11institutetext:  Cosmic Dawn Center (DAWN), University of Copenhagen, Denmark
22institutetext: Niels Bohr Institute, University of Copenhagen, Jagtvej 128,
DK-2200 N, Copenhagen, Denmark 33institutetext: NRC Herzberg, 5071 West
Saanich Rd, Victoria, BC V9E 2E7, Canada 44institutetext: Centro de
Astrobiología (CAB), CSIC-INTA, Ctra. de Ajalvir km 4, Torrejón de Ardoz,
E-28850, Madrid, Spain 55institutetext: Department of Physics, University of
Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK
66institutetext: Sorbonne Université, CNRS, UMR 7095, Institut d’Astrophysique
de Paris, 98 bis bd Arago, 75014 Paris, France 77institutetext: Kavli
Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge,
CB3 0HA, UK 88institutetext: Cavendish Laboratory, University of Cambridge, 19
JJ Thomson Avenue, Cambridge, CB3 0HE, UK 99institutetext: Department of
Physics and Astronomy, University College London, Gower Street, London WC1E
6BT, UK

#  Metal enrichment and evolution in four $z>6.5$ quasar sightlines observed
with JWST/NIRSpec

L. Christensen e-mail<EMAIL_ADDRESS>P. Jakobsen 1122 C. Willott 33
S. Arribas 44 A. Bunker 55 S. Charlot 66 R. Maiolino 778899 M. Perna 44 H.
Übler 7788

(Received; accepted)

We present JWST/NIRSpec $R\\!\simeq\\!2700$ spectra of four high-redshift
quasars: VDES J0020–3653 ($z=6.860$), DELS J0411–0907 ($z=6.825$), UHS
J0439+1634 ($z=6.519$) and ULAS J1342+0928 ($z=7.535$). The exquisite data
quality, signal-to-noise ratio of 50–200, and large
$0.86\\!\leavevmode\nobreak\ \mu{\rm m}\leq\lambda\leq
5.5\\!\leavevmode\nobreak\ \mu{\rm m}$ spectral coverage allows us to identify
between 13 and 17 intervening and proximate metal absorption line systems in
each quasar spectrum, with a total number of 61 absorption-line systems
detected at $2.42<z<7.48$ including the highest redshift intervening O i
$\lambda$1302 and Mg ii systems at $z=7.37$ and $z=7.44$. We investigate the
evolution of the metal enrichment in the epoch of reionization (EoR) at $z>6$
and find: i) A continued increase of the low-ionization O i, C ii, and Si ii
incidence, ii) Decreasing high-ionization C iv and Si iv incidence with a
transition from predominantly high- to low-ionization at $z\approx 6.0$, and
iii) a constant Mg ii incidence across all redshifts. The observations support
a change in the ionization state of the intergalactic medium in the EoR rather
than a change in metallicity. The abundance ratio of [Si/O] in five $z>6$
absorption systems show enrichment signatures produced by low-mass Pop III
pair instability supernovae, and possibly pop III hypernovae. In the Gunn-
Peterson troughs we detect transmission spikes where Ly$\alpha$ photons can
escape. From 22 intervening absorption line systems at $z>5.7$, only a single
low-ionization system out of 13 lies within 2000 km s-1 from a spike, while
four high-ionization systems out of nine lie within $\sim$2000 km s-1 from a
spike. Most spikes do not have associated metal absorbers close by. This
confirms that star-forming galaxies responsible for producing the heavy
elements that are transported to the circumgalactic medium via galaxy winds do
so in predominantly high-density, neutral environments, while lower density
environments are ionized without being polluted by metals at $z\approx 6-7$.

###### Key Words.:

Cosmology: observations – (Cosmology:) dark ages, reionization, first stars –
(galaxies:) quasars: absorption lines – Galaxies: high-redshift – (galaxies:)
intergalactic medium

##  1 Introduction

Investigations of high redshift quasars contribute significantly to our
knowledge of the physical conditions in the early Universe, including the
growth of supermassive black holes (SMBH) and their co-evolution with
galaxies, and their effect on the reionization history of the Universe.
Quasars also serve as bright beacons to illuminate the baryonic material along
their lines of sight since their high luminosity allow their detection to very
large distances. Finding high-redshift quasars has been the focus of numerous
scientific investigations. From color selection of sources in large imaging
surveys (e.g. Fan et al., 2001; Willott et al., 2010; Wang et al., 2019;
Matsuoka et al., 2022; Bañados et al., 2023), hundreds of quasars beyond
redshift 6 have been discovered and compiled in databases (Inayoshi et al.,
2020; Fan et al., 2023), while beyond redshift 7 only eight quasars have been
discovered to date (Mortlock et al., 2011; Bañados et al., 2018; Matsuoka et
al., 2019b, a; Yang et al., 2019, 2021; Wang et al., 2018, 2021).

Spectra of quasars allow detailed studies of intervening baryonic material
from absorption lines arising either in the environment near quasars as well
as along random lines of sight through the Universe, and are invaluable probes
to analyse the intergalactic medium (IGM) and the epoch of reionization (EoR).
Due to the rapid decrease in quasar number densities compared to galaxies at
the highest redshifts, quasars contribute only $<7$% of the reionizing photon
budget at $z\sim 6-6.6$ (Jiang et al., 2022). The neutral IGM will appear as a
damping wing at the red part of the Ly$\alpha$ absorption in quasar spectra
(Miralda-Escudé, 1998), and the shape of this damping wing in quasar spectra
at different redshift can be used to compute the neutral gas fraction
$X_{{H\textsc{i}}}$ as the reionization progresses (Bañados et al., 2018;
Davies et al., 2018, 2020). A complete absorption by the IGM in the Gunn-
Peterson absorption trough (GP hereafter)(Gunn & Peterson, 1965) detected in
$z>6$ quasar spectra (Fan et al., 2001; Mortlock et al., 2011) reveals an
increase in the Ly$\alpha$ optical depth with increasing redshift (Becker et
al., 2021).

In the otherwise dark regions of the GP troughs, transmission spikes in the
IGM start appearing at $z\leavevmode\nobreak\ \lesssim\leavevmode\nobreak\ 6$
where ionized regions allow the transmission of Ly$\alpha$ photons at those
redshifts. Such spikes therefore need to lie near sources that produce
ionizing radiation. Kakiichi et al. (2018) and Meyer et al. (2020)
investigated the correlation between Ly$\alpha$ forest transmission spikes and
location of Ly$\alpha$-emitters and galaxies within a distance of 60 Mpc from
the line of sight towards $z>6$ quasars, and argue that reionization is
primarily driven by a population of galaxies that are fainter than the
detected galaxies.

Besides probing the EoR through studies of hydrogen absorption, quasar spectra
can be used to measure the evolution of heavy element enrichment with cosmic
time from intervening absorption line systems to $z\sim 6$ (see Becker et al.,
2015, for a review of metal absorption lines). Quasars probe random lines of
sight through the Universe, and intersect galaxies either in their
circumgalactic medium (CGM) or interstellar medium (ISM) out to the quasar
redshift, all of which imprint a spectral signature in the background quasar
spectrum.

While the metallicity on the average increases with time, metal absorption
lines also reflect the ionization state and are therefore sensitive to the
ultraviolet background radiation. Early analyses of the Ly$\alpha$ forest
clouds and their correlations with C iv$\lambda\lambda$1548,1550 absorption
lines revealed that the IGM at $z=3$ is enriched to a metallicity level of
$\approx 10^{-3}\leavevmode\nobreak\ Z_{\odot}$ (Cowie et al., 1995; Ellison
et al., 2000). While metals seem to be prevalent, absorption line
densities111Line densities are also commonly called ‘incidence rate’ or simply
‘number densities’ in the literature. These terms are identical in their
definition., defined as the number of absorption line systems detected per
unit (comoving) redshift path length interval, can be used to map the
evolution of the metal enrichment and ionization level. For example, C iv
absorber line densities remain roughly constant with redshift out to $z\sim 4$
and then show a steep drop at $z>5$ (Becker et al., 2011; Codoreanu et al.,
2018; D’Odorico et al., 2022; Davies et al., 2023b). A similar trend is found
for the other high-ionization line Si iv (Cooper et al., 2019; D’Odorico et
al., 2022), suggesting a rapid physical change in the ionization state at
these redshifts. The change of ionization state rather than a change in metal
enrichment is supported from the observation of a change in the C iv/C ii
$\lambda$1334 ratio (Simcoe et al., 2020; Davies et al., 2023b). Similarly,
the line density of weak Mg ii $\lambda$2796 absorbers remain constant over a
large redshift range of $2<z<7$ (Chen et al., 2017), whereas the line density
of O i $\lambda$1302 increases over the range $3.2<z<6.5$ (Becker et al.,
2011, 2019) and is also interpreted as a change in the ionization state at the
EoR, where the Universe becomes progressively more neutral.

Bright quasars also allow detailed analyses of individual absorption systems
by measuring column densities of various atomic species, similar to studies of
$2<z<3$ strong hydrogen absorption line systems (e.g. Prochaska et al., 2003).
Focusing on the highest redshift absorbers at $z>6$, this epoch approaches the
time when the first population of massive stars produce the initial chemical
enrichment. However, for intervening absorption lines systems the absence of
measurable H i column densities in the quasars heavily absorbed Ly$\alpha$
forest or GP troughs makes it impossible to derive absolute metallicities, and
only a few studies of relative metallicities at $z>6$ have been performed to
date. One example is the $z\sim 7.5$ quasar ULAS J1342+0928 whose absorption
system at $z=6.84$ with measured iron, carbon and silicon suggests no alpha-
element enhancement nor any special signatures of enrichment from very massive
stars (Simcoe et al., 2020). When the absorption system lies close to the
quasar redshift, in the so-called associated or proximate absorption systems,
detailed metallicity measurements can be performed since the intrinsic H i
column density can be modelled from fits to the H i damping wing (Bañados et
al., 2019; Andika et al., 2022). However, proximate absorption systems do not
trace a random population, but rather probe the high-density environments near
the quasars (Ellison et al., 2010).

The connection between galaxies in emission and gas in absorption inform us of
the transverse metal enrichment in the CGM of galaxies, albeit only along a
single line of sight to the background quasar. Hence, combining the
observations with simulations will reveal the spatial distribution of metals
in the CGM of galaxies and its evolution with redshift (e.g Finlator et al.,
2018; Peeples et al., 2019). At $z\geq 5$ overdensities of line emitting
galaxies either selected via Ly$\alpha$ emission (Díaz et al., 2021) or [O
iii] $\lambda$5007 (Kashino et al., 2023) have been found within 200 kpc of
the metal absorbers and approximately at the same redshifts.

Large amounts of ground-based telescope time have in recent years been devoted
to spectroscopic studies of $z>6$ quasars (e.g. D’Odorico et al., 2023).
However, with the successful launch and commissioning of the James Webb Space
Telescope (Gardner et al., 2023; Rigby et al., 2023), the question arises of
what new insights observations of high redshift quasars obtained with JWST’s
near infrared spectrograph, NIRSpec (Jakobsen et al., 2022), may bring to bear
on the subject matter. While the highest spectral resolution possible with
NIRSpec of $R\simeq 2700$ is only just adequate for intervening absorption
line studies, the benefits offered by the instrument’s very high sensitivity
and wide ($0.81\leavevmode\nobreak\ \mu{\rm m}\\!\leq\\!\lambda\leq
5.3\leavevmode\nobreak\ \mu{\rm m}$) infrared spectral coverage free of the
atmospheric telluric absorption and OH sky emission lines that hamper ground
based near-IR observations are self-evident.

In this paper, we present high signal-to-noise ratio $R\simeq 2700$
JWST/NIRSpec spectra of four well-studied quasars at $6.5<z<7.5$. We focus our
analysis exclusively on the intervening absorption line systems detected. The
paper is organised as follows. Section 2 describes the observations. The
spectra are presented and analysed in Section 3. The measurements of metal
absorption line systems and the redshift evolution of line densities of atomic
transitions in oxygen, carbon, silicon and magnesium, calcium and sodium are
discussed in Section 4. In Section 5 we compute metal abundance patterns and
compare with the enrichment of Population III and Pop II massive star
Supernova explosions, and in Section 6 we compare the metal absorption system
redshifts with transmission spikes seen in the quasar GP troughs. In Section 7
we present a discussion and conclude in Section 8. Throughout the paper we
assume a flat cosmological model with $H_{0}$=67.4 km s-1 Mpc-1 and
$\Omega_{m}$=0.315 (Planck Collaboration et al., 2020).

##  2 NIRSpec observations

In this paper we present high signal-to-noise ratio ($S/N\\!\simeq\\!50-200$),
$R\\!\simeq\\!2700$ NIRSpec spectra spanning $0.86\\!\leavevmode\nobreak\
\mu{\rm m}\leq\lambda\leq 5.3\\!\leavevmode\nobreak\ \mu{\rm m}$ of the four
high redshift quasars: ULAS J1342+0928 (Bañados et al., 2018, $z=7.54$,
$J_{AB}=20.3$), VDES J0020–3653 (Reed et al., 2019, $z=6.83$, $J_{AB}=20.4$),
DELS J0411–0907 (a.k.a VHS J0411–0907, Pons et al., 2019; Wang et al., 2019,
$z=6.81$, $J_{AB}=20.4$) and UHS J0439+1634 (Fan et al., 2019, $z=6.51$,
$J_{AB}=17.4$). These data were obtained as part of the NIRSpec GTO programs
1219 (PI: N. Lützgendorf) and 1222 (PI: C. Willott).

The NIRSpec Band I ($0.86\leavevmode\nobreak\ \mu{\rm m}\\!\leq\\!\lambda\leq
1.88\leavevmode\nobreak\ \mu{\rm m}$) and Band II ($1.70\leavevmode\nobreak\
\mu{\rm m}\leq\lambda\leq 3.15\leavevmode\nobreak\ \mu{\rm m}$) spectra were
taken with the NIRSpec Fixed Slits together with the G140H/F070LP and
G235H/F170LP grating/filter combinations, whereas the long wavelength Band III
($2.88\leavevmode\nobreak\ \mu{\rm m}\leq\lambda\leq 5.3\leavevmode\nobreak\
\mu{\rm m}$) portions were obtained employing the NIRSpec Integral Field Unit
(IFU) and the G395H/F290LP grating/filter combination (see Jakobsen et al.
(2022) and Böker et al. (2022) for details of these NIRSpec observing modes).
The dates on which the observations were carried out and the position angles
of the slits employed are summarized in Table 1.

Table 1: Observation Epochs snd Slit Orientations Quasar | Mode | Date | PA [deg]
---|---|---|---
ULAS J1342+0928 | FS | 31 Jan 2023 | 66.8
| IFU | 23 Jan 2023 | 55.1
VDES J0020–3653 | FS | 19 Nov 2022 | 187.2
| IFU | 16 Oct 2022 | 160.2
| IFU | 17 Nov 2022 | 189.2
DELS J0411–0907 | FS | 18 Sep 2022 | 50.6
| IFU | 1 Oct 2022 | 61.9
UHS J0439+1634 | FS | 24 Jan 2023 | 210.8
| IFU | 18 Jan 2023 | 210.4

The Band I and Band II Fixed Slit observations all employed 65 group
NRSIRS2RAPID (Rauscher et al., 2017) full frame detector sub-integrations. For
ULAS J1342+0928, VDES J0020–3653, and DELS J0411–0907, spectra were
obtained in both the S200A1 and S200A2 fixed slits in order to span the
detector gap, with two integrations carried out in each of three nodded
positions along the slit for G140H/F070LP (11554 s total exposure time) and
one integration at each nod in G235H/F170LP (5778 s total exposure time). The
brighter UHS J0439+1634 was only observed in the S200A1 slit at three nodded
integrations in both G140H/F070LP and G235H/F170LP (2889 s total exposure
time). These data were reduced and combined using the NIRSpec GTO Team
pipeline (Carniani et al. in preparation). The resulting flux-calibrated
spectra refer to Barycentric vacuum wavelengths sampled at
$\Delta\lambda=2.36$ Å per wavelength bin in Band I and $\Delta\lambda=3.96$
Å per wavelength bin in Band II, which by design is close to the average
native pixel sampling for these dispersers.

One noteworthy aspect of the shortest wavelength Band I G140H observations is
that they were taken with the F070LP order-separation filter, rather than the
default F100LP filter. This was done to include wavelengths in the Gunn-
Peterson troughs below emitted Ly$\alpha$ down to
$0.813\\!\leavevmode\nobreak\ \mu$m with the S200A1 slit and down to
$0.858\\!\leavevmode\nobreak\ \mu$m with the S200A2 slit. This is permissible
in this case since there is little or no light from the quasar emerging in the
trough and therefore no second order light (beyond that of the sky background,
which is subtracted out in the reduction) to contaminate the primary first
order spectrum at wavelengths $\lambda>1.26\\!\leavevmode\nobreak\ \mu$m.
However this choice did require tricking the pipeline into reducing the full
Band I spectrum in two steps. First by running it for the F070LP filter giving
the correctly reduced spectrum over $0.85\\!\leavevmode\nobreak\ \mu{\rm
m}\leq\lambda\leq 1.26\\!\leavevmode\nobreak\ \mu{\rm m}$ and then running it
a second time pretending that the F100LP filter was used and then scaling the
resulting red end of the spectrum covering the $1.0\\!\leavevmode\nobreak\
\mu{\rm m}\leq\lambda\leq 1.88\\!\leavevmode\nobreak\ \mu{\rm m}$ region by
the known ratio of the transmission of the F070LP and F100LP filters. The
latter correction amounts to less than 4% in flux.

The Band III G395H/F290LP IFU observations of ULAS J1342+0928, VDES
J0020–3653, and DELS J0411–0907 employed 25 group NRSIRS2 sub-integrations at
6 dithered IFU positions (11029 s total integration time) and UHS J0439+1634
18 group NRSIRS2 integrations at 8 dithered positions (10620 s total
integration time). The IFU observations of VDES J0020–3653, and DELS
J0411–0907 are presented in Marshall et al. (2023), who along with Perna et
al. (2023) also provide the details of how the IFU data were reduced. Here we
merely employ the one dimensional G395H/F290LP spectra extracted from the four
IFU data cubes over a 1$\aas@@fstack{\prime\prime}$0 diameter aperture
centered on the quasars to supplement the corresponding Band I and II Fixed
Slit spectra of primary interest. The resulting Band III spectra are sampled
at $\Delta\lambda=6.65$ Å per wavelength bin.

Figure 1: NIRSpec fixed slit ($\lambda<31160$ Å) and extracted 1D spectrum
from the Band III data ($\lambda>31160$ Å) of VDES J0020–3653. The modulation
of the flux, which is particularly pronounced around $\sim$18000 and
$\sim$30000 Å here and also the other quasar spectra, is caused by the coarse
sampling of the spectrum described in Sect. 2. The associated error spectrum
is shown in gray. With the very high $S/N$ ratio of the spectrum, the error
spectrum appears virtually at the zero-flux level.

Figure 2: NIRSpec fixed slit and extracted 1D spectrum from Band III data
($\lambda>31160$ Å) of DELS J0411–0907.

Figure 3: NIRSpec fixed slit and 1D spectrum from Band III data
($\lambda>31160$ Å) of UHS J0439+1634. The spectrum shows clear features from
intrinsic broad absorption lines (BAL), particularly from C iv, but also at
wavelengths between Ly$\alpha$ and Si iv, the underlying quasar continuum
suggests an absorbed fraction when extending power-law function continuum fit
obtained from wavelengths redwards of the C iii] line.

Figure 4: NIRSpec fixed slit and extracted 1D spectrum from Band III data
($\lambda>31700$ Å) of ULAS J1342+0928.

The final combined one-dimensional spectra of the quasars along with their
$1\sigma$ error spectra are shown in Figures 2, 2, 4, and 4. The signal-to-
noise ratios achieved in these four spectra are very high. This is illustrated
for the representative case of VDES J0020–3653 in Figure 5. The lower panel
plots the Band I and II signal-to-noise ratio per wavelength bin calculated as
the ratio of the observed signal divided by the one sigma error spectrum
output by the GTO pipeline after median smoothing over a 40 bin window to
remove the absorption lines. It is apparent that $S/N\simeq 50-100$ per
wavelength bin is achieved at nearly all wavelengths. The overall variation
with wavelength seen in this plot partly reflects the blaze functions of the
two gratings employed and the variation in the continuum flux due to the broad
emission lines in the quasar spectrum, but also the fact that not all
wavelengths are exposed equally due to the gap between the two NIRSpec
detector arrays. When employing the G140H grating with the S200A1 fixed slit
the wavelength span $1.3021-1.3391\\!\leavevmode\nobreak\ \mu$m falls on the
detector gap. For the S200A2 fixed slits the wavelength range
$1.3479-1.3849\\!\leavevmode\nobreak\ \mu$m is lost to the gap. Similarly, for
the G235H grating respectively $2.1825-2.2445\\!\leavevmode\nobreak\ \mu$m and
$2.2594-2.3215\\!\leavevmode\nobreak\ \mu$m project to the gap for the S200A1
and S200A2 slits. Moreover, with the S200A1 slit, the G235M spectrum falls off
the red edge of the detector for $\lambda>3.0491\\!\leavevmode\nobreak\ \mu$m
but for $\lambda>3.1262\\!\leavevmode\nobreak\ \mu$m for the S200A2 slit.
These unavoidable wavelength regions of reduced $S/N$ only covered in one of
the two slit spectra are indicated in Figure 5.

The high $S/N$ ratio observations of the bright ($J_{AB}\simeq 20$) quasars
presented here represent a radically different and rarer regime of
applicability for NIRSpec compared to the perhaps more familiar lower spectral
resolution observations of the much fainter ($J_{AB}\simeq 26-29$) highest
redshift galaxies that the instrument was primarily designed for (cf. Curtis-
Lake et al., 2023; Bunker et al., 2023). It is therefore worth pointing out a
few further subtle quirks of NIRSpec revealed by these data that are usually
masked at lower $S/N$ ratio.

While NIRSpec grating observations of faint high redshift galaxies are
invariably severely detector noise limited (cf. Appendix in Jakobsen et al.,
2022), the photon noise in our quasar spectra typically exceeds the total
detector read noise and dark current noise by a factor $\simeq 4$.
Furthermore, the quasar photon signals typically exceeds that of the (zodiacal
light dominated) sky background experienced by JWST by a factor $\simeq\\!20$.
However, one important feature that these quasar spectra still share with
their fainter counterparts is that they are rather coarsely sampled both in
wavelength and spatially along the slit. As explained in Jakobsen et al.
(2022), with the aim of optimizing the faint-end sensitivity limit of NIRSpec,
the 194 mas angular width of the NIRSpec S200A1 and S200A2 Fixed Slits
projects on to just under two mean 104 mas pixels on the detector. As a
result, the apparent line spread function in a given spectrum at a given
wavelength will depend on how precisely the slit width is sampled by the
between two and three pixels that its monochromatic image happens to project
to. This variable sampling issue is further exacerbated by the slit tilt and
optical distortion that NIRSpec spectra are subject to, which necessitate that
significant resampling of the raw 2D spectra be performed in the NIRSpec
reduction pipeline in order to rectify the spectra to a common wavelength and
spatial scale that will allow accurate background subtraction and summation of
the net quasar signal along the spatial direction. That the final spectra are
assembled by co-adding several differently sampled dithered sub-exposures
taken at different positions along the slit leads to further smearing of the
apparent line spread function. Lastly, the fact that the targets observed here
are point sources, whose monochromatic images at the shorter wavelengths are
not expected to fill the full width of the slit, adds even further variation
in the spectral sampling. The net impact of these effects can be readily seen
in Figure 6 which shows close-ups of four representative narrow absorption
lines detected in our final spectra, overlaid by their best fit Gaussian
profiles. It is evident that the coarse sampling of NIRSpec spectra causes the
apparent widths of the absorption lines to vary randomly at the $\simeq\\!1$
wavelength bin level, even among lines having comparable strengths. This
stochastic variation in the apparent shape of the pixelated line spread
function due to sampling noise clearly precludes performing meaningful
detailed profile fits to our unresolved absorption lines. However, it ought
not bias measurement of the main parameters of interest – line equivalent
widths and centroid wavelengths – provided the error propagation calculation
for these measured parameters correctly mirrors the noise level in the
resampled spectra.

Figure 5:  Lower panel: Signal-to-noise ratio per wavelength bin as a function
of wavelength for the combined Band I and Band II spectra of VDES J0020–3653.
The regions of reduced total exposure caused by the detector gap are shown as
shaded. Upper panel: Corresponding anticipated three sigma narrow absorption
line equivalent width detection limit as a function of wavelength.

Figure 6:  Representative examples of normalized line profiles illustrating
the coarse wavelength sampling of the NIRSpec spectra. The shaded error bars
shown are $\pm 1\sigma$. The best-fit Gaussian fits and their FWHM expressed
in spectral wavelength bins are also shown.

The top panel of Figure 5 shows the anticipated $3\sigma$ observed equivalent
width sensitivity limit to detecting unresolved absorption lines calculated
from the lower $S/N$ curve per the expression

$W_{\lambda}>3\frac{\Delta\lambda}{(S/N)}n_{\mathrm{FWHM}}^{3/2}$ (1)

assuming the FWHM of the NIRSpec line spread function to be a multiple of the
wavelength bin size $\delta\lambda=n_{\mathrm{FWHM}}\Delta\lambda$ with
$n_{\mathrm{FWHM}}$ (the number of spectral pixels sampling the line spread
function), conservatively set to $n_{\mathrm{FWHM}}=2.3$ for both bands. It
follows that we anticipate being able to measure absorption lines having
observed equivalent widths $W_{\lambda}>0.2\\!\leavevmode\nobreak\ $ Å in our
spectrum of VDES J0020–3653. The equivalent $S/N$ and line sensitivities
achieved for DELS J0411–0907 and ULAS J1342+0928 are comparable to those of
VDES J0020–3653 as shown in Figure 5. However, our spectrum of the
considerably brighter UHS J0439+1634 reaches $S/N\simeq 90-200$, albeit with
actual gaps in the spectrum at the missing S200A1 wavelengths.

We have not attempted to quantify the $S/N$ achieved in the IFU-based Band III
G395H extensions to our spectra. One reason for this is that the STScI
pipeline used to reduce the IFU data outputs an estimate of the error spectrum
that significantly underestimates the actual noise level in the data. This
does not affect our results, since we only detect a single Na i absorption
feature in the Band III G395H data (Section 4.4.7).

A final consequence of the coarse sampling and optical distortion that NIRSpec
spectra are subject to is worth mentioning. The NIRSpec GTO pipeline employs a
strict photon conserving (pixel projection weighting) resampling scheme when
rectifying the native 2D spectra to a common wavelength and spatial scale.
Nonetheless, in the case of a point source, the coarse sampling of the native
2D spectrum gives rise to Moire-style numerical fringing in the resampled
spectrum along any resampled spatial row (cf. Smith et al., 2007). The
amplitude of this unavoidable numerical fringing (or ‘wiggle’) at any given
wavelength is effectively averaged out provided the net signal is integrated
over a sufficient number of spatial rows large enough to capture most of the
light from the target. In the spectra presented here the GTO pipeline
performed this summation over a fixed five spatial rows. This is adequate to
integrate down the fringing at wavelengths $\lambda<2.8\\!\leavevmode\nobreak\
\mu$m, but gradually fails at longer wavelengths and can be seen in our Band
II G235H spectra due to the FWHM of the PSF increasing linearly with
wavelength in the diffraction-limited NIRSpec, causing a varying amount of
light to be missed in the five row summation. This numerical fringing effect
is particularly pronounced in NIRSpec IFU data (Marshall et al., 2023; Perna
et al., 2023), but is less obvious in our 1D Band III spectra since these were
extracted by summing over a large number of spaxels. In any event, since the
effect causes a modulation of the entire spectrum it should not affect
measured equivalent widths and line centroids as long as the local continuum
is set to follow the modulation (Section 3.2).

##  3 Analysis of the spectra

The full one-dimensional spectra of the quasars are presented in Figures 2, 2,
4, and 4, with emission lines commonly seen in quasar spectra marked. We find
a small shift in the absolute flux levels between the Fixed Slit and the IFU
spectra, and have scaled the extracted one-dimensional Band III spectra by a
factor of $\approx 0.7$ to match the flux level at the overlapping wavelengths
in the Fixed Slit Band II spectra.

###  3.1 Quasar systemic redshifts

In this paper, we focus on the analysis of intervening absorption line
systems, and therefore re-visit the estimated quasar systemic redshifts in
order to distinguish intervening from proximate metal absorbers arising less
than 3000 km s-1 from the quasar redshifts. This velocity cut-off is chosen
since absorbers nearer than this limit show different physical properties such
as metallicities and ionisation levels compared to genuine intervening
absorption systems (Ellison et al., 2010). UHS J0439+1634 is a broad
absorption line (BAL) quasar, and therefore could potentially affect its
environment to even larger velocities of $\sim$10,000 km s-1. In this spectrum
we find two absorption systems that are offset by 9000 km s-1, and both
systems show narrow lines. We choose to include both absorption systems in the
statistical analysis having a uniform cut at 3000 km s-1 for the four quasar
spectra.

Determining systemic redshifts for quasars is complex because of the dynamical
effects that present themselves in quasar spectra. Broad emission lines can be
blueshifted by several thousand km s-1 relative to the systemic redshift, and
can be composed of multiple components with peaks at distinct wavelengths,
reflecting distinct kinematic components in the broad line regions. With the
extended coverage of the rest-frame optical Balmer lines and narrow lines from
[O ii] $\lambda\lambda$3727,3729 or [O iii] $\lambda\lambda$4959,5007 afforded
by the NIRSpec spectra, more accurate systemic redshifts can be derived.

Fits to H$\alpha$ lines from the Band III spectra of VDES J0020–3653 and DELS
J0411–0907 and redshift measurements are presented in Marshall et al. (2023).
The asymmetric [O iii] line profile of VDES J0020–3653 is caused by excess
emission from extended regions spatially offset by $\sim$1 arcsec from the
quasar (Marshall et al., 2023). Our fit to the H$\alpha$ emission line from
VDES J0020–3653 is best reproduced by a combination of a broad Lorentzian and
a broad Gaussian profile plus much weaker narrow components of H$\alpha$ and
[N ii] $\lambda$6584 lines from the host galaxy, which gives a redshift of
$z=6.8601\pm 0.0001$, i.e. slightly higher than $z=6.855$ based on a fit of
two Gaussian components reported by Marshall et al. (2023).

Fits of Lorentz profiles to H$\alpha$ and H$\beta$ emission from DELS
J0411–0907 suggest a systemic redshift of $z=6.8253\pm 0.0002$ consistent with
the initial redshift reported by Pons et al. (2019), and $z=6.8260\pm 0.0007$
based on [C ii] 158 $\mu$m emission (Yang et al., 2021), while multi Gaussian
component fits to [O iii] $\lambda$5007 and H$\alpha$ emission lines suggest a
smaller redshift of $z=6.818$ as the systemic one (Marshall et al., 2023). The
difference in the inferred systemic redshift is caused by a narrow component
of H$\alpha$ that is blueshifted by $\sim$380 km s-1 relative to the broad
H$\alpha$ component (Marshall et al., 2023).

The [O iii] $\lambda$5007 line in the spectrum of ULAS J1342+0928 can be fit
by a double Gaussian line profile, where the narrow component suggests a lower
redshift of $z=7.5317\pm 0.0007$ compared to the centroid of the broad
component at $z=7.5435\pm 0.0002$. In comparison, a single Gauss component fit
to the H$\beta$ emission line provides an adequate fit with $z=7.5353\pm
0.0004$. The redshift derived from H$\beta$ is smaller by $211\pm 35$ km s-1
compared to $z=7.5413\pm 0.0007$ measured from [C ii] 158 $\mu$m emission from
the quasar host (Venemans et al., 2017).

The Mg ii line profile in the spectrum of UHS J0439+1634 is asymmetric with a
significant blue wing. Fitting the broad line with a Lorentzian profile, we
derive a redshift of $z=6.5102\pm 0.0005$ consistent with a fit to the same
line in a GNIRS spectrum from the Gemini telescope that reveals $z=6.511\pm
0.003$ (Fan et al., 2019). The [C ii] 158 $\mu$m emission from this object
suggests a systemic redshift of $z=6.5188\pm 0.0004$ (Yang et al., 2019). The
H$\alpha$ line is covered by the NIRSpec Band III data, and its line profile
shows a broad component that is well fit by a single Lorentzian profile with
narrow emission line components superimposed. The narrow emission lines are
recognised as H$\alpha$ and [N ii] $\lambda\lambda$6548,6584 from the host
galaxy, suggesting a redshift of $z=6.5185\pm 0.0003$, consistent with that
from the [C ii] 158 $\mu$m emission.

The systemic quasar redshifts inferred from our spectra and used in the
following analysis are summarized in Table 2.

Quasar | $z_{\mathrm{sys,JWST}}$ | $z_{\mathrm{sys,[{C\textsc{ii}}]}}$ | $J_{\mathrm{AB}}$
---|---|---|---
VDES J0020–3653 | 6.8601$\pm$0.0001 |  | 20.40$\pm$0.10
DELS J0411–0907 | 6.8253$\pm$0.0002 | 6.8260$\pm$0.0007 | 20.02$\pm$0.14
UHS J0439+1634 | 6.5185$\pm$0.0003 | 6.5188$\pm$0.0004 | 17.46$\pm$0.02
ULAS J1342+0928 | 7.5353$\pm$0.0004 | 7.5413$\pm$0.0007 | 20.30$\pm$0.02
Table 2: Systemic quasar redshifts derived from fits to the Balmer emission
lines in the NIRSpec spectra. The third column lists the quasar systemic
redshifts derived from [C ii] 158 $\mu$m emission (Venemans et al., 2017; Yang
et al., 2019, 2021), and the last column the $J$ band magnitude (Yang et al.,
2021; Reed et al., 2019).

###  3.2 Normalization of spectra

For computations of absorption line equivalent widths, we need to determine
the level of the continuum emission around the lines. First, we make a global
fit of the continuum level across the entire spectrum using the Astrocook code
(Cupani et al., 2022), using nodes with a velocity spacing of 1000 km s-1, and
reject nodes that deviate at the 5$\sigma$ level within each velocity-window.
Some of these outliers are caused by the presence of absorption lines. A
spline function is then fit between the remaining node-points. A small window-
size is necessary in order to also fit the broad emission lines of the
quasars. The spectra in Figures 2, 2, 4, and 4 show strong modulations in the
continuum flux level in the fixed slit spectra around 1.8 ${\mu}$m and 3.0
${\mu}$m as described in Section 2. We therefore choose to manually correct
the continuum node points within Astrocook before the normalization of the
spectra, $f_{\mathrm{norm}}(\lambda)$, and their associated error spectra,
$\sigma(f_{\mathrm{norm,\lambda}}$) are computed. The normalized spectra are
shown in Appendix A with the lines belonging to the identified absorption
systems at different redshifts overlaid.

With normalized spectra the observed equivalent widths ($W_{\mathrm{obs}}$)
and their uncertainties are computed by

$W_{\mathrm{obs}}=\sum_{\lambda_{1}}^{\lambda_{2}}(1-f_{\mathrm{norm}}(\lambda))\Delta\lambda$
(2)
$\sigma(W_{\mathrm{obs}})=\sqrt{\sum_{\lambda_{1}}^{\lambda_{2}}\sigma^{2}(f_{\mathrm{norm,\lambda}})\Delta\lambda^{2}}$
(3)

where $\Delta\lambda$ is the wavelength bin per pixel in the spectra.

In order to analyse the quasar spectra bluewards of their Ly$\alpha$ emission,
we have to take a different approach to normalize the quasar spectra since the
IGM has effectively absorbed most of the emission in the Gunn-Peterson trough
at these redshifts. We use the python module PyQSOFit (Guo et al., 2018) to
construct a model of the quasar continuum emission and broad Ly$\alpha$
emission line. We choose to model the continuum as a power-law function at
wavelengths not affected by broad emission lines and superimpose best fits of
the quasar Ly$\alpha$ emission line on top of the continuum, using a set of
Gaussian functions with different widths, including both broad and narrow
components. The observed spectra are divided by the constructed model spectra
and the normalized spectra show the relative transmission in the IGM. We use
these normalized spectra to explore the connection between the metal
absorption line systems and transmission spikes in the IGM in Section 6.

##  4 Metal-line absorption systems

In the four quasar spectra we identify metal absorption line systems at
several different intervening redshifts as well as in the vicinity of the
quasar redshifts. Compared to the IGM where metal ions are typically highly
ionized, intervening ISM or CGM lines are typically recognised via low-
ionization absorption lines from O i, Si ii, C ii, S ii, Al ii, Zn ii, Cr ii,
Mn ii, Fe ii, Mg ii, and Mg i, but also may have higher ionization lines from
Al iii, C iv, and Si iv. We used the atomic line list in Krogager (2018),
which is compiled from the Vienna Atomic Line Data Base (Piskunov et al.,
1995; Ryabchikova et al., 2015).

The discovery and classification is done through visual inspection searching
for systems of lines commonly detected in quasar damped Ly$\alpha$ systems
(e.g. Prochaska et al., 2003) and Gamma-ray burst afterglow spectra that
include strong damped Ly$\alpha$ systems (e.g. Christensen et al., 2011).
Visual identifications, just as machine generated ones, are aided by the
presence of line doublets, such as Mg ii $\lambda\lambda$2796,2803 or C iv
$\lambda\lambda$1548,1550 whose wavelength separations and fixed relative
strengths given by their oscillator strengths are well known. The relative
line strengths are different by a factor of two in the optically thin regime,
while in the optically thick regime, the line strengths are identical. In
addition, absorption lines may have multiple kinematic components and blending
with other lines makes the identification of line doublets more complex. In
order to identify an absorption line system, we require that at least two or
more lines are found for each system such that the identity of the lines and
the redshift of the absorption system is uniquely determined.

In the spectrum of VDES J0020–3653 we identify 16 absorption systems, in DELS
J0411–0907 we find 13 systems, in UHS J0439+1634 17 systems, and in ULAS
J1342+0928 we find 15 individual absorption systems. In total, 61 identified
systems are detected at $2.4\lesssim z\lesssim 7.5$. Appendix A presents the
four normalized quasar spectra with identified absorption line systems
overlaid. The systems and individual absorption lines are presented in both
graphical and tabular form in Appendix B.

###  4.1 Distinguishing C iv doublets from O i/Si ii absorbers

We need to pay particular attention to the identification of C iv
$\lambda\lambda$1548,1550 doublet and O i $\lambda$1302-Si ii $\lambda$1304
absorbers since the two pairs of transitions have transition wavelength ratios
of 0.99834 and 0.99831, respectively. These two separations cannot be
distinguished at the $R\\!\leavevmode\nobreak\ \simeq\\!\leavevmode\nobreak\
2700$ spectral resolution of the NIRSpec spectra, and weak systems may not be
detected at the signal-to-noise level needed to unambiguously detect the C iv
$\lambda\lambda$1548,1550 doublet. To single out potential O i
$\lambda$1302-Si ii $\lambda$1304 absorption systems, absorption lines from Si
ii $\lambda$1260 and Si ii $\lambda$1526 should be detected too, since they
are stronger by a factor of 8.9 and 1.6, respectively, relative to the Si ii
$\lambda$1304 line. The Si ii $\lambda$1260 line may, however, fall in the
Ly$\alpha$ forest region and be completely absorbed in the GP troughs. In
addition to other silicon transitions, we also search for other associated
lines from C ii $\lambda 1334$ or Mg ii $\lambda\lambda$2796,2803. When none
of these lines are detected, we classify the absorber as a C iv system.

###  4.2 Comparison with known absorption systems

As surveys detect more and more high-redshift quasars, an increasing number of
studies of intervening absorption lines have been performed. The recent XQR-30
survey is based on a sample of 30 quasars at $5.8<z<6.6$ observed with
VLT/X-shooter at higher-resolution (D’Odorico et al., 2023). The only quasar
in common with the NIRSpec sample is UHS J0439+1634, where Davies et al.
(2023a) identify a total of 43 absorption line systems from $z=2.2-6.48$. All
of the 17 systems we detect, apart from the Mg ii doublet at $z=6.208$, are
also detected in Davies et al. (2023a). In contrast, in our lower spectral
resolution data we are not able to clearly identify and detect the other
absorption line systems. In particular, Davies et al. (2023b) find several
weak C iv doublets in the range from $z=5.0-5.3$ and also weak rest-frame
equivalent width ($W_{r}<0.05$ Å) Mg ii absorbers at $z\approx 2.3-5$ that we
cannot confirm in the NIRSpec data. The non-confirmation is either due to
blending with other stronger absorption lines from different redshifts, or the
fact that only one of the two lines in the doublet can be detected in the
NIRSpec data, while the other component is clearly ruled out from the
knowledge of the fixed line strengths between the two doublet lines. We are
also not able to confirm several of the reported Si iv doublets. We note that
around $z\sim 5$ C iv falls in a wavelength region affected by (weaker)
telluric absorption lines and also strong sky emission lines, that Davies et
al. (2023a) do, however, correct for in their analysis.

Turning to absorption line systems at $z>5$, Cooper et al. (2019) report two
systems at $z=5.936$ and 6.178 towards DELS J0411–0907, and at $z=5.889$,
6.271 and 6.843 towards ULAS J1342+0928. These strong systems are all clearly
detected in the NIRSpec spectra. In addition, we also detect a few weaker
$z>5$ Mg ii or C iv doublets towards DELS J0411–0907, whereas towards ULAS
J1342+0928, we also find several additional low-ionization systems at $z>7$
identified through multiple lines. ULAS J1342+0928 was observed with both
X-shooter at the VLT and with FIRE at the Magellan Telescope at a higher
spectral resolution as presented in a detailed case study of the $z=6.84$
absorption system in Simcoe et al. (2020). We retrieved the processed
2-dimensional X-shooter spectra Advanced Data Products from the ESO archive in
order to verify whether the high-redshift absorption line systems detected in
the NIRSpec data are also present in the higher resolution spectra. For the
higher redshift system at $z=7.368$, the X-shooter data reveals a clear
detection of Si ii $\lambda$1260 whereas Mg ii falls in a noisy region of the
spectrum and is not detected. None of the absorption lines from the $z=7.443$
or $z=7.476$ systems can be detected in the X-shooter data, consistent with
the less sensitive detection-limit in the ground-based data.

###  4.3 Line densities

Theoretically we expect a detection limit of absorption lines observed
equivalent widths at $W_{\mathrm{obs}}=0.2-0.5$ Å as described in Sect. 2 and
Fig. 5. With the wide redshift range of the absorption systems, this implies
that the detection limit on the rest-frame equivalent width
$W_{r}=W_{\mathrm{obs}}/(1+z_{\mathrm{abs}})$ depends on the absorber
redshift. The typical detection limit of $W_{r}$ is 0.025–0.03 Å. When
comparing line densities with those of from the literature, we adopt the same
$W_{r}$ cut-off.

To compute line densities, we combine all tables in Appendix B, extract the
individual absorption transitions of interest, e.g. C iv $\lambda$1548, O i
$\lambda$1302, or Mg ii $\lambda$2796 and compute their $W_{r}$. Line
densities from metal-absorption lines should represent random lines of sight
through the Universe, and we therefore exclude absorbers at redshifts closer
than 3000 km s-1 from the quasar systemic redshift, since these absorption
lines represent proximate systems that may be influenced by the quasars.

We compute the redshift path length, which is defined as the full spectral
range in each spectrum where it would be possible to detect the chosen
absorption line. For each quasar we determine the minimum and maximum redshift
where a chosen absorption line can be discovered, excluding regions in the
spectra where there are gaps or where the signal-to-noise ratio is low. The
sum of this gives the sensitivity function, $g(z)$, which presents for each
redshift the number of lines of sight probing the absorption line of interest
with an observed $W_{\mathrm{obs}}>0.3$ Å. The result is presented in Fig. 7
which illustrates that the NIRSpec observations in this study are sensitive to
intervening O i $\lambda$1302 and C ii $\lambda$1334 absorbers at $6\lesssim
z\lesssim 7.5$, Si ii $\lambda$1526 and C iv absorbers at $5\lesssim z\lesssim
7.5$, Mg ii at $2.2\lesssim z\lesssim 7.5$, and Si iv $\lambda$1393 at
$5.5\lesssim z\lesssim 7.5$.

Figure 7: Sensitivity functions, $g(z)$, for the coverage of O i
$\lambda$1302, C ii $\lambda$1334, Si ii $\lambda$1526, C iv $\lambda$1548, Si
iv $\lambda$1393, and Mg ii $\lambda$2796\. The colored lines illustrate how
many of the quasar spectra contribute to the search of metal absorption lines
as a function of redshift.

By integrating the sensitivity function one can compute the line density
$dn/dz$, defined as the number of absorbers $n$ within a chosen redshift
interval $dz$. However, when analysing the line density over a very large
redshift range it is conventional to instead analyse the comoving density
(Bahcall & Peebles, 1969) and use the comoving absorption path length $X$
defined as:

$dX=\frac{(1+z)^{2}}{\sqrt{\Omega_{M}(1+z)^{3}+\Omega_{\Lambda}}}dz.$ (4)

The line density is then computed as

$dn/dX=\frac{\sum_{z_{1}}^{z_{2}}n_{i}}{\int_{z_{1}}^{z_{2}}g(z)dX},$ (5)

where $n_{i}$ is the number of absorbers of the chosen species in quasar
number $i$, summed over the redshift interval between $z_{1}$ and $z_{2}$ in
all the quasar spectra. The denominator is the integral of the absorption path
length in that redshift interval.

To derive uncertainties of the line densities we assume that the distribution
of absorbers is Poissonian in nature. For the small number statistics relevant
in this work we use the upper- and lower 68% confidence intervals tabulated in
Gehrels (1986).

###  4.4 Absorption line density redshift evolution

Among the many different atomic species detected in the four quasar spectra,
in this work we focus on the previously well-studied ones among the low-
ionization and high-ionization lines.

For each atomic transition we compute the line density as a function of
redshift and compare the JWST data with line densities at lower redshifts from
the literature. Additionally, we compare the line densities with the predicted
evolution of both low- and high-ionizations column density distribution from
simulations in the redshift range $5<z<8$ (Huscher et al. 2023, in prep.). The
simulations are based on updates to the Technicolor Dawn cosmological
hydrodynamic simulation (Finlator et al., 2018, 2020). By probing random lines
of sight through the simulation volumes from redshifts $z=5$ to $z=10$, one
can identify various absorption lines (e.g. Doughty & Finlator, 2019, 2023)
and compute their equivalent widths and column densities. Compared to previous
simulations, the model of Huscher et al. (2023, in prep.) includes predictions
of equivalent widths and column densities of other atomic transitions for the
modelled absorbers, allowing us to compare with the observations of a range of
elements in different redshift bins from $z=5$ to $z=10$. For comparison with
the NIRSpec observations, we restrict our attention to the relevant redshift
interval $5<z<7.5$.

####  4.4.1 O i $\lambda$1302 absorbers

We detect seven O i $\lambda$1302 absorbers listed in Table 3 within the total
absorber path length interval $\Delta X=7.68$. The highest redshift absorption
system at $z=7.476$ lies close to the ULAS J1342+0928 quasar redshift at
$z=7.535$, and is considered proximate according to the cut-off of at 3000 km
s-1, and is not included in the analysis of the statistics of line densities.
The two other O i systems at $z>7$ represent the highest redshift detections
of neutral oxygen absorption along the line of sight to high-redshift quasars
reported to date. In all cases, when O i is detected, we also detect more than
one other line at the same redshift, so we can be confident to clearly
identify the O i absorption systems (See Appendix B).

Figure 8 shows the line density of O i absorbers with $W_{r}>0.05$ Å as a
function of redshift for the new NIRSpec measurement compared with values at
lower redshifts from Becker et al. (2019). The line density for the highest
redshift point has a significant uncertainty, implying that even though the
line density shows an increase from $z=6$ to $z=7$ the values are also
consistent with being constant.

For O i as well as for other low-ionization line species, the model predicts
an increase in line density with increasing redshift up to $z\sim 7$ and at
higher redshifts ($z>7$) the line density decreases. The reason for this
behaviour is a combination of a decreasing metallicity of the CGM with
increasing redshift, while the turnover from $z=7\rightarrow 6$ is caused by
the increasing level of ionization of the CGM with time (Doughty & Finlator,
2019). With increasing cosmic time, O i absorbers have a smaller covering
fraction as the clouds’ outskirts become increasingly ionized while the total
metallicity of oxygen and its distribution in the CGM of galaxies does
increase with time (Doughty & Finlator, 2019).

Table 3: O i $\lambda$1302 systems

Quasar | $z_{\mathrm{abs}}$ |  $W_{\mathrm{rest}}$ (Å)
---|---|---
VDES J0020–3653 | 6.4535 | 0.091$\pm$0.007
VDES J0020–3653 | 6.5625 | 0.062$\pm$0.008
VDES J0020–3653 | 6.6690 | 0.115$\pm$0.010
UHS J0439+1634 | 6.2880 | 0.119$\pm$0.006
ULAS J1342+0928 | 7.3680 | 0.061$\pm$0.011
ULAS J1342+0928 | 7.4430 | 0.031$\pm$0.010
ULAS J1342+0928 | 7.4760† | 0.045$\pm$0.010

  * •

†The highest redshift system at $z=7.4760$ lies $\approx$2000 km s-1 from the
quasar redshift and is considered as a proximate system, and therefore not
included in the computation of line densities.

Figure 8: Line densities as a function of redshift for low-ionization lines.
In all panels, colored symbols represent the JWST data, where error bars for
the line densities represent 68% confidence intervals for small-number Poisson
uncertainties (Gehrels, 1986). The line densities are compared to predictions
from numerical models (Huscher et al. 2023, in prep.) computed from the same
selection of $W_{r}$ as from the NIRSpec observations. Upper left panel: O i
$\lambda$1302 absorption line densities for systems with $W_{r}>0.05$ Å
showing an increase in line density at $z>6.5$ from the NIRSpec data compared
to lower redshifts (Becker et al., 2019). Upper right panel: C ii
$\lambda$1334 absorption line densities for systems with $W_{r}>0.03$ Å
compared to lower redshift values derived from the catalog in Davies et al.
(2023a). Lower left panel: Si ii $\lambda$1526 absorption line densities for
systems with $W_{r}>0.025$ Å compared to that derived from the absorption
database in Davies et al. (2023a). The model prediction at $dn/dX\approx 0.04$
under-produces the Si ii line densities by a factor of $\approx$12\. Lower
right panel: Mg ii $\lambda$2796 absorption line densities for systems with
$W_{r}>0.3$ Å. Compared to the Mg ii study in (Chen et al., 2017), the NIRSpec
data suggest that the line density could be constant or have a shallow
decrease with increasing redshifts in agreement with the model prediction.

####  4.4.2  C ii $\lambda$1334 absorbers

In the JWST spectra we identify eight intervening C ii $\lambda$1334
absorption systems listed in Table 4 over a full path length interval of
$\Delta X=11.10$ at $5.9<z<7.5$. Due to the available search path, some of the
C ii lines do not have detected O i absorption, since these lie blueward of
the quasar Ly$\alpha$ wavelength. The upper right hand panel in Fig. 8
illustrates the C ii line densities with $W_{r}>0.03$ Å.

In comparison, Becker et al. (2019) find that most of their O i absorbers also
have C ii lines, but this is primarily caused by selection since to identify O
i absorption lines, the presence of other lines (e.g from C ii) at the same
redshift is required. The line density of C ii at $z<5$ therefore shows a
redshift evolution similar to that of O i. Using the absorption line catalog
of carbon absorbers in Davies et al. (2023a), we compute the C ii line
densities in three redshift bins at $5.0<z<6.5$ as illustrated in Fig. 8.
Including the line density at $z>6$ from the NIRSpec data we find a general
trend of an increasing line density with redshift.

Table 4: C ii $\lambda$1334 systems

Quasar | $z_{\mathrm{abs}}$ |  $W_{\mathrm{rest}}$ (Å)
---|---|---
VDES J0020–3653 | 6.4535 | 0.103$\pm$0.010
VDES J0020–3653 | 6.6690 | 0.168$\pm$0.010
DELS J0411–0907 | 6.1774 | 0.215$\pm$0.006
UHS J0439+1634 | 5.9280 | 0.008$\pm$0.007
UHS J0439+1634 | 6.2880 |  $<$0.086$\pm$0.007†
ULAS J1342+0928 | 6.8427 | 0.626$\pm$0.011
ULAS J1342+0928 | 7.3680 |  $<$0.063$\pm$0.010†
ULAS J1342+0928 | 7.4430 | 0.072$\pm$0.009

  * •

†The upper limit to the equivalent width is quoted because the line is blended
with another absorption line at a different redshift (See Appendix B).

####  4.4.3 Si ii $\lambda$1526 absorbers

Several Si ii transitions can be detected in the spectra with UV transitions
at rest-frame wavelengths at 1260, 1304, 1526, and 1808 Å. Due to the wide
range of oscillator strengths and possible contamination with other absorption
lines, not all absorption lines are detected for any given system. When
deriving the line density of Si ii it is convenient to use a line which is
both a strong transition and also has a large redshift path length. The 1808 Å
line has the longest redshift path length, but it is the weakest transition of
the four, whereas the strongest 1260 Å line can only be detected in a very
short path length interval because its wavelength is close to Ly$\alpha$.
Therefore we choose to investigate the line density based on the 1526 Å line,
which is also a strong transition, and has a larger redshift path length
interval.

In the JWST spectra we identify 12 intervening Si ii absorption systems listed
in Table 5 over a full path length interval of $\Delta X=29.0$ at $5<z<7.45$.
We compare the inferred line density with that derived from the database in
Davies et al. (2023a) in the lower left hand panel Figure 8. It is apparent
that the evolution of the Si ii line density could either be constant or show
a mild increase with redshift to $z\approx 7$.

In the models of Huscher et al. (2023 in prep), rest-frame equivalent widths
are computed for the Si ii $\lambda$1260 transition. For non-saturated lines,
the relation between two lines in the linear part of the curve of growth scale
as $W_{r_{1}}/W_{r_{2}}=\lambda_{1}^{2}f_{1}/(\lambda_{2}^{2}f_{2})$, where
$\lambda_{1}$ and $\lambda_{2}$ are the rest-frame wavelength of the two
transitions and $f_{1}$ and $f_{2}$ are the oscillator strengths. We can
therefore convert the estimated Si ii $\lambda$1260 line densities to that for
Si ii $\lambda$1526\. The model predicts a constant line density of
$dn/dX\approx 0.04$ from $z=5$ to $z=8$ for absorbers with $W_{r}<0.025$ Å,
which is smaller by a factor of $\sim$ 12 compared to the NIRSpec
observations. Alternatively, the model predicts Si ii equivalent widths that
are a factor of 2.5 smaller than the observed ones.

Table 5: Si ii $\lambda$1526 systems

Quasar | $z_{\mathrm{abs}}$ |  $W_{\mathrm{rest}}$ (Å)
---|---|---
VDES J0020–3653 | 5.4695 | 0.028$\pm$0.011
VDES J0020–3653 | 5.7910 | 0.074$\pm$0.014
VDES J0020–3653 | 6.4535 | 0.028$\pm$0.012
VDES J0020–3653 | 6.5625 | 0.059$\pm$0.012
VDES J0020–3653 | 6.6690 | 0.092$\pm$0.013
DELS J0411–0907 | 5.9350 | 0.143$\pm$0.010
DELS J0411–0907 | 6.1774 | 0.127$\pm$0.008
UHS J0439+1634 | 5.8190 | 0.047$\pm$0.006
UHS J0439+1634 | 6.2840 | 0.286$\pm$0.009
ULAS J1342+0928 | 6.8427 | 0.186$\pm$0.012
ULAS J1342+0928 | 7.3680 | $<0.03^{\dagger}$
ULAS J1342+0928 | 7.4430 | 0.018$\pm$0.010

  * •

†The Si ii $\lambda$1526 line is not detected in the spectrum, but the system
is nevertheless included in the analysis since both Si ii $\lambda$1260 and Si
ii $\lambda$1304 are detected at this redshift.

####  4.4.4 Mg ii $\lambda$2796 absorbers

The classical Mg ii $\lambda\lambda$2796,2803 doublet is one of the most
commonly studied atomic transitions in quasar spectra (e.g. Bergeron & Boissé,
1991; Churchill et al., 1999). It is easily distinguishable in absorption
because of its known line separation and the fact that the 2796 line has twice
the oscillator strength as the 2803 line. The line doublet enters the optical
range at $z=0.3$ and can be traced up to $z\sim 7$ in ground-based near-IR
spectra (Matejek & Simcoe, 2012; Chen et al., 2017). In comparison to ground-
based observations of Mg ii absorbers at $z>2.5$ that are heavily affected by
the presence of sky emission- and telluric absorption lines, the NIRSpec
spectra are much cleaner, allowing easy visual identification of Mg ii
absorbers even at the highest redshifts. It is therefore not surprising that
we find some previously undetected absorption systems in these well-studied
high-redshift quasars.

The 3$\sigma$ detection limit of the $W_{\mathrm{obs}}$ for Mg ii systems in
the NIRSpec spectra is 0.3 Å. Above this limit, we find 49 Mg ii absorbers
from $z=2.4$ to $z=7.45$ including the highest redshift detection of an
intervening absorption system to date (see Appendix B). As for the other
lines, absorption line systems that lie closer than 3000 km s-1 to the quasar
redshift are omitted in the computation of line densities. The highest
redshift proximate absorption system at $z=7.476$ is also detected in Mg ii,
and is excluded in the line density computation.

The total redshift path length for Mg ii absorption systems covered by the
four spectra is $\Delta X=73.3$. To compare with other studies, we focus on
the absorbers with $W_{r}>0.3$ Å, where we detect 30 systems. The lower right
hand panel in Fig. 8 illustrates that the line density for Mg ii absorbers
with $W_{r}>0.3$ Å remains constant or at most exhibits a shallow decrease to
the highest redshifts in agreement with previous findings at $z\lesssim 6$
(Matejek & Simcoe, 2012; Chen et al., 2017; Bosman et al., 2017). The observed
distribution agrees with the model prediction of a shallow decrease in line
density with redshift at $z>6$.

Focusing on all the detected Mg ii systems including both strong- and weak
lines, we derive the rest-frame equivalent width distribution described by a
powerlaw function

$\frac{dn}{dW}=\frac{n_{*}}{W*}\exp(-W/W_{*})$ (6)

where $dW$ is the rest-frame equivalent width increment. We do not include a
redshift dependence, since the line density is constant with redshift. The
best fit parameters are $W_{*}$=0.758 and $n_{*}$=1.82. Figure 9 presents the
distribution compared to a large quasar sample examined in Chen et al. (2017),
and compared to ground-based observations, we see no evidence for a change in
the $W_{r}$ distribution in the NIRSpec spectra.

Figure 9:  Distribution of Mg ii equivalent widths from all systems across the
redshift interval from $z=2.5-7.5$. The best fit is illustrated by the solid
line. Compared to much larger quasar surveys at lower redshifts (Chen et al.,
2017), where the dashed line is the best fit, there is no evidence for a
different distribution in the NIRSpec data.

Strong Mg ii $\lambda$2796 absorption lines with rest-frame $W_{r}>1$ Å have
received particular attention since these absorbers are found to have the same
redshift evolution as that derived from the integrated [O ii] luminosity of
galaxies and are proposed to trace the cosmic star-formation rate density at
$z\approx 1$ (Prochter et al., 2006; Ménard et al., 2011). It is therefore
relevant to investigate the strongest Mg ii lines at the highest redshifts
with the new NIRSpec data. We identify 11 strong Mg ii $\lambda$2796 absorbers
with $W_{r}>$ 1Å listed in Table 6. The line density $dn/dX$ is illustrated in
Fig. 10. Since we only have four JWST spectra, small-number Poisson statistics
dominate the uncertainties (Gehrels, 1986), but overall we see that the line
density is consistent with other high-redshift quasar and gamma-ray burst
afterglow samples that find a decrease in strong $W_{r}>1$ Å systems at $z>3$
(Chen et al., 2017; Christensen et al., 2017; Zou et al., 2021). None of the
absorbers detected at $z>7$ fall in the strong-line category.

Figure 10: Strong Mg ii $\lambda$2796 absorption line densities for systems with $W_{r}>1$ Å. The JWST data are compared with the strong Mg ii line densities in a large quasar sample (Chen et al., 2017) and with the XQ-100 survey (Christensen et al., 2017), and are consistent with a decrease in the line densities to the highest redshifts.  Table 6: Strong Mg ii systems Quasar | $z_{\mathrm{abs}}$ |  $W_{\mathrm{rest}}$ (Å)
---|---|---
VDES J0020–3653 | 3.4087 | 2.72$\pm$0.02
VDES J0020–3653 | 3.6040 | 1.39$\pm$0.02
VDES J0020–3653 | 4.0738 | 2.08$\pm$0.02
DELS J0411–0907 | 2.5771 | 1.77$\pm$0.02
DELS J0411–0907 | 3.4290 | 1.79$\pm$0.02
DELS J0411–0907 | 3.7760 | 1.00$\pm$0.02
DELS J0411–0907 | 4.2815 | 2.54$\pm$0.01
UHS J0439+1634 | 3.1700 | 1.31$\pm$0.01
UHS J0439+1634 | 4.5230 | 1.16$\pm$0.01
ULAS J1342+0928 | 3.3758 | 1.48$\pm$0.02
ULAS J1342+0928 | 6.8427 | 1.38$\pm$0.04

####  4.4.5 C iv absorbers

Turning to the high-ionization transitions, we identify 17 C iv
$\lambda\lambda$1548,1550 absorption systems at $5.20<z<6.86$ in our spectra
as listed in Table 7. The total path length interval for C iv is $\Delta X$ =
30.8. Two of the absorbers lie closer than 3000 km s-1 from the quasar
systemic redshifts, and are excluded in the computation of absorption line
density shown in Figure 11. The NIRSpec data reveal that the C iv line density
continues to decrease with redshift beyond $z>6$. Compared to the observed
line densities, the model predicts a much lower line density. This discrepancy
was also noted in D’Odorico et al. (2022), who discussed the mismatching C iv
as a problem in the models in Finlator et al. (2020). If models are forced to
match the line density of C iv, then the line densities for Si iv would be
over-predicted.

Table 7: C iv systems

Quasar | $z_{\mathrm{abs}}$ |  $W_{\mathrm{rest}}$ (Å)
---|---|---
VDES J0020–3653 | 5.2005 | 0.044$\pm$0.008
VDES J0020–3653 | 5.3275 | 0.170$\pm$0.008
VDES J0020–3653 | 5.4699 | 0.228$\pm$0.013
VDES J0020–3653 | 5.7913 | 0.357$\pm$0.014
VDES J0020–3653 | 6.5030 | 0.070$\pm$0.013
VDES J0020–3653 | 6.8550† | 0.201$\pm$0.007
DELS J0411–0907 | 5.4258 | 0.052$\pm$0.009
DELS J0411–0907 | 5.9349 | 0.067$\pm$0.009
UHS J0439+1634 | 5.2740 | 0.179$\pm$0.007
UHS J0439+1634 | 5.3945 | 0.808$\pm$0.006
UHS J0439+1634 | 5.3945 | 0.808$\pm$0.006
UHS J0439+1634 | 5.5228 | 0.108$\pm$0.007
UHS J0439+1634 | 5.7360 | 0.123$\pm$0.006
UHS J0439+1634 | 6.2840 | 0.390$\pm$0.007
UHS J0439+1634 | 6.4877† | 0.238$\pm$0.004
ULAS J1342+0928 | 5.8888 | 0.263$\pm$0.010
ULAS J1342+0928 | 6.7490 | 0.103$\pm$0.010

  * •

†These absorption systems lie within 3000 km s-1 of the quasar systemic
redshift and are excluded from the line density computation.

Figure 11: Line densities of high-ionization lines. The left hand panels shows
the C iv absorption line densities with $W_{r}>0.05$ Å. The C iv line
densities at the highest redshifts from the E-XQR-30 survey (D’Odorico et al.,
2022) are illustrated for comparison. The same E-XQR-30 data set was analysed
in Davies et al. (2023b) and has been corrected for incompleteness. The right
hand panel shows Si iv absorption line densities. The Si iv line densities for
systems with $W_{r}>0.03$ Å from D’Odorico et al. (2022) are illustrated for
comparison.

####  4.4.6 Si iv absorbers

In the spectrum of ULAS J1342+0928 we detect a candidate Si iv $\lambda$1393
line at $z=6.476$, which is also detected in C iv. The spectrum does not
clearly reveal the fainter Si iv $\lambda$1402 line. In the spectrum of
J0439+1634 we detect a single Si iv doublet, also reported by Davies et al.
(2023a), but we note that both transitions in this doublet lie at the wings of
two broad absorption line features in the quasar spectrum. These absorbers are
listed in Table 8.

The total path length for Si iv systems in the JWST spectra is $\Delta
X=18.60$. Fig. 11 compares the Si iv line density from the highest redshift
NIRSpec data to the lower redshift measurements of D’Odorico et al. (2022). As
for the C iv systems, Si iv line densities are known to exhibit a rapid
decrease in line density at $z>5$ (D’Odorico et al., 2022), which we confirm
with our very few detections of Si iv absorption lines.

Table 8: Si iv $\lambda$1393 systems  Quasar | $z_{\mathrm{abs}}$ |  $W_{\mathrm{rest}}$ (Å)
---|---|---
UHS J0439+1634 | 6.173 | 0.096$\pm$0.007
ULAS J1342+0928 | 6.746 | 0.041$\pm$0.012

####  4.4.7  Ca ii and Na i lines

Most of the strong absorption lines arising in the ISM, CGM and IGM are from
rest-frame UV lines, but the extended NIR wavelength coverage of the NIRSpec
Band III G395H data allows us to search for rest-frame optical absorption
lines associated with any of the absorption systems. Since the ionization
potential of Ca ii $\lambda\lambda$3934,3969 and Na i
$\lambda\lambda$5891,5897 is less than that of hydrogen, these absorption
lines arise in neutral regions and are frequently associated with dusty
absorbers at lower redshifts detected in quasar spectra ($z\lesssim 1$)
(Heckman et al., 2000; Wild et al., 2006). Weak and strong Ca ii absorbers,
with a division at $W_{r}=0.7$ Å, probe either galaxy disc gas or halo-
components, respectively (Fang et al., 2023). We detect Ca ii lines in three
absorption systems at $z=$3.17, 3.631, and 3.37, however, the signal is not
sufficient to recover both lines in the doublet in any of the systems. Two of
the absorbers fall into the weak line category with $W_{r,3934}<0.7$ Å, while
the $z=3.631$ system towards ULAS J1342+0928 is a strong one.

The other rest-frame optical line of interest, the doublet Na i
$\lambda\lambda$5891,5897 is covered by the Band III data for the highest
redshift absorbers. However, as discussed in Section 2, the statistical errors
on the Band III portions of the spectra are poorly defined. We therefore
choose to only focus on absorption line systems that were identified in the
Bands I and II Fixed Slit data, and test whether an associated Na i doublet is
present in any of the $z>3.9$ systems. Only a single high-redshift absorber at
$z=5.6814$ towards ULAS J1342+0928 has a potential Na i absorption line
doublet detection.

###  4.5 Combined line density redshift evolution

To compare the evolution of the atomic species, all the line densities in
previous subsections are combined in Fig. 12, which shows the evolution with
redshifts for different elements derived in the literature with an extension
to the highest redshifts ($z>6.0$) detected in the NIRSpec data. All the
computed line densities from the JWST data are listed in Table 9.

Both C iv and Si iv experience a drastic drop in line densities continuing at
the highest redshifts ($z>6.5$) detected in the NIRSpec data, while the line
densities of the low-ionization species O i and C ii continue to increase at
these redshifts. The evolution of Si ii from low redshift ($z<5$) is not
constrained, and with the large uncertainties from the NIRSpec data, the Si ii
line density could be constant at the redshifts covered in this work, but
could also trace the same increase in line density as detected for O i and C
ii. The transition in line densities of both carbon and silicon from
predominantly low-ionization at high redshifts to high-ionization species at
low-redshifts occurs at $z\approx 6.0$. In contrast, the line density of Mg ii
remains constant throughout the range from $z=3$ to $z=7$, or may even
experience a shallow decline towards higher redshifts as predicted by the
model.

Figure 12: Comparison of the redshift evolution of the line densities of O i $\lambda$1302, C ii $\lambda$1334, Si ii $\lambda$1526, Mg ii $\lambda$2796, C iv $\lambda$1548, and Si iv $\lambda$1393\. The highest redshift data points for each of the colored curves represent the NIRSpec analyses in this work, extending from the lower-redshift line densities from O i (Becker et al., 2019), Si ii and C ii from the catalog in Davies et al. (2023a), C iv (D’Odorico et al., 2022; Davies et al., 2023b), Si iv (D’Odorico et al., 2022), and Mg ii (Chen et al., 2017).  Table 9: Compilation of line densities for all elements from the NIRSpec data. Column (2) presents the redshift range, (3) the absorption path-length interval, (4) the number of detected absorbers, and (5) the line density. Element | $z$ | $\Delta X$ | $n$ | $dn/dX$
---|---|---|---|---
(1) | (2) | (3) | (4) | (5)
O i $\lambda$1302 ($W_{r}>0.03$ Å) | 6.25 – 7.45 | 6.60 | 6 | $0.91^{+0.54}_{-0.36}$
O i $\lambda$1302 ($W_{r}>0.05$ Å) | 6.25 – 7.45 | 6.60 | 5 | $0.76^{+0.51}_{-0.33}$
C ii $\lambda$1334 ($W_{r}>0.03$ Å) | 5.95 – 7.45 | 10.64 | 6 | $0.66^{+0.35}_{-0.24}$
Si ii $\lambda$1526 ($W_{r}>0.025$ Å) | 5.1 – 6.3 | 17.60 | 6 | $0.45^{+0.22}_{-0.16}$
Si ii $\lambda$1526 | 6.3 – 7.5 | 10.96 | 6 | $0.55^{+0.33}_{-0.22}$
Si iv $\lambda$1394 ($W_{r}>0.03$ Å) | 5.5 – 7.5 | 18.60 | 2 | $0.11^{+0.14}_{-0.07}$
C iv $\lambda$1548 ($W_{r}>0.05$ Å) | 5.13 – 5.88 | 10.70 | 11 | $1.03^{+0.31}_{-0.31}$
C iv $\lambda$1548 | 5.88 – 6.63 | 13.49 | 4 | $0.30^{+0.23}_{-0.14}$
C iv $\lambda$1548 | 6.63 –7.38 | 5.19 | 1 | $0.19^{+0.44}_{-0.16}$
Mg ii $\lambda$2796 ($W_{r}>0.3$ Å) | 2.5 – 3.5 | 13.30 | 8 | $0.60^{+0.30}_{-0.21}$
Mg ii $\lambda$2796 | 3.5 – 4.5 | 15.27 | 7 | $0.46^{+0.25}_{-0.17}$
Mg ii $\lambda$2796 | 4.5 – 5.5 | 17.36 | 4 | $0.23^{+0.18}_{-0.11}$
Mg ii $\lambda$2796 | 5.5 – 6.5 | 18.56 | 8 | $0.43^{+0.21}_{-0.15}$
Mg ii $\lambda$2796 | 6.5 – 7.5 | 7.49 | 2 | $0.27^{+0.35}_{-0.17}$

##  5 Metal abundances

The spectral resolution and sampling (Section 2) of our NIRSpec spectra is
insufficient to accurately model the absorption line profiles via Voigt
profile fitting techniques with the aim of deriving column densities for the
different ions. However in simple cases, we can extract the same information
from the measurements of the rest-frame equivalent widths. In the optically
thin regime when the optical depth is $\tau_{\lambda}\ll 1$, the column
density $N$ scales linearly with the rest-frame equivalent width (Spitzer,
1978):

$N=1.13\times
10^{20}\frac{W_{r}({\rm\AA})}{f\lambda^{2}({\rm\AA})}\;\;\;\mathrm{[cm^{-2}]},$
(7)

where $f$ is the oscillator strength, and $\lambda$ the rest-frame transition
wavelength.

We derive the relative abundances assuming that the singly ionized atoms and
neutral oxygen are the dominant state of ionization in the absorption systems.
This is a reasonable assumption, since none of the systems with O i absorption
show any associated high-ionization transitions. Moreover at the highest
redshifts, the metallicities are expected to be low, so the optical depths of
the transitions are low too. Finally, the column density ratios are compared
to the solar photosphere abundances in Asplund et al. (2021). The silicon-to-
oxygen and carbon-to-oxygen ratios are computed as:

$\mathrm{[Si/O]}=\log\left(\frac{N_{\mathrm{SiII}}}{N_{\mathrm{OI}}}\right)-\log\left(\frac{N_{\mathrm{Si}}}{N_{\mathrm{O}}}\right)_{\odot}$
(8)
$\mathrm{[C/O]}=\log\left(\frac{N_{\mathrm{CII}}}{N_{\mathrm{OI}}}\right)-\log\left(\dfrac{N_{\mathrm{C}}}{N_{\mathrm{O}}}\right)_{\odot},$
(9)

where the solar values are
$\log\left(\frac{N_{\mathrm{Si}}}{N_{\mathrm{O}}}\right)_{\odot}=-1.18\pm
0.05$ and
$\log\left(\frac{N_{\mathrm{C}}}{N_{\mathrm{O}}}\right)_{\odot}=-0.23\pm 0.06$
(Asplund et al., 2021).

###  5.1 Pop III enrichment signatures in [Si/O]

Figure 13: Abundance ratios of [Si/O] versus [C/O]. The background colored map
with contours illustrates the metal yields from different stellar explosions
colored as follows: Red region:  Pop III Pair-instability Supernovae (PISN),
Green: Pop III Hypernovae, Blue: Pop III Core-collapse Supernovae, Gray and
Purple: Pop II Supernovae. Yields from the different stellar populations are
scaled assuming a Salpeter IMF. The Magenta circles illustrate that the
NIRSpec absorption systems listed in Table 10 have values of [Si/O] similar to
Pop III models, although some overlap with the yields of a single 15
$M_{\odot}$ Pop II star. Other intervening absorption-line systems at
$4.5<z_{\mathrm{abs}}<6$ (Becker et al., 2019; Cooper et al., 2019) (orange
circles) fall in the same region of the abundance pattern space, whereas the
gray circles representing very metal-poor damped Ly$\alpha$ absorbers at
$z_{\mathrm{abs}}\sim 3$ (Cooke et al., 2011, 2017; Welsh et al., 2019) are
also consistent with enrichment of Pop II core collapse SNe (Kobayashi et al.,
2006; Nomoto et al., 2013; Limongi & Chieffi, 2018, annotated as K06, N13, and
L18).

To date, no secure discovery of metal-free Pop-III stars has been made and the
nature of the first generation of stars remains unknown. Pop-III stars leave
behind a signature in the form of distinct metal-abundance patterns created by
the stellar explosions of the massive stars. These patterns make their imprint
on the chemical abundances of extremely metal-poor halo stars in the Milky Way
(e.g. Frebel & Norris, 2015), but also absorption line systems at high
redshifts can be used to trace the nature of the first stars, since the time
span for metal-enrichment of gas in the Universe is small.

In this section we focus on the six systems at $z_{\mathrm{abs}}>6.5$ with
identified O i $\lambda$1302 absorption lines in Table 3. Since no high-
ionization lines are detected in these absorption systems, they are
predominantly neutral and we do not require ionization corrections to derive
metal abundance ratios. We examine whether the absorption lines are likely to
be intrinsically saturated. Even though the spectra in Figures B1–B61 in
Appendix B suggest that none of the lines are saturated, at the low spectral
resolution some lines could have hidden saturation. Indeed some of the lower-
redshift strong absorption lines like the strong Mg ii lines are clearly
saturated. For each of the absorption line systems we construct a conventional
curve-of-growth with a best fit Doppler parameter between 20 and 40 km s-1
from unblended Si ii lines, where two or three separate transitions are
detected. For the uncontaminated O i, C ii, and Fe ii lines, we verify that
the transitions in $W_{r}/\lambda$ vs. $Nf\lambda$ fall on a straight line in
the linear, non-saturated part of the curve of growth. This confirms that most
lines are not saturated and that we can reliably derive the metal abundance
ratios listed in Table 10.

Figure 13 shows the abundance ratios in a diagram of [Si/O] vs. [C/O] for the
six $z>6$ O i absorbers in our sample. Since C ii in two of the six absorbers
is partly contaminated by other absorption lines at other redshifts, and one
is not detected, we provide only upper limits for three [C/O] ratios.

To compare the observed abundance pattern with the chemical enrichment from
various types of stellar explosions, Figure 13 illustrates the predicted
ratios where the combined yields are weighted by the number of stars in a
Salpeter initial mass function (IMF), reflecting the combined enrichment
pattern generated by a stellar population. Pop III pair-instability SNe (PISN)
with initial masses of $140-260$ $M_{\odot}$ are illustrated by red coloured
contours, and occupy a distinct region with a high [Si/O] and low [C/O] ratio
(Heger & Woosley, 2002). Pop III hypernovae (shown in green contours) and Pop
III supernovae (blue contours) with a range of energies and initial masses of
$10-100$ $M_{\odot}$ also display distinct abundance patterns (Heger &
Woosley, 2010). Hypernovae are defined as having explosion energies of
$>10^{52}$ erg, roughly ten times more than that of core-collapse SNe.

Several theoretical works have computed yields from metal-enriched Pop II.
Using the yields table in Nomoto et al. (2013), stars with initial stellar
masses between $10-40$ $M_{\odot}$ and 5% –100% solar metallicity
(illustrated in gray color in Fig. 13) produce an abundance pattern that
overlaps with that of hypernovae. The yields in Kobayashi et al. (2006) gives
more or less identical results. A more recent yields table that includes Pop
II stars with higher initial stellar masses between $13-120$ $M_{\odot}$
(Limongi & Chieffi, 2018) (purple contours) produce an IMF weighted yield with
a higher concentration around [C/O]$\approx$0.1 and [Si/O]$\approx$0.3.

Figure 13 illustrates that also other intervening absorption-line systems at
$4.5<z<6$ (orange circles) (Becker et al., 2019; Cooper et al., 2019) fall in
the same region as the JWST absorption systems. However, several of these
other absorption line systems at $z<5$ have detected Si iv and C iv absorption
lines that display different kinematical profiles and therefore arise in a
different medium than the low-ionization lines (Becker et al., 2019). In such
cases a direct conversion from $W_{r}$ of Si ii and C ii to a total Si and C
column density is not valid. In fact, some of the absorption systems at
$z<4.2$ in Becker et al. (2019) have hydrogen column densities $N$(H
i)$<10^{20}$ cm-2 for which ionization corrections are necessary, and when
these are included, all the absorbers at $3.0\\!<\\!z\\!<\\!4.2$ have
[Si/O]$<\\!0.4$ (Saccardi et al., 2023). For seven absorption systems at
$5<z<6.2$ their [Si/O] ratios without ionization corrections remain close to
the solar value (Becker et al., 2012), suggesting that Pop III enrichment does
not dominate these systems.

In higher H i column density Damped-Ly$\alpha$ systems (DLAs), where the
hydrogen column density is $N$(H i) $>2\times 10^{20}$ cm-2, the clouds are
self shielded, such that the dominant contributions to the metal column
densities are from the low-ionization species. Above metallicities of 1%
solar, C,N,O absorption lines are saturated in DLA systems and only lower
limits on the column densities can be derived (e.g. Berg et al., 2016). The
gray circles in Fig. 13 illustrate that metal-poor DLAs at
$z_{\mathrm{abs}}\sim 3$ with metallicities from $10^{-3}$ to $10^{-2}$
$Z_{\odot}$ in oxygen (Cooke et al., 2011, 2017; Welsh et al., 2019) generally
have abundance patterns similar to solar values and consistent with Pop II SNe
enrichment patterns, albeit with an overlap from the high-mass Pop III
hypernovae enrichment pattern. However, when analysing other elements (Fe and
Al), one of the DLA systems has an enrichment pattern which is better matched
by Pop III SNe (Cooke et al., 2017; Welsh et al., 2023).

For the three absorption systems detected by NIRSpec with limits on the carbon
abundance the enrichment could possibly be created by Pop III PISN. Two of the
other absorbers show abundance ratios with high values of [Si/O] that are
consistent with either enrichment by primarily lower mass ($<15\,M_{\odot}$)
Pop III hypernovae or even Pop III core-collapse SNe for one of them. Models
predict that the abundance ratio $0.5<$[Si/O]$<1.0$ is produced by energetic
Pop III SNe (with explosion energies $>10^{51}$ erg) from stars with initial
stellar masses in the range from $10-15$ $M_{\odot}$ (Heger & Woosley, 2010).
However, also single 15 $M_{\odot}$ star Pop II explosions produce a high
[Si/O] $\approx\\!\leavevmode\nobreak\ 0.5$ ratio (Woosley & Weaver, 1995;
Thielemann et al., 1996; Nomoto et al., 2006, 2013), so the enrichment by a
single Pop II star remains a possibility. We do have to consider that gas
clouds seen in absorption may not only be polluted by a single star alone, but
by several SNe explosions over a range of stellar masses. When integrating the
yields over the IMF, stars with masses in the range from $13-40$ $M_{\odot}$
with initial population metallicities less than solar all produce an
integrated ratio of [Si/O]$\lesssim 0.1$ (Woosley & Weaver, 1995; Kobayashi et
al., 2006; Nomoto et al., 2013).

Table 10: Metal abundance ratios of absorption systems

Quasar | $z_{\mathrm{abs}}$ | [Si/O] | [C/O]
---|---|---|---
ULAS J1342+0928 | 7.3680† | 0.98$\pm$0.10 |  $<$–0.17$\pm$0.11
ULAS J1342+0928 | 7.4430 | 0.43$\pm$0.24 |  0.19$\pm$0.15
VDES J0020–3653 | 6.4535 | 0.15$\pm$0.16 |  –0.13$\pm$0.08
VDES J0020–3653 | 6.5625† | 0.64$\pm$0.11 |  $<$–0.26
VDES J0020–3653 | 6.6690 | 0.49$\pm$0.11 |  –0.02$\pm$0.07
UHS J0439+1634 | 6.2880† | 0.73$\pm$0.06 |  $<$–0.07$\pm$0.06

  * •

†Upper limits for [C/O] are computed because the column densities of carbon
lines are overestimated due to line contamination from other systems, or an
upper detection limit on C ii in the case of the $z=6.5625$ system.

##  6 Transmission spikes in the Gunn-Peterson trough

One of the most debated questions arising from studies of the high-redshift
Universe in the past decades, is what sources reionized the IGM? To further
investigate this, we examine whether the sources associated with metal
absorbers have ionized their surroundings.

We investigate the narrow transmission spikes at $z>5.7$ in the Ly$\alpha$
forest region and in the GP troughs covered by the NIRSpec data using the
normalized quasar spectra bluewards of the quasars Ly$\alpha$ lines described
in Sect. 3.2. To locate narrow transmission spikes we search for peaks with a
3-$\sigma$ prominence above zero, using the normalized spectrum divided by the
normalized noise spectrum, i.e. a spectrum of the $S/N$ ratio. We require that
the peaks extend over three or more pixels to avoid detections from a single
noisy pixel. The identified spikes have relative transmissions of 2–5% or
higher. Figure 14 illustrates the location of the Ly$\alpha$ wavelengths for
the identified metal-absorption line system redshifts plotted on the
normalized spectrum in the GP trough region of the four spectra. The blue and
red colored dashed lines mark high- and low-ionisation absorption systems,
respectively. The green triangles mark the spikes in the IGM where there is a
significant transmission of Ly$\alpha$ photons, and Table 11 lists the
redshifts of the encountered Ly$\alpha$ transmission spikes.

We briefly compare the detected IGM transmission spikes with previously
published results. Yang et al. (2020) examine transmission spikes at $z>5.5$
based on ground-based spectra of a large sample of high-redshift quasars
including two of the quasars in this paper (DELS J0411–0907 and VDES
J0020–3653). They list four spikes in DELS J0411–0907, with three being
multiple peaks at an almost identical redshift of $z\sim 5.7$. We also detect
these four spikes in the NIRSpec data but in addition we also detect spikes at
even higher redshift. For VDES J0020–3653, Yang et al. (2020) do not report
spike redshifts. Due to the higher $S/N$ ratio of the JWST spectra and the
absence of noise due to telluric features, we can detect weaker spikes in the
GP troughs.

Only a single low-ionization absorption system (at $z=5.928$ towards UHS
J0439+1634) out of the total of 13 intervening low-ionization absorption
systems (red dashed lines in Fig. 14) match the transmission spikes in the IGM
to within a velocity offset of 2000 km s-1, whereas four out of nine of the
high-ionization system lie within $\approx$2000 km s-1 of transmission spikes,
all of which at $z\sim 5.8$. Not surprisingly, the two proximate absorption
systems in the spectra of UHS J0439+1634 and VDES J0020–3653 are indeed high-
ionization systems. Conversely, from the total of 21 absorption systems at
$z>5.7$, 16 spikes do not match any metal-absorption system within $\pm$2000
km s-1. The fact that most of the low-ionization systems are not associated
with a transmission spike supports the hypothesis that the metal-enrichment
arises from star-formation followed by heavy element productions by supernova
explosions preferentially in higher density regions that may be the last
regions to be reionized (Oh, 2002). On the other hand, half of the high-
ionization systems at $5.7<z<6.8$ appear in regions of the IGM that are
ionized to a degree that allows the escape of Ly$\alpha$ photons.

Table 11: Redshifts of transmission spikes in the GP troughs Quasar | $z_{\mathrm{spikes}}$
---|---
ULAS J1342+0928 | 5.9214
VDES J0020–3653 | 5.7333, 6.0125
DELS J0411–0907 | 5.7178, 5.8497, 5.9059
| 5.9756, 6.2781, 6.5127
UHS J0439+1634 | 5.70232, 5.7702, 5.8787
| 5.9117, 6.1017
Figure 14: The panels show the relative transmission in the GP trough after
normalizing the spectra to a model of the quasar continuum in the Ly$\alpha$
forest. The individual absorber redshifts are marked where their expected
Ly$\alpha$ wavelength falls. Red dashed lines are absorbers that are only low-
ionization systems, and blue dashed lines mark absorbers with high-ionization
lines from C iv. Two of the absorbers lie close to the quasar redshift, and
their Ly$\alpha$ wavelengths fall in the proximity region of the quasar
spectrum. A single low-ionization absorber at $z=5.928$ is associated with a
transmission spike in the intervening IGM illustrated by the green triangles,
while four of the high-ionization absorbers lie within $\pm$2000 km s-1 from
the IGM transmission spikes at $z\sim 5.8$.

##  7 Discussion

###  7.1 A forest of lines or discrete absorption line systems?

Oh (2002) has suggested that as the Universe becomes progressively more
neutral, a forest of weak metal lines may appear at redshifts close to the
EoR. Analogous to the H i Ly$\alpha$ forest arising from neutral clouds of
hydrogen in the IGM that becomes more numerous towards higher redshifts,
metals in the IGM would also progressively become more neutral. For example an
O i $\lambda$1302 forest could give rise to a depression of the quasar
continuum at the 10-20% level (Oh, 2002). Other atoms such as C ii, or Si ii
have ionization potentials close to that of H i and have oscillator strengths
that also make the lines useful as tracers of the enrichment and the
increasing neutral IGM towards the reionization epoch. Likewise, the Mg ii
$\lambda\lambda$2796,2803 doublet is easily detected, and could give rise to a
continuous Mg ii forest provided the neutral IGM is globally enriched with
metals (Hennawi et al., 2021). However, ground-based spectra of high redshift
quasars have not yet revealed any signature from a Mg ii forest (Tie et al.,
2023). In the NIRSpec data we detect a line density of Mg ii that remains
constant out to $z=7.5$. Mg ii lines are identified as discrete absorption
systems rather than a global decrease in the continuum in the form of a Mg ii
forest. Also in the case of O i, we are only able to detect discrete absorbers
that likely arise in the CGM or ISM of intervening galaxies. Further analysis
of JWST/NIRSpec data using more advanced methods such as auto-correlation
techniques may be able to find a metal forest, but that is beyond the scope of
the analysis of individual absorption systems in this work. Detecting a Mg ii
metal-line forest with a global decrease in the continuum flux at the few
percent level is difficult due to the wiggles in the NIRSpec data described in
Section 2, but also the broad emission-line composite from Mg ii and Fe ii in
the quasar spectrum will make it difficult to accurately determine the level
of the unabsorbed continuum emission.

###  7.2 High-redshift metallicities

In the absence of measurable H i column densities, we can only measure
relative metallicities. In the redshift interval $2<z<6$ absorption systems
display a relatively constant relative abundance pattern in [Si/O] and [C/Fe]
with redshift and do not reveal clear signatures of Pop III SNe enrichment
(Becker et al., 2012; Kulkarni et al., 2014), with a couple of possible
exceptions from DLA systems at $z\approx 3$ (Welsh et al., 2023). A relevant
question is therefore if we have overestimated the [Si/O] or [C/O] ratios for
the $z>6$ absorption systems. In comparison with ground-based spectroscopic
studies, the NIRSpec data have low spectral resolution and could potentially
have hidden saturation, or be composed of multiple components rather than a
single one assumed for the O i systems in this work. We argue against the
possibility of hidden saturation based on the analysis of the curve of growth
for the absorption lines in any of the O i absorbers where we find that they
are consistent with being on the linear, optically thin region of the curve of
growth. In agreement with our finding, numerical simulations at $z\sim 7$ also
find that O i absorption lines along random sight-lines are optically thin
(Doughty & Finlator, 2019).

Alternatively, multiple absorption components in the observed spectra could
have variations in abundance patterns between them, but in order to display a
high [Si/O] ratio, some of the components will inevitably have to have an
extreme value. Ultimately, we need higher spectral resolution measurements to
reveal and decompose the absorption lines in distinct components but this may
have to wait until the arrival of high-resolution spectrographs on future
extremely large telescopes (e.g. Maiolino et al., 2013).

Another possibility leading to inaccurate abundance ratios arises if the atoms
exist predominantly in ionization states other than Si ii or C ii in which
case ionization corrections are needed to derive [Si/O] and [C/O] ratios
accurately. However, we do not regard this as likely for following reasons.
Firstly, the intervening absorption systems are primarily neutral because
there are no high-ionization C iv or Si iv lines associated with the O i
systems. Secondly, the O i absorbers are not associated with a transmission
spike in the GP troughs at these redshifts, and consequently the environment
is predominantly neutral. O i is charge-exchanged locked with H i, so O i is
the dominant state of oxygen. Possibly, Si iii and C iii could be present, but
these atomic states do not have absorption lines in the spectral range covered
by the NIRSpec data. However, if that were the case, then the total [Si/O] and
[C/O] would be even higher than detected in the high-redshift absorbers. For
similar $z\sim 6$ absorption systems, Cooper et al. (2019) argue that the low-
ionization systems are equivalents to $z\sim 3$ DLA systems in which
metallicities derived without ionization corrections are reliable.

###  7.3 Ionization state of the CGM

Our finding that the the number density of the high-ionization ions C iv and
Si iv continues to drop at $z>6.5$ extends the trends observed at slightly
lower redshifts (Cooper et al., 2019; D’Odorico et al., 2022; Davies et al.,
2023b). The disappearing of these absorption lines cannot be attributed to a
change in metallicity, since C ii and Si ii are frequently detected with
column densities that are ten times higher than in the high-ionization states
(Becker et al., 2011; Bosman et al., 2017; Cooper et al., 2019). Moreover, we
find that the Mg ii line density appears to be constant throughout the
redshift range probed out to $z\sim 7.5$, or at least only display a modest
decrease to the highest redshifts covered, $z\sim 7.5$. This supports the
contention that the change in the high-ionization line densities is caused by
a softening of the UV radiation field dominated by star-forming galaxies
rather than by the harder radiation of active galactic nuclei at these
redshifts.

The under-production of C iv at $z>6$ is known to be a problem in cosmological
simulations (Keating et al., 2016; Finlator et al., 2018; Doughty et al.,
2018) although interestingly we find that the number density of C ii absorbers
agrees with observations. We detect only two Si iv absorbers in the JWST
spectra, and confirm that the line density of this transition continues to
decrease with redshift consistent with model predictions. The fact that the
observed line density of Si iv at $z>5.5$ matches the simulations (D’Odorico
et al., 2022), whereas the observed Si ii line density is higher than in
simulations, is not likely due to the hardness of the UV background in the
simulations, because the ratio of these lines does not depend on the spectral
hardness (Doughty et al., 2018). In the epoch where reionization has not yet
been completed, the low-ionization ions will be the dominant ones, and may be
present out to large distances from the galaxies (Doughty & Finlator, 2023).

###  7.4 Environments of the metal absorbers

To date, not many studies have attempted to match metal-absorption lines to
galaxies at $z>6$ primarily due to the lack of instrument sensitivity since
the galaxies are very faint. From ground-based observations, targeting
Ly$\alpha$ emitters has recently been the best option due to the increased
contrast of the emission lines above the background. By studying correlations
of high-ionization C iv systems at $z\sim 5$ with Ly$\alpha$ emitters Díaz et
al. (2021) found emitters within 200 kpc in projection from the absorbers. The
environments of the even higher redshift low-ionization systems are less clear
from observations. Using JWST/NIRcam slitless spectra of one $z=6.3$ quasar
field Kashino et al. (2023) identify galaxies with [O iii] emission lines at
$5.3<z<6.2$ and find that most of the detected galaxy redshifts match either
high- or low-ionizations absorption-line systems in the quasar spectrum.
Conversely, out of the eight intervening absorption line systems, only four of
the systems have detected galaxies within 200 kpc of the quasar sight line
(Kashino et al., 2023), where most of the non-detections are from low-
ionization absorber systems.

In a survey of eight quasars at $z>6$, Meyer et al. (2020) detect a weak
correlation between transmission spikes and $z\approx 6$ Ly$\alpha$-emitters
and Lyman-break galaxies (LBGs) within 1–10 proper Mpc in projection along the
line of sight, but argue that fainter galaxies below the detection threshold
are necessary to drive the reionization. In a similar analysis of a single
quasar at $z=6.4$ Kakiichi et al. (2018) find that three out of six LBGs lie
close in redshift space to the IGM transmission spikes, and also conclude that
fainter galaxies clustered around the brighter LBGs are needed to drive
reionization, but also argue that luminous quasars do contribute to the
ionizing flux at the latest stages of the reionization. In a recent analysis
of JWST NIRcam slitless data, Kashino et al. (2023) measure a clear
correlation between galaxy redshifts at $5.7<z<6.1$ and transmission spikes in
the Ly$\alpha$ forest, but find that metal-absorption systems are not
connected to the transmission spikes. This is consistent with our finding of
no clear association of low-ionization absorbers with IGM transmission peaks.

Whether any of the absorption systems have detectable galaxy counterparts
nearby can be investigated with the Band III JWST integral field spectra. The
3 arcsec field of view of the IFU limits the search to within 7–10 kpc in
projection from the quasar, which is much smaller than the typical size of 100
kpc of the metal-enriched CGM around high-redshift galaxies (Tumlinson et al.,
2017). The sizes and covering fractions of the metal-enriched CGM depend on
the galaxy masses and redshifts, where simulations predict that the median
separation of an O i absorber at $z=7$ to its host galaxy is about 250
$h^{-1}$ comoving kpc or a physical size of about 45 kpc, and that the
absorber may probe either the CGM or the ISM of the host (Doughty & Finlator,
2019). Even though the IFU of NIRSpec has a small field of view, there is a
non-negligible chance of detecting the associated galaxy through emission
lines at the absorber redshift in the IFS data cube, especially if the
strongest absorbers probe the ISM. This association of metal absorption lines
with galaxies will be investigated in forthcoming papers.

##  8 Summary and conclusions

We have presented JWST/NIRSpec spectra of four $z>6.5$ quasars covering the
wide spectral range 0.8–5.3 $\mu$m. Targeting luminous quasars with magnitudes
of $J_{\mathrm{AB}}$=17–20, the data shows an exquisite signal with a $S/N$
ratio of 50–200 per spectral bin allowing for an in-depth analysis of each of
the quasar lines of sight. We detect metal-absorption line systems from
intervening absorbers and systems associated with the quasars. In each of the
four spectra we identify between 13 and 17 absorption line systems with a
total of 61 systems at redshifts ranging from $z=2.4$ to $z=7.5$. Since the
line of sight towards the quasars probe random locations, the identifications
include detections of intervening metal-enriched gas in the CGM or the ISM of
intervening galaxies at the highest redshift to date. Most absorption line
systems are identified in multiple atomic transitions due to the high quality
of the NIRSpec data, while some weaker systems are only detected through Mg ii
or C iv doublets.

At the quasar redshifts, the Ly$\alpha$ transitions of each absorption system
fall in the Gunn-Peterson trough, so deriving hydrogen column densities or
metallicities based on the JWST data is impossible, and likewise is it
impossible to derive the neutral hydrogen gas fractions. Instead, we use low-
ionization lines such as O i as a proxy to examine the changing ionization
state of the IGM (Oh, 2002). Rather than detecting a forest of metal lines in
the IGM at the end of the EoR, we detect instead a number of discrete O i (and
Mg ii) absorbers, as logically expected since winds from galaxies are
responsible for distributing the heavy elements out into the CGM of the host
galaxies.

From the number statistics of the low-ionization absorbers O i $\lambda$1302,
C ii $\lambda$1334, Si ii $\lambda$1526, and Mg ii $\lambda$2796, we derive
the redshift evolution of the line densities beyond $z>6$. Combined with other
studies of lower redshift quasar spectra (Becker et al., 2019; Chen et al.,
2017; Davies et al., 2023a), we find that the line densities of O i, C ii, and
Si ii derived from the NIRSpec data reveal a continued increase beyond $z>6$,
whereas Mg ii shows a redshift evolution that is consistent with being
constant or mildly decreasing with increasing redshift. In all atomic species,
the number of detected absorbers remains small and the redshift path-length
interval is also limited in our NIRSpec data set of just four quasars.
Consequently, the Poisson uncertainties are large and the line density
evolution could also be constant to within $\sim 1\sigma$ uncertainties for
all atomic species. Nevertheless, compared to numerical simulations, the
observations follow the predicted redshift dependence for the low-ionization
lines at $z=5\rightarrow 8$, where the model predicts an increase with
increasing redshift to $z\sim 7$ due to the increased neutral content and a
decreasing UV ionizing background flux. At $z>7$ the model predicts that line
densities decrease with redshift as a consequence of the decreasing metal
enrichment of the CGM.

For the high-ionization transitions C iv and Si iv we derive a decrease in
line density beyond $z>6$ following the previously reported rapid decrease
seen for these lines from $z=4\rightarrow 6$ (D’Odorico et al., 2022; Davies
et al., 2023b). Again, the drop is consistent with the model prediction. The
transitions in line densities from a predominantly low-ionization level to a
high-ionization level occurs at $z\approx 6.0$ for both carbon and silicon.
The observations therefore suggest that there is a continuous change in the
ionization state rather than a drastic change in the distribution of metals at
$z>6$.

We matched the redshifts of the absorption line systems at $z>5.7$ with spikes
of transmission of Ly$\alpha$ photons in the GP troughs, and find that half of
the intervening absorbers that have high-ionization absorption lines fall
within $\pm$2000 km s-1 of the Ly$\alpha$ redshift of a matching transmission
spike. For low-ionization absorbers on the other hand, only a single system
out of 13 intervening absorbers falls within $\pm$2000 km s-1 of a spike. This
demonstrates that low-ionization systems are found in environments that are
not ionized sufficiently to allow Ly$\alpha$ photons to escape. Low-ionization
metal-absorption line systems therefore likely trace higher density
environments in the Universe that are among the last ones to become reionized.

While we are not able to derive absolute metallicities, our relative
metallicity ratios are accurate provided that the absorption lines are not
saturated, and that ionization corrections are insignificant. We argue that
the systems traced through O i absorption lines are reliable tracers, since in
none of the cases do we detect high-ionization lines and they are not
associated with IGM transmission spikes. Focusing on silicon, carbon and
oxygen atoms, we derive high values of the metal line ratio [Si/O] in the
range from 0.5–1.0 in five of the $z>6$ absorption systems. Such high values
of [Si/O] are consistent with the enrichment pattern expected from energetic
Pop III hypernovae with initial stellar masses from $10-15$ $M_{\odot}$, or
even from Pop III core-collapse SNe (Heger & Woosley, 2010). However,
explosions of single Pop II stars with a stellar mass of 15 $M_{\odot}$ can
also produce a high [Si/O] ratio (Woosley & Weaver, 1995; Kobayashi et al.,
2006; Nomoto et al., 2013), so this scenario cannot be ruled out if the
absorption system happen to probe a gas cloud enriched by a single star alone.
Absorbers with the highest [Si/O] ratios and upper limits on [C/O] could be
enriched by Pop III pair instability supernovae. The high [Si/O] ratio and
limit on the carbon abundance cannot be explained by the yields produced by
Pop II SNe. We therefore suggest that the CGM of the neutral absorption
systems at $z>6$ are dominated by the enrichment from Pop III explosions.

In the near future, surveys from LSST and Euclid will detect an increasing
number of (fainter) quasars at $z>7$. Also with the increasing depth and
completed wide-field surveys with JWST, new detections of quasars at $z>10$
are being made (Goulding et al., 2023; Maiolino et al., 2023). Follow-up near-
IR spectroscopic observations with NIRSpec and future ELT spectrographs will
allow to extend this work to even higher redshifts into the first half of the
reionization epoch beyond $z>7.5$. Higher resolution spectra compared to the
$R\\!\simeq\\!2700$ NIRSpec observations are needed to fit the absorption line
profiles with conventional Voigt fitting techniques to verify metal column
densities, and with an increasing number of high redshift quasar spectra the
larger path length interval will provide line densities with smaller Poisson
uncertainties. Future observations of the fields surrounding the four quasars
presented in this paper may be able to identify the galaxies responsible for
the intervening absorption line systems and single out the galaxies that are
possibly responsible for the Pop III metal-enrichment at $z>6$.

###### Acknowledgements.

We thank Kristian Finlator for useful discussions, and Ezra Huscher for
sharing results from their simulations. We also thank Stefania Salvadori for
valuable insights into Pop III enrichment. L.C. is supported by
DFF/Independent Research Fund Denmark, grant-ID 2032–00071. The Cosmic Dawn
Center is funded by the Danish National Research Foundation under grant no.
140. AJB has received funding from the European Research Council (ERC) under
the European Union’s Horizon 2020 Advanced Grant 789056 “First Galaxies” MP
acknowledges support from the research project PID2021-127718NB-I00 of the
Spanish Ministry of Science and Innovation/State Agency of Research
(MICIN/AEI), and the Programa Atracción de Talento de la Comunidad de Madrid
via grant 2018-T2/TIC-11715. HÜ gratefully acknowledges support by the Isaac
Newton Trust and by the Kavli Foundation through a Newton-Kavli Junior
Fellowship. The work is based on data obtained by the European Southern
Observatory, Paranal, Chile, Program IDs: 098.B-0537, 0100.A-0446 and
0100.A-0898. This work is based on observations made with the NASA/ESA/CSA
James Webb Space Telescope. The data were obtained from the Mikulski Archive
for Space Telescopes at the Space Telescope Science Institute, which is
operated by the Association of Universities for Research in Astronomy, Inc.,
under NASA contract NAS 5-03127 for JWST. These observations are associated
with GTO programs 1219 and 1222.

## References

  * Andika et al. (2022) Andika, I. T., Jahnke, K., Bañados, E., et al. 2022, AJ, 163, 251
  * Asplund et al. (2021) Asplund, M., Amarsi, A. M., & Grevesse, N. 2021, A&A, 653, A141
  * Bañados et al. (2019) Bañados, E., Rauch, M., Decarli, R., et al. 2019, ApJ, 885, 59
  * Bañados et al. (2023) Bañados, E., Schindler, J.-T., Venemans, B. P., et al. 2023, ApJS, 265, 29
  * Bañados et al. (2018) Bañados, E., Venemans, B. P., Mazzucchelli, C., et al. 2018, Nature, 553, 473
  * Bahcall & Peebles (1969) Bahcall, J. N. & Peebles, P. J. E. 1969, ApJ, 156, L7
  * Becker et al. (2015) Becker, G. D., Bolton, J. S., & Lidz, A. 2015, PASA, 32, e045
  * Becker et al. (2021) Becker, G. D., D’Aloisio, A., Christenson, H. M., et al. 2021, MNRAS, 508, 1853
  * Becker et al. (2019) Becker, G. D., Pettini, M., Rafelski, M., et al. 2019, ApJ, 883, 163
  * Becker et al. (2011) Becker, G. D., Sargent, W. L. W., Rauch, M., & Calverley, A. P. 2011, ApJ, 735, 93
  * Becker et al. (2012) Becker, G. D., Sargent, W. L. W., Rauch, M., & Carswell, R. F. 2012, ApJ, 744, 91
  * Berg et al. (2016) Berg, T. A. M., Ellison, S. L., Sánchez-Ramírez, R., et al. 2016, MNRAS, 463, 3021
  * Bergeron & Boissé (1991) Bergeron, J. & Boissé, P. 1991, A&A, 243, 344
  * Böker et al. (2022) Böker, T., Arribas, S., Lützgendorf, N., et al. 2022, A&A, 661, A82
  * Bosman et al. (2017) Bosman, S. E. I., Becker, G. D., Haehnelt, M. G., et al. 2017, MNRAS, 470, 1919
  * Bunker et al. (2023) Bunker, A. J., Cameron, A. J., Curtis-Lake, E., et al. 2023, arXiv e-prints, arXiv:2306.02467
  * Chen et al. (2017) Chen, S.-F. S., Simcoe, R. A., Torrey, P., et al. 2017, ApJ, 850, 188
  * Christensen et al. (2011) Christensen, L., Fynbo, J. P. U., Prochaska, J. X., et al. 2011, ApJ, 727, 73
  * Christensen et al. (2017) Christensen, L., Vergani, S. D., Schulze, S., et al. 2017, A&A, 608, A84
  * Churchill et al. (1999) Churchill, C. W., Rigby, J. R., Charlton, J. C., & Vogt, S. S. 1999, ApJS, 120, 51
  * Codoreanu et al. (2018) Codoreanu, A., Ryan-Weber, E. V., García, L. Á., et al. 2018, MNRAS, 481, 4940
  * Cooke et al. (2011) Cooke, R., Pettini, M., Steidel, C. C., Rudie, G. C., & Nissen, P. E. 2011, MNRAS, 417, 1534
  * Cooke et al. (2017) Cooke, R. J., Pettini, M., & Steidel, C. C. 2017, MNRAS, 467, 802
  * Cooper et al. (2019) Cooper, T. J., Simcoe, R. A., Cooksey, K. L., et al. 2019, ApJ, 882, 77
  * Cowie et al. (1995) Cowie, L. L., Songaila, A., Kim, T.-S., & Hu, E. M. 1995, AJ, 109, 1522
  * Cupani et al. (2022) Cupani, G., D’Odorico, V., Cristiani, S., et al. 2022, in Astronomical Society of the Pacific Conference Series, Vol. 532, Astronomical Society of the Pacific Conference Series, ed. J. E. Ruiz, F. Pierfedereci, & P. Teuben, 207
  * Curtis-Lake et al. (2023) Curtis-Lake, E., Carniani, S., Cameron, A., et al. 2023, Nature Astronomy, 7, 622
  * Davies et al. (2018) Davies, F. B., Hennawi, J. F., Bañados, E., et al. 2018, ApJ, 864, 142
  * Davies et al. (2020) Davies, F. B., Wang, F., Eilers, A.-C., & Hennawi, J. F. 2020, ApJ, 904, L32
  * Davies et al. (2023a) Davies, R. L., Ryan-Weber, E., D’Odorico, V., et al. 2023a, MNRAS, 521, 289
  * Davies et al. (2023b) Davies, R. L., Ryan-Weber, E., D’Odorico, V., et al. 2023b, MNRAS, 521, 314
  * Díaz et al. (2021) Díaz, C. G., Ryan-Weber, E. V., Karman, W., et al. 2021, MNRAS, 502, 2645
  * D’Odorico et al. (2023) D’Odorico, V., Bañados, E., Becker, G. D., et al. 2023, MNRAS, 523, 1399
  * D’Odorico et al. (2022) D’Odorico, V., Finlator, K., Cristiani, S., et al. 2022, MNRAS, 512, 2389
  * Doughty & Finlator (2019) Doughty, C. & Finlator, K. 2019, MNRAS, 489, 2755
  * Doughty et al. (2018) Doughty, C., Finlator, K., Oppenheimer, B. D., Davé, R., & Zackrisson, E. 2018, MNRAS, 475, 4717
  * Doughty & Finlator (2023) Doughty, C. C. & Finlator, K. M. 2023, MNRAS, 518, 4159
  * Ellison et al. (2010) Ellison, S. L., Prochaska, J. X., Hennawi, J., et al. 2010, MNRAS, 406, 1435
  * Ellison et al. (2000) Ellison, S. L., Songaila, A., Schaye, J., & Pettini, M. 2000, AJ, 120, 1175
  * Fan et al. (2023) Fan, X., Bañados, E., & Simcoe, R. A. 2023, Annual Review of Astronomy and Astrophysics, 61, null
  * Fan et al. (2001) Fan, X., Narayanan, V. K., Lupton, R. H., et al. 2001, AJ, 122, 2833
  * Fan et al. (2019) Fan, X., Wang, F., Yang, J., et al. 2019, ApJ, 870, L11
  * Fang et al. (2023) Fang, H., Xia, I., Ge, J., Willis, K., & Zhao, Y. 2023, MNRAS, 518, 5590
  * Finlator et al. (2020) Finlator, K., Doughty, C., Cai, Z., & Díaz, G. 2020, MNRAS, 493, 3223
  * Finlator et al. (2018) Finlator, K., Keating, L., Oppenheimer, B. D., Davé, R., & Zackrisson, E. 2018, MNRAS, 480, 2628
  * Frebel & Norris (2015) Frebel, A. & Norris, J. E. 2015, ARA&A, 53, 631
  * Gardner et al. (2023) Gardner, J. P., Mather, J. C., Abbott, R., et al. 2023, PASP, 135, 068001
  * Gehrels (1986) Gehrels, N. 1986, ApJ, 303, 336
  * Goulding et al. (2023) Goulding, A. D., Greene, J. E., Setton, D. J., et al. 2023, arXiv e-prints, arXiv:2308.02750
  * Gunn & Peterson (1965) Gunn, J. E. & Peterson, B. A. 1965, ApJ, 142, 1633
  * Guo et al. (2018) Guo, H., Shen, Y., & Wang, S. 2018, PyQSOFit: Python code to fit the spectrum of quasars, Astrophysics Source Code Library
  * Heckman et al. (2000) Heckman, T. M., Lehnert, M. D., Strickland, D. K., & Armus, L. 2000, ApJS, 129, 493
  * Heger & Woosley (2002) Heger, A. & Woosley, S. E. 2002, ApJ, 567, 532
  * Heger & Woosley (2010) Heger, A. & Woosley, S. E. 2010, ApJ, 724, 341
  * Hennawi et al. (2021) Hennawi, J. F., Davies, F. B., Wang, F., & Oñorbe, J. 2021, MNRAS, 506, 2963
  * Inayoshi et al. (2020) Inayoshi, K., Visbal, E., & Haiman, Z. 2020, ARA&A, 58, 27
  * Jakobsen et al. (2022) Jakobsen, P., Ferruit, P., Alves de Oliveira, C., et al. 2022, A&A, 661, A80
  * Jiang et al. (2022) Jiang, L., Ning, Y., Fan, X., et al. 2022, Nature Astronomy, 6, 850
  * Kakiichi et al. (2018) Kakiichi, K., Ellis, R. S., Laporte, N., et al. 2018, MNRAS, 479, 43
  * Kashino et al. (2023) Kashino, D., Lilly, S. J., Matthee, J., et al. 2023, ApJ, 950, 66
  * Keating et al. (2016) Keating, L. C., Puchwein, E., Haehnelt, M. G., Bird, S., & Bolton, J. S. 2016, MNRAS, 461, 606
  * Kobayashi et al. (2006) Kobayashi, C., Umeda, H., Nomoto, K., Tominaga, N., & Ohkubo, T. 2006, ApJ, 653, 1145
  * Krogager (2018) Krogager, J.-K. 2018, arXiv e-prints, arXiv:1803.01187
  * Kulkarni et al. (2014) Kulkarni, G., Hennawi, J. F., Rollinde, E., & Vangioni, E. 2014, ApJ, 787, 64
  * Limongi & Chieffi (2018) Limongi, M. & Chieffi, A. 2018, ApJS, 237, 13
  * Maiolino et al. (2013) Maiolino, R., Haehnelt, M., Murphy, M. T., et al. 2013, arXiv e-prints, arXiv:1310.3163
  * Maiolino et al. (2023) Maiolino, R., Scholtz, J., Curtis-Lake, E., et al. 2023, arXiv e-prints, arXiv:2308.01230
  * Marshall et al. (2023) Marshall, M. A., Perna, M., Willott, C. J., et al. 2023, arXiv e-prints, arXiv:2302.04795
  * Matejek & Simcoe (2012) Matejek, M. S. & Simcoe, R. A. 2012, ApJ, 761, 112
  * Matsuoka et al. (2022) Matsuoka, Y., Iwasawa, K., Onoue, M., et al. 2022, ApJS, 259, 18
  * Matsuoka et al. (2019a) Matsuoka, Y., Iwasawa, K., Onoue, M., et al. 2019a, ApJ, 883, 183
  * Matsuoka et al. (2019b) Matsuoka, Y., Onoue, M., Kashikawa, N., et al. 2019b, ApJ, 872, L2
  * Ménard et al. (2011) Ménard, B., Wild, V., Nestor, D., et al. 2011, MNRAS, 417, 801
  * Meyer et al. (2020) Meyer, R. A., Kakiichi, K., Bosman, S. E. I., et al. 2020, MNRAS, 494, 1560
  * Miralda-Escudé (1998) Miralda-Escudé, J. 1998, ApJ, 501, 15
  * Mortlock et al. (2011) Mortlock, D. J., Warren, S. J., Venemans, B. P., et al. 2011, Nature, 474, 616
  * Nomoto et al. (2013) Nomoto, K., Kobayashi, C., & Tominaga, N. 2013, ARA&A, 51, 457
  * Nomoto et al. (2006) Nomoto, K., Tominaga, N., Umeda, H., Kobayashi, C., & Maeda, K. 2006, Nucl. Phys. A, 777, 424
  * Oh (2002) Oh, S. P. 2002, MNRAS, 336, 1021
  * Peeples et al. (2019) Peeples, M. S., Corlies, L., Tumlinson, J., et al. 2019, ApJ, 873, 129
  * Perna et al. (2023) Perna, M., Arribas, S., Marshall, M., et al. 2023, arXiv e-prints, arXiv:2304.06756
  * Piskunov et al. (1995) Piskunov, N. E., Kupka, F., Ryabchikova, T. A., Weiss, W. W., & Jeffery, C. S. 1995, A&AS, 112, 525
  * Planck Collaboration et al. (2020) Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6
  * Pons et al. (2019) Pons, E., McMahon, R. G., Simcoe, R. A., et al. 2019, MNRAS, 484, 5142
  * Prochaska et al. (2003) Prochaska, J. X., Gawiser, E., Wolfe, A. M., Cooke, J., & Gelino, D. 2003, ApJS, 147, 227
  * Prochter et al. (2006) Prochter, G. E., Prochaska, J. X., & Burles, S. M. 2006, ApJ, 639, 766
  * Rauscher et al. (2017) Rauscher, B. J., Arendt, R. G., Fixsen, D. J., et al. 2017, PASP, 129, 105003
  * Reed et al. (2019) Reed, S. L., Banerji, M., Becker, G. D., et al. 2019, MNRAS, 487, 1874
  * Rigby et al. (2023) Rigby, J., Perrin, M., McElwain, M., et al. 2023, PASP, 135, 048001
  * Ryabchikova et al. (2015) Ryabchikova, T., Piskunov, N., Kurucz, R. L., et al. 2015, Phys. Scr, 90, 054005
  * Saccardi et al. (2023) Saccardi, A., Salvadori, S., D’Odorico, V., et al. 2023, ApJ, 948, 35
  * Simcoe et al. (2020) Simcoe, R. A., Onoue, M., Eilers, A.-C., et al. 2020, arXiv e-prints, arXiv:2011.10582
  * Smith et al. (2007) Smith, J. D. T., Armus, L., Dale, D. A., et al. 2007, PASP, 119, 1133
  * Spitzer (1978) Spitzer, L. 1978, Physical processes in the interstellar medium (John Wiley & Sons)
  * Thielemann et al. (1996) Thielemann, F.-K., Nomoto, K., & Hashimoto, M.-A. 1996, ApJ, 460, 408
  * Tie et al. (2023) Tie, S. S., Hennawi, J. F., Wang, F., et al. 2023, arXiv e-prints, arXiv:2308.11888
  * Tumlinson et al. (2017) Tumlinson, J., Peeples, M. S., & Werk, J. K. 2017, ARA&A, 55, 389
  * Venemans et al. (2017) Venemans, B. P., Walter, F., Decarli, R., et al. 2017, ApJ, 851, L8
  * Wang et al. (2021) Wang, F., Yang, J., Fan, X., et al. 2021, ApJ, 907, L1
  * Wang et al. (2019) Wang, F., Yang, J., Fan, X., et al. 2019, ApJ, 884, 30
  * Wang et al. (2018) Wang, F., Yang, J., Fan, X., et al. 2018, ApJ, 869, L9
  * Welsh et al. (2019) Welsh, L., Cooke, R., & Fumagalli, M. 2019, MNRAS, 487, 3363
  * Welsh et al. (2023) Welsh, L., Cooke, R., Fumagalli, M., & Pettini, M. 2023, arXiv e-prints, arXiv:2307.03771
  * Wild et al. (2006) Wild, V., Hewett, P. C., & Pettini, M. 2006, MNRAS, 367, 211
  * Willott et al. (2010) Willott, C. J., Delorme, P., Reylé, C., et al. 2010, AJ, 139, 906
  * Woosley & Weaver (1995) Woosley, S. E. & Weaver, T. A. 1995, ApJS, 101, 181
  * Yang et al. (2019) Yang, J., Venemans, B., Wang, F., et al. 2019, ApJ, 880, 153
  * Yang et al. (2021) Yang, J., Wang, F., Fan, X., et al. 2021, ApJ, 923, 262
  * Yang et al. (2020) Yang, J., Wang, F., Fan, X., et al. 2020, ApJ, 904, 26
  * Zou et al. (2021) Zou, S., Jiang, L., Shen, Y., et al. 2021, ApJ, 906, 32

##  Appendix A Normalised spectra and absorption line systems

For visualisation purposes the normalized Fixed Slit spectra of the quasars
are shown here, with all the different intervening and proximate absorption
line systems overlaid. Absorption lines are color coded to mark lines
belonging to the same redshift absorbers. Figures with zoom in on individually
detected absorption lines for each system are presented in Appendix B. Note
that the Ly$\alpha$ forest is ignored, and we do not include the Band III data
from 3-5.3 $\mu$m, because only a very few absorption lines are detected at
these wavelengths.

Figure 15: Normalized spectrum of VDES J0020–3653. Absorption line systems are
identified via multiple absorption lines at the same redshifts. Here, 16
different absorption systems are detected at $z$ = 3.4087, 3.6040, 3.6265,
3.7930, 4.0738, 4.2510, 5.2005, 5.3277, 5.4695, 5.6540, 5.7910, 6.4535,
6.5028, 6.5625, 6.669, and 6.855. Each absorption system is specified with a
distinct colour. The absorber at the highest redshift lies at the quasar
redshift.  Figure 16: Normalized spectrum of DELS J0411–0907. Absorption line
systems are identified via multiple absorption lines at the same redshifts.
Here, 13 different absorption systems are detected at $z$ = 2.52, 2.5771,
2.969, 3.156, 3.3943, 3.4290, 3.7760, 4.2498, 4.2815, 5.1935, 5.4258, 5.935,
and 6.1774.  Figure 17: Full spectrum of UHS J0439+1634. The normalization of
the quasar continuum emission is not accurate around 10,000–11,000 Å, where
the quasar spectrum has strong BAL features. Absorption line systems are
identified via multiple absorption lines at the same redshifts. Here, 17
different absorption systems are detected at $z$ = 2.4161, 3.170, 3.4081,
4.3445, 4.523, 5.1728, 5.274, 5.3945, 5.5228, 5.736, 5.819, 5.928, 6.028,
6.173, 6.284, 6.288, and 6.4877. The highest redshift absorber is at the
quasar redshift. Figure 18: Full spectrum of ULAS J1342+0928. Absorption line
systems are identified via multiple absorption lines at the same redshifts.
Here, 15 different absorption systems are detected at $z$ = 2.9145, 3.3758,
3.430, 3.593, 3.631, 3.6735, 5.6814, 5.8888, 6.1234, 6.271, 6.749, 6.8427,
7.368, 7.443, and 7.476. The highest redshift absorber is associated with the
quasar, being offset by $\approx$2000 km s-1 from the quasar systemic
redshift.

##  Appendix B Individual absorption systems

The appendix presents each of the detected absorption line systems. In each of
the quasar spectra we have identified between 13–17 intervening absorption
line systems at different redshifts, ranging from lower redshifts ($z=2.4$) to
absorbers located at the quasar redshifts.

In the figures in this appendix all panels illustrate the normalised quasar
spectrum around the detected absorption line species within $\pm$600 km s-1
for each of the absorption systems. Flux errors per spectral pixel are
overlaid, and the y-axis of each absorption line is scaled to match the depth
of the line. Equivalent widths in the observed frame are computed by
integrating the lines from within $\pm$200 km s-1, while for the stronger
systems that display broader lines, the lines are integrated within $\pm$300
km s-1. The reported wavelengths of the absorption lines in all tables are
computed from weighted mean flux values rather than based on absorption line
fits. Line profiles for narrow line systems for a single absorption system may
appear visually different as explained in Fig. 6 in Section 2. Some lines are
blended or partially contaminated with other identified lines from other
absorption systems. We describe in the comments to the tables which lines are
blended with a line from another system. Some weak lines or lines towards
longer wavelengths are dominated by errors from the complex normalization of
the quasar spectrum.

Figure 19: Absorption system at $z=3.4087$ toward J0020–3653. Note that the
y-axis range changes according to the strengths of the absorption lines in
this and all other panels. Table 12: J0020–3653 $z=3.4087$

$\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
10334.94 | Fe ii 2344.21 | 2.821$\pm$0.119
10468.29 | Fe ii 2374.46 | 1.916$\pm$0.098†
10504.90 | Fe ii 2382.76 | 6.216$\pm$0.104‡
11403.76 | Fe ii 2586.65 | 2.574$\pm$0.104
11463.38 | Fe ii 2600.17 | 7.266$\pm$0.095
12328.28 | Mg ii 2796.35 | 12.011$\pm$0.081
12359.93 | Mg ii 2803.53 | 11.204$\pm$0.081
12577.86 | Mg i 2852.96 | 0.700$\pm$0.107

  * •

† Line blended with Cr ii $\lambda$2062 at $z=4.0738$

  * •

‡ Line partly blended with C iv $\lambda$1548 at $z=5.791$

Figure 20: Absorption system at $z=3.604$ toward J0020–3653.  Table 13: J0020–3653 $z=3.604$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
12874.40 | Mg ii 2796.35 | 6.396$\pm$0.095
12907.46 | Mg ii 2803.53 | 4.372$\pm$0.105
Figure 21: Absorption system at $z=3.6265$ toward J0020–3653.  Table 14: J0020–3653 $z=3.6265$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
12937.32 | Mg ii 2796.35 | 0.872$\pm$0.098
12970.54 | Mg ii 2803.53 | 0.320$\pm$0.102
Figure 22: Absorption system at $z=3.793$ toward J0020–3653.  Table 15: J0020–3653 $z=3.793$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
13402.92 | Mg ii 2796.35 | 1.977$\pm$0.070
13437.32 | Mg ii 2803.53 | 0.770$\pm$0.089
13674.26 | Mg i 2852.96 | 0.190$\pm$0.125

Figure 23: Absorption system at $z=4.0738$ toward J0020–3653.  Table 16:
J0020–3653 $z=4.0738$

$\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
10433.02 | Cr ii 2056.25 | 0.128$\pm$0.093
10463.36 | Cr ii 2062.23 | 1.813$\pm$0.085†
11415.42 | Fe ii 2249.88 | 0.614$\pm$0.093
11894.07 | Fe ii 2344.21 | 5.535$\pm$0.073
12047.54 | Fe ii 2374.46 | 4.390$\pm$0.053
12089.67 | Fe ii 2382.76 | 6.750$\pm$0.052
13124.14 | Fe ii 2586.65 | 5.113$\pm$0.140
13192.76 | Fe ii 2600.17 | 6.237$\pm$0.135
13074.56 | Mn ii 2576.88 | 0.265$\pm$0.157
13163.97 | Mn ii 2594.50 | 0.289$\pm$0.156
13224.65 | Mn ii 2606.46 | 0.866$\pm$0.163
14188.13 | Mg ii 2796.35 | 10.553$\pm$0.095
14224.56 | Mg ii 2803.53 | 10.212$\pm$0.097
14475.37 | Mg i 2852.96 | 0.842$\pm$0.118
17173.49 | Ti ii 3384.74 | 0.718$\pm$0.150

  * •

† Line blended with Fe ii $\lambda$2374 at $z=3.4087$

Figure 24: Absorption system at $z=4.251$ toward J0020–3653. Table 17: J0020–3653 $z=4.251$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
14683.64 | Mg ii 2796.35 | 2.392$\pm$0.098
14721.34 | Mg ii 2803.53 | 1.630$\pm$0.096
Figure 25: Absorption system at $z=5.2005$ toward J0020–3653. Table 18: J0020–3653 $z=5.2005$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
9599.58 | C iv 1548.19 | 0.274$\pm$0.052
9615.55 | C iv 1550.77 | 0.168$\pm$0.053
Figure 26: Absorption system at $z=5.3277$ toward J0020–3653.  Table 19: J0020–3653 $z=5.3277$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
9796.51 | C iv 1548.19 | 1.075$\pm$0.053
9812.81 | C iv 1550.77 | 0.396$\pm$0.056
Figure 27: Absorption system at $z=5.4695$ toward J0020–3653.  Table 20: J0020–3653 $z=5.4695$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
9877.03 | Si ii 1526.71 | 0.181$\pm$0.069
10016.05 | C iv 1548.19 | 1.477$\pm$0.084
10032.71 | C iv 1550.77 | 0.697$\pm$0.087
10809.16 | Al ii 1670.79 | 0.499$\pm$0.079
15165.89 | Fe ii 2344.21 | 0.342$\pm$0.118
15415.30 | Fe ii 2382.76 | 0.469$\pm$0.136
16734.33 | Fe ii 2586.65 | 0.774$\pm$0.145
16821.82 | Fe ii 2600.17 | 0.588$\pm$0.152
18091.00 | Mg ii 2796.35 | 2.264$\pm$0.135
18137.44 | Mg ii 2803.53 | 1.734$\pm$0.164

Figure 28: Absorption system at $z=5.654$ toward J0020–3653.  Table 21: J0020–3653 $z=5.654$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
10301.69 | C iv 1548.19 | 0.805$\pm$0.093
10318.82 | C iv 1550.77 | 0.706$\pm$0.102
Figure 29: Absorption system at $z=5.791$ toward J0020–3653.  Table 22:
J0020–3653 $z=5.791$

$\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
10367.86 | Si ii 1526.71 | 0.501$\pm$0.093
10513.79 | C iv 1548.19 | 2.426$\pm$0.095†
10531.28 | C iv 1550.77 | 0.936$\pm$0.097
15919.56 | Fe ii 2344.21 | 0.317$\pm$0.132
16181.36 | Fe ii 2382.76 | 0.405$\pm$0.131
17657.77 | Fe ii 2600.17 | 1.029$\pm$0.149
18990.03 | Mg ii 2796.35 | 1.684$\pm$0.168
19038.78 | Mg ii 2803.53 | 1.926$\pm$0.145

  * •

† Line partly blended with Fe ii $\lambda$2382 at $z=3.4087$

Figure 30: Absorption system at $z=6.4535$ toward J0020–3653.  Table 23: J0020–3653, $z=6.4535$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
9705.71 | O i 1302.17 | 0.680$\pm$0.050
9946.94 | C ii 1334.53 | 0.768$\pm$0.077
11379.31 | Si ii 1526.71 | 0.212$\pm$0.089
17472.60 | Fe ii 2344.21 | 0.083$\pm$0.159
17698.05 | Fe ii 2374.46 | 0.094$\pm$0.148
17759.94 | Fe ii 2382.76 | 0.406$\pm$0.159
19279.60 | Fe ii 2586.65 | 0.741$\pm$0.187
19380.39 | Fe ii 2600.17 | 0.139$\pm$0.094
20842.61 | Mg ii 2796.35 | 2.301$\pm$0.175
20896.12 | Mg ii 2803.53 | 1.956$\pm$0.170
Figure 31: Absorption system at $z=6.5028$ toward J0020–3653.  Table 24: J0020–3653, $z=6.5028$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
11615.80 | C iv 1548.19 | 0.524$\pm$0.096
11635.12 | C iv 1550.77 | 0.114$\pm$0.092
17877.41 | Fe ii 2382.76 | 0.383$\pm$0.150
20980.47 | Mg ii 2796.35 | 1.642$\pm$0.167
21034.33 | Mg ii 2803.53 | 1.086$\pm$0.175
Figure 32: Absorption system at $z=6.5625$ toward J0020–3653.  Table 25:
J0020–3653, $z=6.5625$

$\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
9847.65 | O i 1302.17 | 0.472$\pm$0.059
11545.72 | Si ii 1526.71 | 0.445$\pm$0.091
10092.39 | C ii 1334.53 | ¡ 0.4†
21147.41 | Mg ii 2796.35 | 1.633$\pm$0.185
21201.70 | Mg ii 2803.53 | 1.553$\pm$0.175

  * •

†C ii is not clearly detected in this system. A 3$\sigma$ upper limit is
listed because the limit is used in Section 5.

Figure 33: Absorption system at $z=6.669$ toward J0020–3653 Table 26:
J0020–3653, $z=6.669$

$\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
9668.71 | Si ii 1260.75 | 0.964$\pm$0.055
9986.33 | O i 1302.17 | 0.884$\pm$0.074
10003.22 | Si ii 1304.37 | 0.368$\pm$0.081
10234.53 | C ii 1334.53 | 1.292$\pm$0.078
10243.54 | C ii* 1335.71 | 0.447$\pm$0.073
11708.31 | Si ii 1526.71 | 0.707$\pm$0.078
12813.27 | Al ii 1670.79 | 0.318$\pm$0.090
17977.78† | Fe ii 2344.21 | 0.947$\pm$0.171
18209.74 | Fe ii 2374.46 | 0.139$\pm$0.175
18273.42 | Fe ii 2382.76 | 0.362$\pm$0.180
19837.02 | Fe ii 2586.65 | 0.469$\pm$0.137
19940.73 | Fe ii 2600.17 | 0.595$\pm$0.128
21445.22 | Mg ii 2796.35 | 1.857$\pm$0.167
21500.28 | Mg ii 2803.53 | 1.729$\pm$0.157

  * •

† Line blended with unidentified feature.

Figure 34: Absorption system at $z=6.855$ toward J0020–3653 Table 27: J0020–3653, $z=6.855$ $\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
9730.94 | N v 1238.82 | 0.638$\pm$0.056
9762.23 | N v 1242.80 | 0.202$\pm$0.058
12161.07 | C iv 1548.19 | 1.582$\pm$0.058
12181.30 | C iv 1550.77 | 1.176$\pm$0.063

Figure 35: Absorption system at $z=2.52$ toward J0411–0907. Note that the
y-axis range changes according to the strengths of the absorption lines in
this and all other panels. Table 28: J0411–0907, $z=2.52$

$\lambda_{\mathrm{obs}}$ | ID | $W_{\mathrm{obs}}$
---|---|---
9843.16 | Mg ii 2796.35 | 1.315$\pm$0.067
9868.43† | Mg ii 2803.53 | 0.967$\pm$0.072
10042.43 | Mg i 2852.96 | 0.216$\pm$0.084