STIS. This sightline was split into two data points in Figs. 4 and 5.
The plotted data points from the literature are the reported ones in each study. Both O’Meara et al. (2007) and Noterdaeme et al. (2009) used the same cosmology as ours, while Petitjean et al. (1993) used the q_{0} = 0 cosmology. The absorption distance X(z) in our cosmology is about 6% smaller than theirs. Since the CDDF uses the logarithm value of X(z), the difference in the CDDF is negligible even without converting their CDDF to our cosmology.
Acknowledgments
We are grateful to Drs. Martin Haehnelt, Jamie Bolton and Gerry Williger for insightful discussions and comments. We are also grateful to our anonymous referee for very constructive comments. A.P. acknowledges support in parts by the German Ministry for Education and Research (BMBF) under grant FKZ 05 AC7BAA.
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Online material
Appendix A: Data
In this appendix we present all the data for the quasar by quasar number density evolution (Table A.1), the mean number density evolution (Table A.2), the differential column density distribution (CDDF) of the entire H i sample (Table A.3), the CDDF of the C ivenriched forest (Table A.4) and of the unenriched forest (Table A.5) for Δv_{metal} = ±100 km s^{1}, and the CDDF of the C ivenriched forest (Table A.6) and of the unenriched forest (Table A.7) for Δv_{metal} = ±10 km s^{1}.
Number density evolution data for each quasar.
Mean number density evolution data for Δz = 0.26.
CDDF of the entire H i forest f = log ^{(}dN/(dN_{H i}dX)^{)} for the highorder fit sample.
CDDF f = log ^{(}dN/(dN_{H i}dX)^{)} of the C ivenriched forest for Δv_{metal} = ±100 km s^{1}.
CDDF f = log ^{(}dN/(dN_{H i}dX)^{)} of the unenriched forest for Δv_{metal} = ±100 km s^{1}.
CDDF f = log ^{(}dN/(dN_{H i}dX)^{)} of the C ivenriched forest for Δv_{metal} = ±10 km s^{1}.
CDDF f = log ^{(}dN/(dN_{H i}dX)^{)} of the unenriched forest for Δv_{metal} = ±10 km s^{1}.
All Tables
Linear regression results for the differential column density distribution as a function of redshift and column density using the highorder fit.
Linear regression results for the number density evolution dn_{H i + C iv}/dz of the C ivenriched H i forest absorbers in the quasar by quasar analysis.
Linear regression results for the differential column density distribution of the C ivenriched forest at log N_{H i} = [14.5,17.0].
H i density relative to the critical density at log N_{H i} = [12.75,17.0]: the entire H i forest Ω_{H i}, the enriched forest Ω_{H i + C iv} and the unenriched forest Ω_{H i − C iv}.
CDDF of the entire H i forest f = log ^{(}dN/(dN_{H i}dX)^{)} for the highorder fit sample.
CDDF f = log ^{(}dN/(dN_{H i}dX)^{)} of the C ivenriched forest for Δv_{metal} = ±100 km s^{1}.
CDDF f = log ^{(}dN/(dN_{H i}dX)^{)} of the unenriched forest for Δv_{metal} = ±100 km s^{1}.
CDDF f = log ^{(}dN/(dN_{H i}dX)^{)} of the C ivenriched forest for Δv_{metal} = ±10 km s^{1}.
CDDF f = log ^{(}dN/(dN_{H i}dX)^{)} of the unenriched forest for Δv_{metal} = ±10 km s^{1}.
All Figures
Fig. 1 Numbers of absorption lines as a function of N_{H i} at 2.2 < z < 2.6 and 2.8 < z < 3.2. The Lyαonly fits are shown as solid lines, while the highorder Lyman fits are marked as dashed lines. Solid errors indicate the 1σ Poisson errors of the Lyαonly fits. 

Open with DEXTER  
In the text 
Fig. 2 Total absorption distance X(z) covered with our sample of 18 highredshift quasars. The solid line is for the Lyαonly fit, while the dashed one is for the highorder fit. 

Open with DEXTER  
In the text 
Fig. 3 Number of H i absorbers with log N_{H i} = [12.75,17] as function of redshift in our our sample of 18 highredshift quasars. The solid line is for the Lyαonly fit. The dashed line is for the highorder fit, while the red heavy dotdashed line is for the Lyαonly fit for the redshift range used for the highorder fit. 

Open with DEXTER  
In the text 
Fig. 4 Number density evolution of the Lyα forest in the column density range log N_{H i} = [14,17] of the Lyαonly fits. Black filled circles show results from our data set, which is tabulated in Table A.1. Other data points indicate various results obtained from the literature. The vertical error bars give the 1σ Poisson error, while the xaxis error bars show the redshift range covered by each sightline. The solid line shows the fit to our data only. Dashed line is the result including the literature data for z > 1 (log (1 + z) > 0.3). The dotted line gives the fit given in Kim et al. (2002). The green dotdashed curve shows the predicted dn/dz evolution based on a quasaronly UV background by Davé et al. (1999). The red dotted and the blue dotdotdotdashed curves at z < 2 illustrate the predicted dn/dz based on momentumdriven wind and nowind models with a UV background by quasars and galaxies, respectively (Davé et al. 2010). 

Open with DEXTER  
In the text 
Fig. 5 Number density evolution of the Lyα forest in the column density range log N_{H i} = [13.1,14.0]. All the symbols have the same meaning as in Fig. 4. 

Open with DEXTER  
In the text 
Fig. 6 Mean number density evolution of the Lyα forest. The vertical error bars give the 1σ Poisson error, while the xaxis error bars show the redshift range covered by each data point. The data point with the dotted error bar indicate the Janknecht et al. (2006) data. The dashed straight lines mark the fit to the mean data excluding the Janknecht et al. (2006) data. 

Open with DEXTER  
In the text 
Fig. 7 Differential column density distribution at 1.9 < z < 3.2 using the Lyαonly fits. Both black (log N_{H i} ≥ 12.75) and grey (log N_{H i} < 12.75) data points show the results from our quasar sample. The grey data points mark the column densities that are affected by incompleteness. The stars are the data points obtained by Petitjean et al. (1993). The filled circles at log N_{H i} > 20.1 and at 19 < log N_{H i} < 20.1 are from the SDSS II DR7 at ⟨ z ⟩ = 3.02 by Noterdaeme et al. (2009) and from O’Meara et al. (2007) at ⟨ z ⟩ = 3.1, respectively. The solid line gives the power law fit to our data for log N_{H i} = [12.75,14]. The dotted line represent the fit obtained by Hu et al. (1995), while the dashed line represents a theoretical prediction at z ~ 3 by Altay et al. (2011). The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. Gas overdensities on the top xaxis are computed using Eq. (10) from Schaye (2001) at z = 2.55 (see text for details). 

Open with DEXTER  
In the text 
Fig. 8 Line number density evolution derived on a quasar by quasar analysis using the highorder Lyman sample for column density intervals of log N_{H i} = [12.75,14.0] and log N_{H i} = [14,17]. The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the redshift range covered by each sightline. The straight lines denote results from a linear regression to the data with parameters given in Table 2. The data are tabulated in Table A.1. 

Open with DEXTER  
In the text 
Fig. 9 Mean line number density evolution of the combined sample as a function of redshift using the highorder Lymanseries sample for column density intervals of log N_{H i} = [12.75,14.0] and log N_{H i} = [14,17]. The sample is binned in redshift with Δz = 0.26, starting from z = 1.90. The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the redshift range covered by each data point. For comparison, the results of the Lyαonly fits (grey open circles) are shown for the log N_{H i} = [14,17] interval. The straight solid lines denote results from a linear regression to the binned data. Two dashed lines represent the mean number density evolution of the Lyαonly fit sample for log N_{H i} = [14,17] (log dn/dz = (−0.41 ± 0.35) + (4.14 ± 0.63) × log (1 + z)) and for log N_{H i} = [12.75,14] (log dn/dz = (1.96 ± 0.13) + (1.12 ± 0.24) × log (1 + z)), respectively. Exactly same redshift range was used for both fit samples. The data are tabulated in Table A.2. The parameters of the fits are given in Table 2. 

Open with DEXTER  
In the text 
Fig. 10 Differential column density distribution at 1.9 < z < 3.2 of our quasar sample using the highorder Lyman fit. Black and grey data points show the results from our quasar sample. The grey data points below log N_{H i} < 12.75 mark the column densities that are affected by incompleteness. The grey data points above log N_{H i} > 12.75 represent the results from the Lyαonly fit. The dashed line represents a theoretical prediction at z ~ 3 by Altay et al. (2011). The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. The filled circles at log N_{H i} > 20.1 and at 19.0 < log N_{H i} < 20.1 are from the SDSS II DR7 at ⟨ z ⟩ = 3.02 by Noterdaeme et al. (2009) and from O’Meara et al. (2007) at ⟨ z ⟩ = 3.1, respectively. The solid line gives the power law fit to our data for log N_{H i} = [12.75,14.0]. The overdensity plotted on the top xaxis is calculated at z = 2.55. 

Open with DEXTER  
In the text 
Fig. 11 Upper panels: differential column density distribution as a function of redshift. Both black and grey data points show the results from the highorder Lyman sample. The grey data points at log N_{H i} < 12.75 mark the column densities that are affected by incompleteness. The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. The black solid line gives the power law fit for log N_{H i} = [12.75,14.0] at each redshift bin, whereas the dashed line is the fit to the z = [1.9,3.2] redshift range (see Fig. 10). The overdensity plotted on the top xaxis is calculated at the mean z for each redshift bin. Lower panels: residuals from the power law fit from the entire redshift range at log N_{H i} = [12.75,14.0]. 

Open with DEXTER  
In the text 
Fig. 12 Example of a velocity plot (a relative velocity vs normalised absorption profile plot) of H i and associated C iv detected in the z = 2.352743 absorber in the spectrum of HE1122−1648. The strongest C iv component is set to be at the zero velocity. The observed spectra are plotted as a histogram, while Voigtprofile fits are as a smooth curve. Thick red curves are the combined fit from individual components. The heavy tick marks above the profiles indicate the velocity centroid of each component. Nonnegligible blends by other ions are indicated in gray. The b value (in km s^{1}) and log N_{H i} with the VPFIT fitting errors are displayed next a tick mark indicating the center of the component. 

Open with DEXTER  
In the text 
Fig. 13 C iv column density distribution. Filled circles are our results at the redshift range used for the highorder fit C ivenriched H i sample at 1.9 < z < 3.2. The CCD gap in the C iv region was accounted for. Red filled triangles and blue filled diamonds are from Pichon et al. (2003) at 1.5 < z < 2.3 and 2.3 < z < 3.1, respectively. Green open squares are taken from Songaila (2001) at 2.90 < z < 3.54. The vertical error bars indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. The black dotted line shows the linear regression to filled circles at log N_{C iv} = [12.25,15.5]: log dn_{C iv}/(dN_{C iv}dX) = (11.41 ± 1.61) + (−1.85 ± 0.13) × log N_{C iv}. The solid line is the power law fit at log N_{C iv} = [12.25,13.5]: log dn_{C iv}/(dN_{C iv}dX) = (6.60 ± 1.23) + (−1.47 ± 0.10) × log N_{C iv}. The turnover at log N_{C iv} ~ 12.5 shown in green data is simply due to the incompleteness for weak C iv. Similarly, the turnover seen at log N_{C iv} ~ 12.1 in our data is also due to the incompleteness. 

Open with DEXTER  
In the text 
Fig. 14 Line width vs. column density for the C iv absorption lines along 16 sightlines excluding Q0055−269 and J2233−606 at the two redshift bins. Error bars are fitting errors from the VPFIT profile fitting. In the upper panel, two heavy dashed lines delineate a 3σ detection limit for a spectrum with S/N = 120 and S/N = 90 per pixel. At 2.4 < z < 3.2, most spectra show S/N greater than 90. In the lower panel, the heavy dashed line shows a 3σ detection limit for S/N = 120. Broader and weaker absorption lines at the left of the detection limit are missed in lower S/N spectra. The vertical dotted line indicates the adopted low N_{C iv} bound of log N_{C iv} = 12.2 above which the incompleteness does not affect the C iv detection significantly. The histogram shown with the base at log N_{C iv} = 12.2 is the number of C iv lines as a function of b_{C iv} with the b_{C iv} binsize of 2 km s^{1}. Thick ticks above the number distribution mark the median b_{C iv} for log N_{C iv} ≥ 12.2. The total number of C iv lines is 194 and 171 at 2.4 < z < 3.2 and 1.9 < z < 2.4, respectively. Among them, 138 and 122 lines have log N_{C iv} ≥ 12.2 at the same redshift range. 

Open with DEXTER  
In the text 
Fig. 15 N_{H i} − N_{C iv} diagram for log N_{H i} = [12.0,17.8] from the highorder fit sample at 1.9 < z < 3.2. The upper panel is for the Δv_{C iv} = ±100 km s^{1} sample, while the lower panel for the Δv_{C iv} = ±10 km s^{1} sample. Open circles represent H i absorbers associated with all the possible C iv components, since a single H i line could be assigned to several C iv lines. On the other hand, red filled circles indicate a H i absorber associated with only one closest C iv. The total number of open (filled) circles is 1082 (451) and 184 (163) for the Δv_{C iv} = ±100 km s^{1} and Δv_{C iv} = ±10 km s^{1} sample, respectively. 

Open with DEXTER  
In the text 
Fig. 16 Quasar by quasar line number density evolution of C ivenriched H i absorbers. The left panels are derived from the Δv_{C iv} = ±100 km s^{1} sample, while the right panels represent the Δv_{C iv} = ±10 km s^{1} one. The open circles in the upper panel represent C ivenriched absorbers having log N_{H i} = [12.75,14.0] and the filled circles in the lower panel represent log N_{H i} = [14,17]. The vertical error bars mark 1σ Poisson errors, while the xaxis error bars show the redshift range covered by each sightline. Sightlines having no C ivenriched H i absorbers for a given velocity range are plotted at log ddn_{C iv}/ddz = 0. The solid lines represent linear regressions to the data, using the parameters summarised in Table 4. Sightlines with no C ivenriched absorbers, log dn_{H i + C iv}/dz is plotted to be 0 with a downward arrow. 

Open with DEXTER  
In the text 
Fig. 17 Fraction of the C ivenriched H i absorber number density to the total absorber number density as a function of redshift. The left panel is derived from the Δv_{C iv} = ±100 km s^{1} sample, while the right panel represents the Δv_{C iv} = ±10 km s^{1} sample. The black open circles represent a column density interval of log N_{H i} = [12.75,14.0] and the filled circles represent log N_{H i} = [14,17]. The vertical error bars mark 1σ Poisson errors, while the xaxis error bars show the redshift range covered by each sightline. In the left panel for log N_{H i} = [12.75,14.0] (gray open circles), two lowest data points at log (1 + z) ~ 0.5 (or z ~ 2) including an upper limit are from Q1101−264 and Q0122−380. Both have a short redshift coverage, therefore become more susceptible to cosmic variance. Sightlines with no C ivenriched H i absorbers are plotted as upper limits with an arbitrary value of 0.05 and 0.002 for log N_{H i} = [14,17] and log N_{H i} = [12.75,14.0], respectively. 

Open with DEXTER  
In the text 
Fig. 18 A velocity plot of a highly enriched C iv absorber at z = 2.030069 toward HE1122−1648. The zero velocity is centered at z = 2.030069. Although there is no obvious Lyα absorption seen at the zero velocity, both C iv and N v doublets are present to secure the existence of this absorber. Note that the yaxis range for each ion is different: from the normalised flux 0 to 1 for H i and from 0.5 to 1 for the rest of the ions. 

Open with DEXTER  
In the text 
Fig. 19 Distribution function for C ivenriched H i lines for Δv_{C iv} = ±100 km s^{1} (red filled squares) and for Δv_{C iv} = ±10 km s^{1} (blue stars) at 1.9 < z < 3.2. Also shown is the differential column density distribution function for all H i Lyα absorbers excluding Q0055−269 and J2233−606 (black filled circles) in the same redshift range analysed for the C ivenriched forest. The solid line indicates the fit to filled circles for log N_{H i} = [12.75,14.0]: log dN/(dN_{H i}dX) = (7.43 ± 0.44) + (−1.44 ± 0.03) × log N_{H i}. The vertical errors indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. All the grey data points indicate that the data are incomplete at log N_{H i} < 12.75. 

Open with DEXTER  
In the text 
Fig. 20 Differential column density distribution function for enriched and unenriched absorbers in our highorder fit sample using Δv_{C iv} = ±100 km s^{1} (upper panels) and Δv_{C iv} = ±10 km s^{1} (lower panels) at the two different redshift ranges. Black filled circles and gray stars mark unenriched absorbers. Red filled squares and blue stars indicate the C ivenriched forest for the Δv_{C iv} = ±100 km s^{1} sample and the Δv_{C iv} = ±10 km s^{1} sample, respectively. Both Q0055−269 and J2233−606 are excluded in the analysis. The solid black line indicates the fit to the entire H i sample and the whole redshift range at log N_{H i} = [12.75,14.0] as in Fig. 19. Red and blue dashed lines represent the fit to each C ivenriched forest sample at log N_{H i} = [14.5,17]. The vertical errors indicate 1σ Poisson errors, while the xaxis error bars show the N_{H i} range covered by each data point. The lower parts of the panels show the difference between the observed CDDF and the expected CDDF from the powerlaw fit obtained for the entire H i samples (black solid lines). 

Open with DEXTER  
In the text 
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