2010 Vol. 10 No. XX, 000–000

11institutetext: LPTA, Université Montpellier 2 - CNRS/IN2P3, 34095
Montpellier, France 22institutetext: Department of Astronomy, Nanjing
University, Nanjing 210093, P. R. China 33institutetext: European Southern
Observatory, Alonso de Córdova 3107, Santiago, Chile 44institutetext:
Department of Astronomy and Astrophysics, P. Universidad Catolica de Chile,
Casilla 306, Santiago, Chile
Received [year] [month] [day]; accepted [year] [month] [day]

# Low-ionization galaxies and evolution in a pilot survey up to z = 1
00footnotetext: Based on observations obtained in service mode at the European
Southern Observatory at Paranal

E. Giraud 11 Q.-S. Gu 22 J. Melnick 33 H. Quintana 44 F. Selman 33 I. Toledo
44 P. Zelaya 44

###### Abstract

We present galaxy spectroscopic data on a pencil beam of $10.75^{\prime}\times
7.5^{\prime}$ centered on the X-ray cluster RXJ0054.0-2823 at $z=0.29$. We
study the spectral evolution of galaxies from $z=1$ down to the cluster
redshift in a magnitude-limited sample at $\rm R\leq 23$, for which the
statistical properties of the sample are well understood. We divide emission-
line galaxies in star-forming galaxies, LINERs, and Seyferts by using
emission-line ratios of [OII], $\rm H\beta$, and [OIII], and derive stellar
fractions from population synthesis models. We focus our analysis on
absorption and low-ionization galaxies. For absorption-line galaxies we
recover the well known result that these galaxies have had no detectable
evolution since $z\sim 0.6-0.7$, but we also find that in the range $z=0.65-1$
at least 50% of the stars in bright absorption systems are younger than
2.5Gyr. Faint absorption-line galaxies in the cluster at $z=0.29$ also had
significant star formation during the previous 2-3Gyr, while their brighter
counterparts seem to be composed only of old stars. At $z\sim 0.8$, our
dynamically young cluster had a truncated red-sequence. This result seems to
be consistent with a scenario where the final assembly of E/S0 took place at
$z<1$. In the volume-limited range $0.35\leq z\leq 0.65$ we find that 23% of
the early-type galaxies have LINER-like spectra with $\rm H\beta$ in
absorption and a significant component of A stars. The vast majority of LINERs
in our sample have significant populations of young and intermediate-aged
stars and are thus not related to AGN, but to the population of ‘retired
galaxies’ recently identified by Cid-Fernandes et al. (2010) in the SDSS.
Early-type LINERs with various fractions of A stars, and E+A galaxies appear
to play an important role in the formation of the red sequence.

###### keywords:

cosmology: observations – galaxies: evolution - large scale structures -
evolution – RX J0054.0-2823

##  1 Introduction

In the course of an investigation of the diffuse intergalactic light in X-ray
emitting clusters at intermediate redshifts (Melnick et al., 1999), we
detected a puzzling S-shaped arc-like structure in the ROSAT cluster RX
J0054.0-2823 (Faure et al., 2007), which we tentatively identified as the
gravitationally lensed image of a background galaxy at a redshift between
z=0.5 and z=1.0. The cluster, however, is characterized by having three
dominant D or cD galaxies in the center, two of which are clearly interacting.
We designed an observing strategy that allowed us at the same time to observe
the arc, the diffuse Intra-Cluster Light (ICL), and a magnitude limited sample
of individual galaxies in the field taking advantage of the multi-object
spectroscopic mode of the FORS2 instrument on Paranal. By optimizing the mask
design (see below) we were able to obtain: (a) very deep observations of the
arc; (b) very deep long-slit observations of the ICL; and (c) redshifts and
flux distributions for 654 galaxies of which 550 are in the pencil beam and at
$0.275\leq z\leq 1.05$.

Our pencil beam sample covers a redshift range up to z = 1 (with some galaxies
up to z = 1.7). In standard cosmology with $H_{o}=75$ $\rm
km~{}s^{-1}~{}Mpc^{-1}$, $\Omega_{0,m}=0.30$, and $\Omega_{0,\Lambda}=0.70$,
this range provides a large leverage of about 3000 Mpc or 7 Gyr, which should
be sufficient to extract some of the most conspicuous characteristics on
galaxy evolution at $z\leq 1$. About half of all stars seem to be still
forming, mostly in disks, in this redshift range (Dickinson et al., 2003;
Hammer et al., 2005). Our spectroscopy provides a 50-60% complete sample of
the galaxies in a pencil beam of $\sim 10^{\prime}\times 10^{\prime}$,
centered on the cluster, uniformly down to R=23. Our sample compares in size
with the DEEP1 spectroscopic pilot survey (Weiner et al., 2005) but is smaller
than large surveys such as DEEP2 (e.q. Lin et al. 2008; Yan et al. 2009), VVDS
(e.q. Franzetti et al. 2007; Garilli et al. 2008), GOODS (e.q. Bell et al.
2005; Weiner et al. 2006). The advantage of a pilot survey is that it can be
handled rather easily by a single (or a few) researcher(s) to test new
methods, new ideas before applying these new methods to large samples.

The vast majority of our individual spectra reduced to zero redshift have S/N
ratios per $\rm 2.6\AA$ pixel larger than 3 at $\rm 4200\AA$. This resolution
is very well adapted to the detection of small equivalent width [OII]
emission, which is expected to be found in bulge dominated galaxies with small
disks, in some LINERs, in “mixed” mergers between E/S0 and star-forming
objects, and perhaps in some post-starbursts galaxies. The line of sight of
our field crosses three main structures: a dynamically young cluster at
$z=0.29$, an over-dense region with layers at $z=0.4-0.5$, and a mixed region
of field and possible layers from $z=0.6$ to $z=1$. According to morphology-
density relations (Dressler, 1980; Dressler et al., 1997; Melnick & Sargent,
1977; Smith et al., 2005; Postman et al., 2005; Cooper et al., 2006; Scoville
et al., 2007), we expect that over-dense regions will provide a rather large
number of red objects available to our study. Therefore red objects with or
without star formation, or with low photo-ionization is the subject which we
will focus on, having in mind the possible roles of E+A galaxies (Dressler &
Gunn, 1983; Norton et al., 2001; Blake et al., 2004; Goto, 2007; Yang et al.,
2008, and references therein) and of LINERs (Yan et al., 2006) in the
building-up of the red sequence.

We focus on galaxies with either low star-formation or low ionization which
appear at $z\leq 0.6$. We use line ratio diagnosis based upon [OII], $\rm
H\beta$, and [OIII], from Yan et al. (2006), to classify galaxies in LINERs,
star-forming galaxies, and Seyferts. This method, combined with visible
morphology, allow us to isolate a significant population of early-type LINERs,
and galaxies with diluted star-formation in later morphological types at
$z=0.35-0.6$.

Several studies suggest that the bulk of stars in early-type cluster galaxies
had a formation redshift of $z\geq 3$, while those in lower density
environments may have formed later, but still at $z\geq 1.5-2$ (for reviews
see Renzini, 2006, 2007). This may be in contradiction with the rise in the
number of massive red galaxies found by Faber et al. (2007) who concluded that
most early types galaxies reached their final form below $z=1$. Our data
include a clear red sequence at $z=0.29$ and a quite large number of
absorption systems up to $z\sim 1$ which we fit with population synthesis
models in order to search for age variations with $z$ and luminosity.

The paper is structured as follows. Section 2 presents details of the
observations and the data reduction procedures. Section 3 is on the resulting
redshift catalog. Section 4 presents an overview of variations in spectral
energy distribution with redshift for absorption and emission systems. Section
5 is dedicated to population variations with $z$ and luminosity in absorption
systems. Low-ionization galaxies are in 5.3. In Section 5.4 we suggest a
scenario in which early-type LINERs will become E/S0 galaxies once the A stars
die, and photo-ionization disappear. Summary and Conclusions are in Section 6.

##  2 Observations and data reduction

The observations (ESO program 078A-0456(A) were obtained with the FORS2
instrument (fors:2005, 2005) on the Cassegrain focus of the VLT UT1 telescope
in multi-object spectroscopy mode with the exchangeable mask unit (MXU). They
were acquired in service observing and were spread over two periods 78 and 80
to satisfy our observing conditions. FORS2 was equipped with two $\rm 2k\times
4k$ MIT CCDs with $15\rm\mu m$ pixels. These CCDs have high efficiency in the
red combined with very low fringe amplitudes. We used the grisms 300V and
600RI, both with the order sorting filter GG435. With this filter, the 300V
grism has a central wavelength at 5950 Å and covers a wavelength between
$4450-8700$ Å at a resolution of 112 Å$~{}{\rm mm}^{-1}$. The 600RI grism has
a central wavelength of 6780 Å and covers the 5120-8450 Å region at a
resolution of 55 Å ${\rm mm}^{-1}$. Combined with a detector used in binned
mode, the 300V grism has a pixel resolution of 3.36 Å pixel-1. The grisms were
used with a slit width of 1′′. In order to match the major and minor axis of
the ICL and the prominent arc-like feature rotation angles of $-343^{o}$,
$-85^{o}$, and $-55^{o}$ were applied. The slit lenghts used for the ICL
spectra are 56.5′′, 32.5′′, and 24.5′′, while those of typical galaxies vary
between 7′′ and 12′′. The ICL was located either on the master CCD or the
second one, resulting in a combined pencil beam field of $\rm
10.75^{\prime}\times 7.5^{\prime}$ (Figure 1).

A total of 30 hours of observing time including field acquisition, mask
positioning, and integration time were dedicated to our pencil beam redshift
survey of the J0054.0-2823 field. Each mask was filled with 39-49 slitlets in
addition to the ICL slits. In order to trace some of the apparent structures
connected to J0054.0-2823, and to reach beyond its Virial radius, we also
obtained MXU exposures of 8 FORS2 fields of $\rm 7^{\prime}\times 5^{\prime}$
adjacent to the pencil beam, so in total we obtained spectra of 730 individual
sources.

Figure 1: The central (pencil beam) field from R images obtained with the wide
field camera at the 2.2m telescope in La Silla

###  2.1 Mask preparation

Tables for preparing the masks and instrument setups were obtained with the
FORS Instrumental Mask Simulator111
http://www.eso.org/sci/observing/phase2/FORS/FIMS.html (FIMS, 2006). The
selection of the objects for the preparation of the slit masks of the pencil
beam field was done by using a photometric catalog in V and I which we had
derived from deep images obtained in a previous NTT run (Faure et al., 2007),
and pre-images in R from the VLT. The selection of the objects in the fields
adjacent to the pencil beam were obtained by using images taken with the WIde
Field Imager (WIFI; $34^{\prime}\times 33^{\prime}$) at the 2.2m telescope on
La Silla. Photometry in V and R from the WFI images are used throughout the
paper. The allocated time was divided in observing blocks (OBs) to be executed
in service mode. A typical OB of 1h execution time had a science integration
time of 2900s in two exposures of 1450s.

We estimated exposure times for E to Sb galaxies in the range z = 0.3 - 0.8.
Using the exposure time calculator of FORS, we obtained magnitude limits, the
major steps of which are given in Table LABEL:maglimit, which we used to
optimize the distribution of slitlets in the masks. After isolating bright
objects which did not require long exposure time, we prepared a grid with an
exposure time step $\rm 2\times 1450~{}s$ which we filled with galaxies having
V magnitudes such that the expected S/N ratio would be better than 2.8 (1
pixel along the dispersion). After receiving VLT pre-images in the red band,
we did a similar grid in R and adjusted the two grids. The masks were prepared
interactively with the FIMS tool and the R pre-images. We started to fill
masks with objects that require an exposure time $\rm\leq 2\times 1450~{}s$,
then moved to $\rm\leq 4,~{}6,~{}and~{}8\times 1450~{}s$. Because we prepared
sets of masks with slits in very different directions (those of the ICL long
and short axis in particular), objects that could not be targeted with a mask
in a given direction (i.e. such as any mask with running name ICL-s in Table
2) were targeted in a perpendicular one (i.e. masks with running name ICL-L),
an approach which made the mask filling quite efficient, in particular in
over-dense areas and field edges. Objects which were close to a predicted S/N
of 2.8 in an OB, were selected to be also observed in another OB as often as
possible. Some objects with expected good S/N in an OB, were re-observed in
another OB when there was no other target in the corresponding slit strip.
They provides a set of high S/N $(\sim 20)$ ratio spectra.

A total of 973 slitlets were selected, 621 in 14 different masks in the pencil
beam field, and 352 in 8 masks in the adjacent fields. Thirty five percent of
the sources of the pencil beam field were observed through different masks,
whereas the slitlets of the adjacent fields are all for different sources.

Table 1:  Table used for preparing MXU plates of multiple Observing Blocks  Number of OBs of 1h | Integration time | Magnitude limit in V | S/N for S0-Sb at $0.3\leq z\leq 0.8$
---|---|---|---
1 | 2900s | 24.4 - 24.8 | 2.8 - 5.2
2 | 5800s | 24.8 - 25.2 | 2.8 - 5.2
4 | 11600s | 25.2 - 25.6 | 2.8 - 5.2

The resulting list of masks and OBs, and the journal of observations are given
in Table 2. Spectra of the pencil beam field were obtained through masks with
running names Bright, ICL-L, ICL-s, and arc. ICL-L and ICL-s were obtained
with rotator angle $-343^{o}$ and $-85^{o}$ respectively, and arc with a
rotation of $-55^{o}$. Masks with names SE, E, NE, N, NW, W, SW1 & SW2 are on
adjacent fields. The observations were obtained during clear nights, with
seeing between $0.7^{\prime\prime}$ and $1.5^{\prime\prime}$ and dark sky.

Table 2:  Journal of the MXU Observations Name | OB ID | Date | Exp. time (s) | # slitlets | Grism
---|---|---|---|---|---
Bright1 | 255728 | 20 Oct. 06 | $3\times 550$ | 34 | 600RI
Bright2 | 255726 | 23 Oct. 06 | $3\times 550$ | 38 | 600RI
SW1 | 255710 | 18 Oct. 06 | $3\times 550$ | 45 | 300V
SW2 | 255708 | 15 Oct. 06 | $3\times 550$ | 40 | 300V
W | 255712 | 19 Oct. 06 | $3\times 550$ | 49 | 300V
SE | 255706 | 3 Oct. 07 | $3\times 550$ | 42 | 300V
N | 255716 | 5 Oct. 07 | $3\times 550$ | 42 | 300V
NW | 255714 | 5 Oct. 07 | $3\times 550$ | 48 | 300V
NE | 255718 | 14 Oct. 07 | $3\times 550$ | 47 | 300V
E | 255704 | 15 Oct. 06 | $3\times 710$ | 39 | 600RI
ICL-s1 | 255750 | 12 Dec. 06 | $2\times 1450$ | 46 | 300V
ICL-s2 | 255748 | 15 Nov. 06 | $2\times 1450$ | 48 | 300V
ICL-L1 | 255761 | 12 Dec. 07 | $2\times 1450$ | 41 | 300V
ICL-L2 | 255763 | 9 Jan. 07 | $2\times 1450$ | 39 | 300V
arc2 | 255734 | 24 Nov. 06 | $2\times 1450$ | 48 | 300V
arc1 | 255736, 38 | 9 Jan. 07, 11 Sept. 07 | $4\times 1450$ | 43 | 300V
ICL-s3 | 255744, 46, 47 | 27 Oct. 06, 9 Nov. 06 | $6\times 1450$ | 47 | 300V
ICL-L3 | 255752, 59, 60 | 21 Sept. 07, 31 Oct. 07 | $6\times 1450$ | 49 | 300V
arc3 | 255730, 32, 33 | 17 Aug. 07 | $6\times 1450$ | 46 | 300V
ICL-s4 | 255739, 41, 42, 43 | 23 Oct. 06, 13 Nov. 06 | $8\times 1450$ | 46 | 300V
ICL-L4 | 255754, 56, 57, 58 | 15, 17 & 20 Nov. 06 | $8\times 1450$ | 49 | 300V
RI | 255720, 22, 23, 24, 25 | 13 Nov. 06, 12 & 14 Sept. 07, |  |  |
|  | & 3 Oct. 07 | $10\times 1450$ | 47 | 600RI

###  2.2 Spectral extraction

The data were reduced by the ESO quality control group who provided us with
science products (i.e. sky subtracted, flat fielded and wavelength calibrated
spectra of our objects), together with calibration data: master bias (bias and
dark levels, read-out noise), master screen flats (high spatial frequency
flat, slit function), wavelength calibration spectra from He-Ar lamps, and a
set of spectrophotometric standards, which were routinely observed. The sky
subtracted and wavelength calibrated 2D spectra allowed a very efficient
extraction of about 60 % of the spectra. Nevertheless the pipeline lost a
significant fraction of objects, in particular when they were located on the
edges of the slitlets. To increase the efficiency of the spectral extraction
we performed a new reduction starting from frames that were dark subtracted,
flat-fielded and wavelength calibrated, but not sky subtracted, using a list
of commands taken from the LONG context of the MIDAS package. For each
slitlet, the position of the object spectrum was estimated by averaging 500
columns in the dispersion direction between the brightest sky lines and
measuring the maximum on the resulting profile. The sky background was
estimated on one side of the object, or on both, depending on each case.
Spatial distortion with respect to the columns was measured on the sky line at
5577 Å and used to build a 2D sky which was subtracted to the 2D spectrum.
Multiple exposures where then aligned and median averaged. The 1D spectra of
objects were extracted from 2D medians by using the optimal extraction method
in MIDAS.

###  2.3 Redshift identification

The identification of lines for determining the redshifts was done
independently by two methods and three of the authors. The 2D spectrum was
visually scanned to search for a break in the continuum, or an emission-line
candidate (e.g. [OII] $\lambda$3728.2 Å). A plot of the 1D spectrum was
displayed in the corresponding wavelength region to search for [OII], the Ca H
& K lines, and/or Balmer lines H$\epsilon$, H9 $\lambda$3835.4 Å, H8
$\lambda$3889.1 Å, H10 $\lambda$3797.9 Å, and H$\delta$. The redshift was then
confirmed by searching for the [OIII] doublet $\lambda$4958.9 & 5006.8 Å, and
H$\beta$ in emission if [OII] had been detected, or G and the Mgb band, if the
4000 Å break and (or) the H and K lines had been identified. The MgII
$\lambda$2799 Å line in absorption and, in some cases AlII $\lambda$ 3584 Å,
were searched to confirm a potential redshift $z\geq 0.65$, while in the cases
of low redshift candidates we searched for $\rm H\beta$, the NaD doublet
$\lambda$5890 & 5896 Å, and in a few cases H$\alpha$. The H$\gamma$ line, the
E (FeI+CaI $\lambda$5270 Å) absorption feature and, in some bright galaxies
the Fe $\lambda$4383 Å, Ca $\lambda$4455 Å, Fe $\lambda$4531 Å absorption
lines, were used to improve the redshift value. The resulting identification
ratio of galaxy redshifts is of the order of 90%. The 10% of so-called
unidentified include stars, objects with absorption lines which were not
understood, a few objects with low signal, and defects. Six QSO’s were also
found. An example of good spectrum of red galaxy, with its main absorption
lines identified, is shown in Figure 2.

Figure 2: Example of a spectrum of a red and bright galaxy with [OII] and the
main absorption lines identified

A second independent visual identification was performed using Starlink’s
Spectral Analysis Tool (SPLAT-VO), matching an SDSS reference table of
emission and absorption
lines222http://www.sdss.org/dr5/algorithms/linestable.html to the spectra.
After a first estimate of the redshift a cross-correlation was performed using
the FXCOR task on the RV package of IRAF333IRAF is distributed by the National
Optical Astronomy Observatory, which is operated by the Association of
Universities for Research in Astronomy, Inc., under cooperative agreement with
the National Science Foundation.. Due to the large span of redshifts, two sets
of templates were used. The first one consisting of 3 template spectra of
galaxies ($\lambda=3500-9000$Å with emission and absorption lines and a
dispersion of 3Å/pix) from the SDSS
survey444http://www.sdss.org/dr2/algorithms/spectemplates/index.html with
continuum subtraction using a spline3 order 5 fitting function. The second set
of templates were two average composite spectra of early type and intermediate
type galaxies ($\lambda=2000-7000$Å with only absorption lines and a
dispersion of 2Å/pix) from the K20
survey555http://www.arcetri.astro.it/$\sim$k20/spe_release_dec04/index.html
using a spline3 order 7 function for continuum subtraction. An interactive
selection of the wavelength range used in the cross correlation was done on
each spectrum avoiding contamination by sky lines. The spectra were re-binned
to the template dispersion (smaller for 300V spectra and larger for 600RI
spectra) , which gave the best results. Velocity errors were determined from
the quality of the cross-correlation, by using standard R value of Tonry &
Davies (Tonry:1979p5170, 1979). Here we used $R_{T}$ to differentiate it from
the R band magnitudes symbol. These values are provided in the IRAF task FXCOR
and explained in the reference quoted. In brief, $R_{T}$ is proportional to
the ratio of the fitted peak height and the antisymmetric noise as defined by
Tonry & Davies (1979). The redshifts, $R_{T}$ values, and velocity errors are
given in Table 5, which also includes the list of visually identified lines.

A third independent visual inspection was carried out when a discrepancy was
seen between the previous two sets of measurements, and also in the very few
cases were no redshift could be measured. For these spectra we first tried to
detect emission or absorption lines and then used Gaussian fits to establish
the line centroids and their errors and shifts. The redshift of each line was
measured independently and the galaxy redshift was obtained from the weighted
average of all lines. This third inspection resolved nearly all the few
remaining discrepancies so we have retained the cross-correlation values
whenever possible. We note that Xcorr failed in two instances: 1) for $z>0.8$
galaxies with low S/N and few weak absorption lines, and, 2) when no
absorption lines, but 1, 2 or 3 clear emission lines were present. In these
cases we used the visual line identifications and assigned a conservative
error of 300 km/s.

Spectra from more than one mask were obtained for 94 objects. Their final
velocities and velocity errors were calculated as error-weighted means from
multiple observations, although no significant disagreements were found. These
repeated observations serve as a check on the internal errors. Figure 3
presents the differences between the cross-correlation velocity measurements
for all galaxies with multiple observations. The representative full width
half maximum (FWHM) error is 200 km/s. In Figure 4 we have plotted the
relation between velocity errors and the Tonry $R_{T}$ value obtained in our
cross correlations. Most errors are $<300$ km/s even for $4>R_{T}>2$ and the
typical error is of order 80 km/s with the vast majority of the radial
velocities have errors below 200 km/s. We have only discarded a few values
with $R_{T}<1$ when there were no measurable emission lines.

Figure 3:  Radial velocity differences for galaxies with multiple
observations. The objects with velocity discrepancies larger than $400$ km/s
are broad line QSO’s and one high-z galaxy. Figure 4: Relation between radial
velocity errors ($V_{err}$) and the Tonry $R_{T}$ parameter (Tonry:1979p5170,
1979) in redshifts obtained by cross-correlation. The points away from the
general trend (5 points with $R_{T}>10$ and 5 with $V_{err}>500~{}\rm
km~{}s^{-}1$) are 5 QSO’s and distant weak spectrum galaxies with emission
lines. Objects with $V_{err}>500~{}\rm km~{}s^{-}1$, marked in red, were not
used in combined spectra.

###  2.4 Flux Calibrations

The 1D spectra were divided by the response curve of the detector, which had
been determined from 4 spectrophotometric standard stars observed along the
runs, and reduced by the same method (bias, flat field, wavelength
calibration, and extraction) as the galaxy spectra. The thick absorption
telluric band of O2 centered at 7621 Å (unresolved line series) was not
removed from the observation response curve and was considered as a feature of
the global wavelength dependent efficiency.

The relative fluxes per wavelength of the corrected spectra can be compared
with stellar population models, in arbitrary unit, but are not calibrated in
flux. The spectra were re-binned to the z = 0 rest frame with relative flux
conservation. Because a significant fraction of spectra have a too low S/N
ratio for a meaningful comparison with population synthesis models, one may
either select the brightest objects or combine spectra of similar types. The
spectra taken at different locations of the MXU masks have different lengths
along the dispersion direction. In order to merge them the spectra were
normalized to have the same flux in the region 4050-4250 Å (see below).

###  2.5 Quality of the spectra

The final S/N ratio of the extracted spectra, corrected for the response
curve, and re-binned to zero redshift depends on a number of parameters:
seeing, night sky transparency and background, magnitude of the object and
integration time, wavelength of the S/N measurement, and redshift. To give an
idea of the final products we present in Table 3 a representative set of 28
spectra at various z, magnitudes, number of OBs and resulting S/N ratio
measured on zero redshift spectra in the wavelength range 4150-4250 Å which
corresponds well to the location where we will measure the main indexes of
this work. S/N ratios of spectra re-binned to zero redshift are for a pixel
element of $\rm 2.6~{}\AA$ throughout the paper. Table 3 gives also the names
of the OBs.

Table 3:  Signal-to-noise ratio of representative spectra. The columns indicate respectively: the redshift $(z)$ of a selected object, its V and R Petrosian magnitudes, the number of observing blocks, N(OB), from which its spectrum is extracted, the S/N ratio measured in the wavelength range 4150-4250Å of the spectrum rebinned to zero redshift, the name of observing blocks from Table 2, and the grism used. Spectra from OB’s with running name “arc” have on the average higher S/N ratio than those with name “ICL” as illustrated by the two objects marked (*). $z$ | V | R | N(OB) | S/N | Name of OBs | Grism
---|---|---|---|---|---|---
0.2923 | 19.2 | 18.6 | 2 | 14 | arc1 | 300V
0.2932 | 20.3 | 19.3 | 2 | 18 | ICL-L1 & L2 | 300V
0.2928 | 21.6 | 20.5 | 3 | 17 | arc2 & ICL-L1 | 300V
0.2905 | 22.8 | 22.1 | 2 | 10 | ICL-L1 & L2 | 300V
0.2910 | 23.5 | 22.8 | 3 | 9 | arc1 & 2 | 300V
0.4486 | 21.3 | 20.0 | 1 | 6 | Bright2 | 600RI
0.4477 | 22.3 | 21.3 | 2 | 20 | arc1 | 300V
0.4148 | 23.0 | 22.3 | 2 | 7 | ICL-s1 & s2 | 300V
0.4538 | 23.1 | 22.0 | 5 | 14 | ICL-L4 & arc2 | 300V
0.5355 | 22.3 | 20.9 | 1 | 8 | arc2 | 300V
0.6309 | 22.7 | 21.4 | 4 | 11 | arc2 & 3 | 300V
0.6553 | 22.3 | 21.5 | 4 | 9 | ICL-s4 & arc2 | 300V
0.6282 | 23.5 | 22.5 | 5 | 9 | arc1 & 3 | 300V
0.6267 | 23.9 | 22.9 | 4 | 10 | arc3 | 300V
0.6864 | 22.6 | 21.9 | 1 | 4 | ICL-s2 | 300V
0.6886 | 23.0 | 22.0 | 7 | 13 | ICL-L3 & L4 | 300V
0.6861 | 23.0 | 22.1 | 4 | 8 | ICL-s4 | 300V
0.6864 | 23.5 | 22.3 | 4 | 10 | ICL-s3 & arc2 | 300V
0.6879 | 23.8 | 22.8 | 4 | 7 | ICL-s4 | 300V
0.8222 | 20.7 | 20.0 | 1 | 10 | ICL-s1 | 300V
0.8287 | 22.7 | 22.4 | 5 | 10 | ICL-s4 & arc2 | 300V
0.8249 | 23.2 | 22.6 | 3 | 8 | arc3 | 300V
0.8823 | 23.8 | 23.4 | 4 | 3.5 | ICL-s4 | 300V
0.9792 | 23.3 | 22.7 | 3 | 8 | arc3 (*) | 300V
0.9626 | 23.2 | 22.7 | 4 | 5 | ICL-L4 (*) | 300V
0.9637 | 23.4 | 23.2 | 3 | 6 | ICL-s3 | 300V
0.9809 | 23.8 | 23.7 | 5 | 6 | RI | 600RI
1.0220 | 24.1 | 23.3 | 4 | 3 | ICL-s4 | 300V

###  2.6 Spectral indexes

The 4000 Å break amplitude definition used in the present paper is the
‘narrow’ 4000 Å break defined by Balogh et al. (1999) as the flux ratio in the
range 4000-4100Å over 3850-3950Å (e.g. Kauffmann et al., 2003). The error in
D(4000) is calculated from the spectral noise in the two passbands. The
equivalent widths of [OII] and of $\rm H\delta$ were measured by using the
MIDAS context ALICE as follows: the continuum was obtained by linear
interpolation through two passbands each side of the line, a Gaussian was
fitted to the emission or absorption line, and an integration was done over
the resulting Gaussian profile above or below the continuum. The continuum and
line fits, and the integration were done interactively on a graphic window in
which the spectral region of the line was displayed. Table LABEL:integr lists
the wavelength ranges of the sidebands used to define the fluxes and continua.

Table 4:  Wavelength bands used in the measurement of 4000 Å break amplitude, and in the determination of the continua of the [OII] and $\rm H\delta$ indexes (equivalent widths). Index | Blue band | Red band
---|---|---
D(4000) | 3850 - 3950 Å | 4000 - 4100 Å
EQW([OII]) | 3650 - 3700 Å | 3750 - 3780 Å
$\rm EQW(H\delta)$ | 4030 - 4070 Å | 4130 - 4180 Å

Uncertainties in equivalent widths were deduced from simple Monte Carlo: the
values of the equivalent widths are the average of 20 continuum determinations
and best Gaussian fits to the absorption or emission lines, and the errors in
equivalent widths are deduced from the Monte Carlo dispersion. The largest
index errors are for spectra in which $\rm H\delta$ is both in absorption and
in emission. In such cases the emission line was removed after fitting the
spectrum of an A star onto all Balmer lines to estimate the depth of $\rm
H\delta$ in absorption, and this step was added to the Monte Carlo. The errors
on indexes given in Tables of combined spectra throughout the paper are those
which were measured on combined spectra. They do not take into account the
astrophysical dispersions in the distributions of individual galaxies which
were used to build combined spectra. Those astrophysical dispersions are given
in relevant Tables concerning spectral variations.

Full observational measurement errors on indexes of individual spectra were
obtained by measuring $\rm D(4000)$ and $\rm EQW([OII])$ on spectra with
multiple observations. Thus $17\%$ of the spectra have typical errors of $4\%$
in D(4000) and $10\%$ in EQW([OII]); $54\%$ have typical errors of $8\%$ in
D(4000) and $20\%$ in EQW([OII]); and $14\%$ have poorer spectra with typical
errors of $16\%$ in D(4000) and $40\%$ in EQW([OII]).

###  2.7 Stellar Population Analysis

In order to study the stellar population quantitatively, we applied a modified
version of the spectral population synthesis code,
starlight666http://www.starlight.ufsc.br/ (Cid Fernandes et al., 2004; Gu et
al., 2006) to fit the observed and combined spectra. The code does a search
for the best-fitting linear combination of 45 simple stellar populations
(SSPs), 15 ages, and 3 metallicities ($0.2\,Z_{\odot}$, $1\,Z_{\odot}$,
$2.5\,Z_{\odot}$) provided by Bruzual & Charlot (2003) to match a given
observed spectrum $O_{\lambda}$. The model spectrum $M_{\lambda}$ is:

$M_{\lambda}(x,M_{\lambda_{0}},A_{V},v_{\star},\sigma_{\star})=M_{\lambda_{0}}\left[\sum_{j=1}^{N_{\star}}x_{j}b_{j,\lambda}r_{\lambda}\right]\otimes
G(v_{\star},\sigma_{\star})$ (1)

where
$b_{j,\lambda}=L_{\lambda}^{SSP}(t_{j},Z_{j})/L_{\lambda_{0}}^{SSP}(t_{j},Z_{j})$
is the spectrum of the $j^{\rm th}$ SSP normalized at $\lambda_{0}$,
$r_{\lambda}=10^{-0.4(A_{\lambda}-A_{\lambda_{0}})}$ is the reddening term,
$x$ is the population vector, $M_{\lambda_{0}}$ is the synthetic flux at the
normalization wavelength, and $G(v_{\star},\sigma_{\star})$ is the line-of-
sight stellar velocity distribution modeled as a Gaussian centered at velocity
$v_{\star}$ and broadened by $\sigma_{\star}$. The match between model and
observed spectra is calculated as
$\chi^{2}(x,M_{\lambda_{0}},A_{V},v_{\star},\sigma_{\star})=\sum_{\lambda=1}^{N_{\lambda}}\left[\left(O_{\lambda}-M_{\lambda}\right)w_{\lambda}\right]^{2}$,
where the weight spectrum $w_{\lambda}$ is defined as the inverse of the noise
in $O_{\lambda}$. The code yields a table with input and ouput parameters for
each component. Input parameters include individual stellar masses, ages,
metallicities, L/M, … and ouput parameters include luminosity fractions, mass
fractions, fit parameters of individual components …, and global parameters
such as velocity dispersion and extinction. For more details we refer to the
paper by Cid Fernandes et al. (2005). In the present work we use the standard
luminosity fraction in the rest frame of normalized spectra at $\rm
4050~{}\AA$, which we compare in different redshift bins.

Figure 5: Spectral fitting results with SSP models for the redshift $<z>=0.29$
bin. (a): Observed (thin black line), model (red line) and residuals for the
absorption spectrum. Points indicate bad pixels and emission-line windows that
were masked out during fitting. (b): Emission-line spectrum; (c): Total
spectrum.

Figure 5 shows an example of the fit for the averaged spectrum at $<z>=0.29$.
Panels (a), (b), and (c) correspond to absorption-line, emission-line, and all
spectra respectively. After fitting the spectra, we rebin the 45 SSPs into 5
components according to their age: I ($10^{6}\leq t<10^{8}$ yr), II
($10^{8}\leq t<5\times 10^{8}$ yr), III ($5\times 10^{8}\leq t<10^{9}$ yr), IV
($10^{9}\leq t<2.5\times 10^{9}$ yr), and V ($t\geq 2.5\times 10^{9}$ yr).
Components with the same age and different metallicities are combined
together.

##  3 The Catalog of Galaxies and Large Scale Structures in the Line of Sight
in the Pencil Beam

Table 5 presents positions, redshifts, Petrossian R-magnitudes ($m_{R}$), and
line identifications for the full sample of 654 galaxies observed in our
program. The radial velocities and the corresponding measurement errors are
also given. The full Catalogue from which Table 5 is extracted will be sent as
a public database to CDS. The rough data are presently in the public domain at
ESO.

Table 5: Properties of galaxies in the field of RX J0054.0-2823  obj | RA ($\alpha$) | DEC ($\delta)$ | z | $m_{R}$ | V | Verr | $R_{T}$ | Nobs | lines
---|---|---|---|---|---|---|---|---|---
| J2000 | J2000 |  |  | km/s | km/s |  |  |
23 | 13.598707 | -28.434965 | 0.79304 | 22.78 | 237912 | 161 | 4.7 | 1 | K–H
26 | 13.590379 | -28.416917 | 0.77636 | 22.26 | 232908 | 77 | 8.1 | 1 | [OII]–H10–H9–H
27 | 13.584442 | -28.394515 | 0.41463 | 22.29 | 124389 | 22 | 17.3 | 1 | [OII]–H9–H–H$\beta$–[OIII]
28 | 13.586628 | -28.438063 | 0.44877 | 22.02 | 134631 | 73 | 6.7 | 1 | K–H–G
30 | 13.580301 | -28.435437 | 0.29032 | 21.08 | 87096 | 68 | 11.3 | 1 | H9–K–H–H$\delta$–H$\alpha$
31 | 13.572009 | -28.380385 | 0.63267 | 21.32 | 189801 | 49 | 11.5 | 1 | [OII]–K–H
32 | 13.579012 | -28.439414 | 0.45335 | — | 136005 | 22 | 19.5 | 1 | [OII]–H$\gamma$–H$\beta$–[OIII]
33 | 13.574997 | -28.439377 | 0.44741 | 19.21 | 134223 | 80 | 11.4 | 1 | K–H–G–H$\beta$
34 | 13.573913 | -28.442855 | 0.63013 | 20.63 | 189039 | 87 | 7.9 | 1 | K–H–H$\delta$–G
35 | 13.571781 | -28.435423 | 0.44862 | 20.26 | 134586 | 73 | 9.8 | 1 | H9–K–H–G–H$\beta$

Figure 6 shows the R-magnitude histogram of the galaxies with measured
redshifts superimposed on the magnitude histogram of all galaxies in our
pencil-beam indicating that our observations sample uniformly at a rate of
50-60% the population of galaxies down to $\rm R=22.5$. The sampling seems
fairly representative in the magnitude bin $\rm R=22.5-23.0$, and sparse at
$\rm R>23$. The apparent increase in incompleteness toward brighter magnitudes
is due to a selection bias in the observations, which were designed to avoid
bright galaxies at redshifts $z\leq 0.25$.

Figure 6: R-magnitude histogram of galaxies with measured redshift in the
central beam.

Figure 7 presents the magnitude redshift relation and the cone diagrams for
the full sample. The points are color coded according to the presence or
absence of emission lines.

Figure 7:  (a) Magnitude redshift relation for the full sample. The three
lines overploted over the measured points correspond to absolute R magnitudes
of -22.5, -20.5, and -18.5. The distances have been calculated using a
cosmology with $\Omega_{0,\Lambda}=0.70$, $\Omega_{0,m}=0.30$, $w=-1$, and
$\rm H_{0}=75km~{}s^{-1}~{}Mpc^{-1}$ (h = $\rm
H_{0}/75km~{}s^{-1}~{}Mpc^{-1}$). Red dots are galaxies with no emission lines
and blue dots are galaxies with emission lines. (b) Cone diagrams in Dec for
all the galaxies measured in the field of RX J0054.0-2823. The scales is in
Mpc calculated using the angular distance for the standard cosmology. The
detection threshold for emission-lines is $\rm EQW([OII])\sim 2-3$Å. (c) Same
as (b) but for RA.

A cursory inspection of Figure 7 reveals the presence of several conspicuous
structures - walls of objects spanning almost the entire field of view - over
the full range of redshifts covered by our observations. Ignoring objects with
$z<0.28$, we see structures centered at $z=0.29$ (our prime target); two
distinct structures at $z\sim 0.4$, which we will denote $z=0.415$ and
$z=0.447$; a rather complex structure at $z\sim 0.6$, with two main over-
densities at $z=0.58-0.63$, and $z=0.68$; a single rather sparsely populated
layer at $z=0.82$. In what follows, we will refer to these groups (including
the main cluster at $z=0.29$) as our pencil beam structures. Making bins
centered on the peaks of the redshift distribution maximizes the number of
objects in each bin and minimizes its redshift dispersion. So using the
apparent structures rather than a blind slicing appears well adapted to our
sample. If the structures are real, the objects of a given structure may have
a common history and this may also help to reduce the cosmic scatter.

The numbers of spectra observed in each structure are given in Table
LABEL:zdist. The redshift bins given in column (2) are chosen a posteriori to
fit the structures. The numbers of redshifts measured in each bin are given in
column (3), with the respective numbers of absorption and emission systems in
columns (4) & (5). We have determined the median distance to the nearest
object $\triangle\delta$ in each of the apparent structures in column (6), and
the median velocity dispersion, $\rm\sigma(V)\equiv c\sigma(z)/(1+z)$ in
column (7). We give a rough morphology of the structures in column (1). The
cluster at $z=0.293$ appears to have small projected separation and velocity
dispersion. Layers or filaments have a comoving velocity dispersion (dynamical
and cosmological) less than $\rm\sim 1500~{}km~{}s^{-1}$; clouds have
$\rm\sigma(V)>2000~{}km~{}s^{-1}$. The structures marked “filaments” are the
arc layers seen in Figure 7. Projected on the sky they seem to be filamentary,
but the median distances $\triangle\delta$ to the nearest object are
approximately 2/3 those expected for uniform distributions, so they are not
clearly different from 2D layers. The names of the bins that are used to
combine spectra are given in column (8). Large scale arc structures, as seen
in cone diagrams, are expected to be formed by infall of galaxies on
gravitational potentials: galaxies which are on the far side have a negative
infall velocity, while those on the nearby side have a positive infall
component, which when superimposed on the Hubble flow reduces the velocity
dispersion. This is presumably what we observe in the two filaments or layers
with low velocity dispersion at z = 0.4.

We combined the spectra in each structure using the median. This results in a
slightly lower total S/N (by $\sqrt{2}$), but allows to eliminate spurious
features.

Table 6: Apparent structures in the field of RX J0054.0-2823  Apparent Structure | z range | N | N(abs) | N(em) | $\triangle\delta~{}(kpc)$ | $c\sigma(z)/(1+z)$ | Composite name
---|---|---|---|---|---|---|---
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
| 0.275 - 0.285 | 0 | 0 | 0 |  |  |
Cluster | 0.285 - 0.298 | 91 | 60 | 31 | 165 | 527 | SPEC029
| 0.298 - 0.320 | 5 | 1 | 4 |  |  |
| 0.320 - 0.330 | 12 | 7 | 5 |  |  |
| 0.330 - 0.390 | 28 | 7 | 21 |  |  |
| 0.390 - 0.430 | 35 | 6 | 29 | 490 | 3250 | SPEC0415
Filament (layer) | 0.432 - 0.440 | 29 | 5 | 24 | 500 | 350 | SPEC0415
| 0.440 - 0.444 | 1 | 1 | 0 |  |  |
Filament (layer) | 0.444 - 0.456 | 46 | 21 | 25 | 470 | 450 | SPEC0447
| 0.456 - 0.465 | 1 | 1 | 0 |  |  |
Cloud | 0.465 - 0.550 | 53 | 15 | 38 | 480 | 3370 |
Cloud | 0.550 - 0.620 | 56 | 15 | 41 | 520 | 3550 | SPEC063
Filament (layer) | 0.620 - 0.657 | 48 | 12 | 36 | 580 | 1450 | SPEC063
| 0.657 - 0.673 | 1 | 0 | 1 |  |  |
Filament (layer) | 0.673 - 0.696 | 43 | 12 | 31 | 650 | 1010 | SPEC068
| 0.710 - 0.790 | 22 | 4 | 18 |  |  |
Filament & cloud | 0.790 - 0.850 | 33 | 6 | 27 | 820 | 2050 | SPEC082
| 0.850 - 0.880 | 0 | 0 | 0 |  |  |
Cloud | 0.880 - 0.930 | 11 | 1 | 10 |  |  |
| 0.930 - 0.946 | 0 | 0 | 0 |  |  |
Cloud | 0.946 - 1.046 | 25 | 4 | 21 | 1080 | 4110 | SPEC099

###  3.1 Magnitudes

The R-band average magnitudes of galaxies in each redshift bin are given in
Table LABEL:magnitudes separately for absorption, “red” and “blue” emission-
line galaxies, together with the adopted distance moduli. The partition “red”
versus “blue” is defined by the median spectral slope in each redshift bin. In
a study on emission line galaxies (Giraud et al., 2010) we divided the sample
of emission-line galaxies in two halves: those with continuum slopes bluer
than the average and those with continuum slopes redder than the average in
each redshift bin. This was done interactively by displaying reduced 1D
spectra and using MIDAS. While a median partition is not necessarily a
physical partition, we showed that, in the present case, it divides ”young”
galaxies, for which the evolution is dominated by on-going star formation from
”old” galaxies where the evolution is dominated by changes in the older
stellar populations.

Table 7: Average R-band magnitudes of absorption systems (abs), and red and blue emission-line galaxies. The adopted distance moduli $(m-M)_{0}$ and the 4150-4250Å fluxes $f$ normalized to the blue galaxies at $z=0.9$ are also tabulated. $<z>$ | R(abs) | R(red) | R(blue) | $(m-M)_{0}$ |  $f$(abs) |  $f$(red) |  $f$(blue)
---|---|---|---|---|---|---|---
0.29 | 19.80 | 20.12 | 20.97 | 40.18 | 0.74 | 0.72 | 0.48
$0.43$ | 20.24 | 20.59 | 20.95 | 40.86 | 1.08 | 0.92 | 0.75
$0.65$ | 21.50 | 21.60 | 21.94 | 41.51 | 1.42 | 1.28 | 0.86
$0.9$ | 22.45 | 22.13 | 22.35 | 41.98 | 2.08 | 1.70 | 1

We used the R-band photometry to calibrate individual spectra by convolving
each spectrum with a box filter 1290 Å wide, centered at $\lambda=6460$ Å.
Once the spectra were calibrated in the observer R-band, we measured the
average fluxes in the wavelength range 4150-4250Å of the galaxies, which we
normalized to the flux of blue emission galaxies at $<z>=0.9$ to compute the
luminosity index $f$. Thus $f$ (that is equal to 1 for blue galaxies at
$<z>=0.9$) is an indicator of AB(4200) that allows us to compare the
luminosities of red and blue galaxies at a given redshift and to investigate
luminosity variations with $z$. Thus Table LABEL:magnitudes clearly shows that
in each redshift bin, absorption-line and red emission-line galaxies are more
luminous than blue galaxies.

##  4 Composite spectra

Each galaxy spectrum was wavelength calibrated, corrected for instrument
response, re-binned to zero redshift, and normalized to have the same flux in
the wavelength range $\rm\triangle\lambda=4050-4250\AA$. Normalizing spectra
gives the same weight to all galaxies. As a consequence stellar fractions must
be understood as average stellar fractions per galaxy.

We have truncated the sample at $z=1.05$ and assembled the spectra in bins
centered on (pseudo) structures at 0.29, 0.41, 0.45, 0.63, 0.68, 0.82, and
0.99 to build high S/N composite spectra for each bin. In order to compensate
(or at least alleviate) for Malmquist bias we rejected objects fainter than
$\rm M_{R}=-18.8$ mostly at $z\leq 0.45$ (Figure 7a). A sample completely free
of Malmquist bias would require a cutoff at $\rm M_{R}\sim-20.5$. For clarity
of the figures, we often combined the mean spectra at $z=0.41$ & $0.45$ into a
single bin at $<z>=0.43$, the spectra at $z=0.63$ & $0.68$ into a bin at
$<z>=0.65$, and in some cases the spectra at $z=0.82$ & $0.99$ into a bin at
$<z>=0.9$. The spectra of galaxies in these four bins are presented in Figure
8 where we show the spectra of absorption systems (top) and emission line
galaxies (bottom) separately. The corresponding $4000\AA$ break amplitudes are
given in Table LABEL:D4000

Figure 8: Composite spectra of absorption systems (top); and emission line galaxies (bottom) normalized in the wavelength range $\rm\triangle\lambda=4050-4250$ Å. All individual galaxies are brighter than $\rm M_{R}=-18.8$ Table 8:  4000Å break amplitudes for absorption (abs) and emission (em) galaxies, and equivalent width of H$\delta$ for absorption galaxies with measurement errors. The S/N ratios of the combined spectra were measured in the interval 4050Å–4250Å. The magnitude cutoff is $\rm M_{R}=-18.8$ for all redshift bins. | Absorption systems | Emission systems
---|---|---
$<z>$ | D(4000) | $\rm EQW(H\delta)$ | S/N | D(4000) | S/N
0.29 | $1.67\pm 0.065$ | $-1.5\pm 0.2$ | 23 | $1.22\pm 0.02$ | 32
$0.43$ | $1.70\pm 0.06$ | $-1.5\pm 0.2$ | 22 | $1.22\pm 0.01$ | 52
$0.65$ | $1.60\pm 0.055$ | $-1.8\pm 0.2$ | 24 | $1.14\pm 0.01$ | 35
0.82 | $1.57\pm 0.06$ | $-2.4\pm 0.5$ | 18 | $1.07\pm 0.02$ | 28
0.99 | $1.43\pm 0.05$ | $-2.9\pm 0.3$ | 23 | $1.08\pm 0.02$ | 25

The most conspicuous spectral change with redshift is a decrease in flux
redward of the G-band from $<z>=0.29$ and $<z>=0.43$ to higher $z$ coupled to
an increase to the blue of [OII] from $<z>=0.65$ to $<z>=0.82$ and higher $z$
in emission-line galaxies. This systematic change of the continuum implies
that the galaxy population varies as a function of redshift: more star forming
galaxies at higher $z$ and more galaxies with old stars at lower $z$. This
spectral change, which is known, will not be studied further in this paper
except to quantify (in 5.3.1) the impact of LINER-like galaxies at $z=0.4--
0.9$. In the following section we concentrate on absorption systems and low-
ionization galaxies.

##  5 Absorption line systems

The spectral resolution of the 300V grism allows us to detect [OII] emission
down to $\rm EQW([OII])\sim 2-3~{}\AA$. We will call absorption-line galaxies
those for which any mechanism of ionization is low enough to preclude [OII]
detection at our detection level. Thus, our pure absorption-line sample
comprises mostly E, E+A, and S0 galaxies with no on-going star formation,
nuclear activity, or other mechanism of ionization.

###  5.1 Absorption systems as function of redshift

The normalized and combined spectra of absorption line systems presented in
Figure 8 (top) do not show any obvious change in their continuum and 4000Å
break amplitude up to $z\approx 0.6$ (Table LABEL:D4000). There is a moderate
decrease in the 4000Å break at $z\geq 0.65$ ranging from $5\%$ at $z\sim 0.65$
to $7\%$ at $z\sim 0.82$ and up to $15\%$ at $z\sim 1$, while the $\rm
H\delta$ absorption line becomes stronger at $z\geq 0.65$ (Table LABEL:D4000),
suggesting the presence of increasing numbers of A stars at higher redshifts.
The indexes suggest that these galaxies had the bulk of their star formation
at $z\geq 1$, while some of the systems at $z>0.8$ still had clearly
detectable star formation about 1 Gyr ago.

We have compared our spectral indexes at $z\sim 0.82$ with those measured by
Tran et al. (2007) in the rich cluster MS 1054-03 at z = 0.83 using the same
index definitions from Kauffmann et al. (2003). The average break amplitude
and $\rm H\delta$ index of absorption systems in MS 1054-03 are respectively
$\rm D(4000)(abs)=1.67\pm 0.00$ and $\rm EQW(H\delta)(abs)=-1.7\pm 0.0$ (Tran
et al., 2007, Table 4). Our absorption systems at $z\sim 0.82$ appear to have
younger stellar populations as indicated both by $\rm D(4000)$ and $\rm
EQW(H\delta)$ (Table LABEL:D4000). Therefore our absorption systems contain A
stars, but clearly less than composite field E+A galaxies at $<z>=0.6$ for
which $\rm D(4000)(abs)=1.36\pm 0.02$ and $\rm EQW(H\delta)(abs)=-4.6\pm 0.2$
(quoted in Tran et al. (2007, Table 4) from data in Tran et al. (2004)).
Consequently our average spectrum at $z\sim 0.82$ is intermediate between pure
E and pure E + A. In fact, our SSP models (Table LABEL:poptable) indicate that
absorption-line systems at $z\geq 0.65$ contain on average more than $50\%$ of
stars younger than 2.5Gyr per galaxy, while those at $z\geq 0.8$ had
significant star formation as recently as one Gyr ago (Table LABEL:poptable).

Table 9: Stellar population properties of normalized average absorption (abs) spectra in each redshift bin. The magnitude cutoff is at $\rm M_{R}=-18.8$, except for the 10 faintest absorption systems at $z=0.29$ where we used all the observed objects. The fractions indicated in all SSP Tables are standard luminosity fractions at $\rm 4050\AA$, as in Cid-Fernandes et al. (2010, and references therein) $<z>$ |  log(Age): | $<8$ | $8-8.7$ | $8.7-9$ | $9-9.4$ | $>9.4$
---|---|---|---|---|---|---
$0.29$ | abs | 0.0% | 0.0% | 30.1% | 0.1% | 69.8%
| abs (10 brightest) | 0 | 0 | 17.4 | 0 | 82.4
| abs (10 faintest) | 0 | 0 | 12.0 | 66.4 | 21.7
$0.43$ | abs | 0.0 | 0.7 | 11.7 | 6.9 | 80.7
$0.65$ | abs | 0.0 | 0.0 | 18.2 | 38.5 | 43.3
$0.82$ | abs | 0.0 | 0.0 | 86.8 | 3.3 | 9.8
$0.99$ | abs | 0.0 | 0.0 | 42.3 | 0.0 | 57.7

Post-starburst E+A galaxies are thought to be in a transition phase between a
star-forming period and a passively evolving period. Being close to the phase
of shutdown or quenching of star formation, they probably play an important
role in the build-up of early-type systems (e.g. Wild et al, 2009; Yan et al.,
2009). Studies of intermediate redshift clusters at $0.3\leq z\leq 0.6$ have
found either a higher fraction of post-starburst galaxies in clusters than in
the field (Dressler et al., 1999; Tran et al., 2003, 2004), or a similar
fraction (Balogh et al., 1999). In fact, there is a strong variation in the
E+A fraction between the SSDS low redshift survey at $z\sim 0.07-0.09$, and
high $z$ surveys at $z\approx 0.5-1$ (VVDS, Wild et al, 2009), or $z\approx
0.7-0.9$ (DEEP2, Yan et al., 2009).

In order to search for E+A galaxies in our sample we built template spectra by
combining a pure E spectrum from our sample with various fractions of an A
stellar template. We then compared our models with absorption-line systems in
the range $\rm 1.2\leq D(4000)\leq 1.5$ assuming, by definition, that E+A
galaxies contain at least $\rm 25\%$ A stars. Using this (standard) definition
we searched our sample at $0.35\leq z\leq 1$ and found only 6 bona-fide E+A
galaxies. In fact all the objects were found at $0.68\leq z\leq 1$, which
makes our small number consistent with the VVDS and the DDEP2 surveys within a
factor of 2. The median of the normalized spectra of these 6 (as far as we can
judge from our images) elliptical galaxies is presented in Figure 9 (a). We
were surprised to find no E+As at $z\sim 0.4$, but we did find 4 objects with
early-type morphology and very small $\rm<EQW([OII])>\approx 3.5\AA$, which
probably would have been classified as E+As on lower resolution spectra. The
average spectrum of these 4 objects is shown in Figure 9 (b). Their $\rm R\sim
22$ magnitudes place them at the faint end of absorption line systems at the
corresponding redshifts.

Figure 9: (Top) Average spectrum of 6 E+A galaxies at $0.68\leq z\leq 1$. The
Balmer series $\rm H\delta$, $\rm H+H\epsilon$, H8, H9, H10, and H11 is very
promintent and $\rm<D(4000)>=1.40$. (Botton) Average spectrum of 4 galaxies in
the intermediate redshift range $0.4\leq z\leq 0.5$ with E/S0 morphological
type, showing a poststarburst E+A spectrum with still some star formation.
$\rm<EQW([OII])>\approx 3.5\AA$ and $\rm<D(4000)>=1.41$. Probably this
spectrum would have been classified as E+A at lower resolution.

###  5.2 Absorption-line galaxies as function of luminosity at $z=0.29$

Having tested bright absorption galaxies at various redshifts (with cut-off at
$\rm M_{R}=-18.8$), we now turn to faint absorption galaxies in the cluster at
$<z>=0.29$ by combining the spectra of the 10 faintest galaxies without
emission lines. Their average R-band magnitude is $\rm R=22$, which at a
distance modulus of 40.18 corresponds to $\rm M_{R}=-18.2$, and the faintest
object has $\rm M_{R}=-17.44$. Their mean indexes, $\rm D(4000)=1.55\pm 0.01$;
$\rm H\delta=-2.27\pm 0.04$, measured on the spectrum shown in Figure 10, are
consistent with a younger age than absorption-line galaxies with $\rm
M_{R}\leq-18.8$ (Table LABEL:D4000) in the same cluster. This is in agreement
with the well known evidence that the stellar populations in absorption
systems tend to be younger in low mass galaxies than in the more massive ones
(e.g. Renzini, 2006). The index values are in fact very close to those of our
absorption systems at z = 0.8 (Table LABEL:D4000) which, by selection effects,
are bright (Table LABEL:magnitudes and Figure 7).

The SSP models indicate that on average about 80% of the stars in the 10
faintest galaxies are younger than 2.5 Gyr (Table LABEL:poptable), i.e. were
born at $z<1$. In comparison, 80% of the stars contributing to the spectrum of
the brightest absorption galaxies in the cluster are older than 2.5 Gyr (Table
LABEL:poptable). To illustrate the spectral differences between bright and
faint systems at $z=0.29$, and the striking similarity between the spectra of
faint galaxies at $z=0.29$ and those of bright galaxies at $z=0.8$, we have
plotted in Figure 10 the average spectra of the 10 brightest and the 10
faintest absorption systems at $z=0.29$, and the average spectrum of
absorption galaxies at $z=0.82$. The effect of downsizing, (in the present
case the so-called ’archeological dowsizing’) where star formation shifts from
high mass galaxies at high redshifts, to low mass galaxies at low redshifts is
clearly exemplified in this figure.

Figure 10: Normalized spectra of the 10 brightest (in red) and the 10 faintest
(in blue) absorption-line galaxies in the cluster at $z=0.29$, and the full
sample of absorption-line systems at $z=0.82$ (in cyan).

At a redshift of $z\sim 0.8$ (i.e. $\sim 4$ Gy earlier), the red-sequence of
our unrelaxed (merging central system; elongated intra-cluster light and
galaxy distribution) cluster at z=$0.29$ was already in place, but was
truncated at brighter magnitudes because the faint absorption-line galaxies
were still copiously forming stars. This seems consistent with the observation
that some clusters at $z\simeq 1$ have red sequences truncated at faint limits
(Kodama et al., 2004; Koyama et al., 2007), and supports the picture of an
environmental dependence of red-sequence truncation presented by Tanaka et al.
(2005). This is also in agreement with scenarios where the final assembling of
the red-sequence can be observed well below $z=1$ (Faber et al., 2007).

As discussed above, the strict definition of E+A galaxy requires a mix of an
E-type spectrum with at least 25% A stars and no traces of star formation,
which in our sample implies no emission lines with equivalent widths larger
than 2-3Å. With this definition our $z=0.29$ cluster contains only one E+A
galaxy while the dense layers at $z\sim 0.4$, where the red-sequence is
already in place (layer in Figure 7), contains none. However, both in the
cluster and in the intermediate redshift layers we find plenty of galaxies
with early type morphologies, A stars, and very weak emission lines. In the
next section we present a closer look at these low-ionization emission line
galaxies.

###  5.3 Galaxy evolution and low-ionization emission-line galaxies (LINERs).

In an extensive work based on the SDSS survey, Yan et al. (2006) determined
the extent to which [OII] emission produced by mechanisms other than recent
star formation introduces biases in galaxy evolution studies based upon [OII]
only. They showed that the $\rm[OII]/H\beta$ ratio separates LINERs from star-
forming galaxies, while $\rm[OIII]/[OII]$ and $\rm[OIII]/H\beta$ separate
Seyferts from LINERs and star-forming galaxies. Using the classification
scheme of Yan et al. (2006) we divided our spectra in 3 main classes: LINERs,
with clearly detected [OII], but no ($3\sigma$) detection of [OIII] and $\rm
H\beta$ in emission after subtracting an E+A profile; Seyferts, with
$\rm[OIII]/H\beta\geq 3$; and star-forming galaxies, which are the objects
with clearly detected [OII] that are neither Seyferts nor LINERs after
subtracting an E+A profile. Typical spectra of low-ionization objects, star-
forming galaxies and Seyferts are shown in Figure 11.

Figure 11: Typical average spectra of low-ionization objects, star-forming
galaxies and Seyferts from the sample in the $<z>=0.415$ layer.

####  5.3.1 The impact of LINERs in our previous results on red emission-line
galaxies

Because the spectral coverage in a rather large fraction of our objects at
$<z>=0.68$ and higher is truncated below $\rm 5000\AA$ in the rest frame, we
applied our classification scheme only to objects in the range $0.29\leq z\leq
0.65$. To extract $\rm H\beta$ in emission we built a series of E+A models,
combining an observed E spectrum with different fractions of an A stellar
template, ranging form $0.05\%$ to $80\%$ of the total luminosity. To
determine the best-fit model we minimized the continuum slope of the
difference between the spectrum and the E+A model. Thus, in the range
$0.35-0.55$ our sample contains 23% LINERs, 51% star-forming galaxies, 8%
Seyferts, and 14% uncertain types. The layer at $z=0.63$ has 18% LINERs, 50%
star-forming galaxies, 7% Seyferts, 13% uncertain types and 11% of truncated
spectra. Altogether, the fraction of LINERs among emission-line galaxies up to
$z=0.65$ in our pencil beam is $\rm\approx 22\%$. With an average
$\rm<D(4000)>=1.39\pm 0.18$. LINERs at $z\leq 0.65$ have a potentially
significant impact on the conclusions of Giraud et al. (2010) about the
evolution of red emission-line galaxies. To quantify this impact, we have
subtracted all LINERs from the sample of emission-line spectra in the $z=0.43$
bin, determined the new blue-to-red partition (as in Giraud et al. 2010;
section 5.1), and computed a new average spectrum for the red galaxies. This
(also cleaned of rare red Seyferts) is shown in Figure 12 where it is compared
with the mean red spectrum at $z=0.9$. We find that the differences in
continuum slope and D(4000) between $<z>=0.43$ and $<z>=0.9$ is reduced by a
factor of 2/3. The main difference between red galaxies with LINERs and those
without is the presence of young stellar population.

Figure 12: Average spectra of red emission-line galaxies after subtracting
early-type LINERs and galaxies with diluted star formation (and rare Seyferts)
from the sample in the $<z>=0.43$ bin and recalculating the median blue-to-red
partition, and of the red half of emission-line galaxies at $<z>=0.9$. The
spectra at $<z>=0.9$ were not classified because $\rm H\beta$ and [OIII] are
missing in most cases.

####  5.3.2 Early-type LINERs

The fraction of nearby early-type galaxies hosting bona-fide (i.e. nuclear)
LINERs in the Palomar survey (Filippenko & Sargent, 1985; Ho et al. 1997a, )
was found to be $\sim 30\%$ (Ho et al. 1997b, ), but LINER-like emission line
ratios are also observed in extended regions (Phillips et al., 1986;
Goudfrooij et al., 1994; Zeilinger et al., 1996; Sarzi et al., 2006, and
references therein). A similar fraction of LINER-like ratios is found in the
SDSS at $0.05\leq z\leq 0.1$ in color-selected red galaxies (Yan et al.,
2006).

Because it is very difficult to disentangle early-type LINERs from spirals
with extended and diluted star formation by using only [OII] and $\rm H\beta$,
we make use of morphology to distinguish compact objects with low ellipticity
and profiles consistent with early type galaxies, from other morphologies:
apparent disks, high ellipticity, and irregular or distorted morphologies.
Images of early-type galaxies with low ionization spectra are shown in Figure
13.

Figure 13: Examples of early-type galaxies having low-ionization spectra, and
indicated redshifts.

Our visual early-type morphologies are the same as ZEST type T=1 (Scarlata et
al., 2007, Figure 4 (b), (c), (d)). In the $<z>=0.43$ bin we find that 92% of
the galaxies classified as star-forming objects have morphologies inconsistent
with early-types. At $<z>=0.43$ and in the $z=0.63$ layer, about half of the
LINERs have compact morphology while the other half are mainly bulge-dominated
disk galaxies, or “early disks” of ZEST type T=2.0 ((Bundy et al, 2009, Figure
4)). At $z=0.29$ all LINERs have disks. The spectra of galaxies with apparent
disks have an extended [OII] emission suggesting that they do have extended
star-formation. Average spectra of 11 early-type galaxies (E) and 10 later
types (hereafter S) resulting from our morphological classification are shown
in Figure 14.

Figure 14: Median spectra of 11 early-type galaxies and of 10 galaxies with
later type morphology (S) with low ionization at $z=0.4-0.5$.

The absence of $\rm H\beta$ in the S sample suggests that $\rm H\beta$ in
emission resulting from star formation is diluted in $\rm H\beta$ in
absorption from A and older stars. The closest spectral comparison in the
atlas of galaxy spectra (Kennicutt, 1992) is with an Sb galaxy. The rather
strong $\rm H\beta$ in absorption in early-types (E) combined with [OII]
suggests either a low fraction of young stars or a mechanism of photo-
ionization other than young stars as in (Fillipenko, 2003; Ho, 2004, and
references therein). In fact, the recent work by the SEAGAL collaboration (Cid
Fernandes et al., 2010, and references therein) has shown that the majority of
galaxies with LINER spectra in the SDSS can be explained as retired galaxies,
that is, galaxies that have stopped forming stars but still contain
appreciable amounts of gas that is being photoionized by intermediate-aged
post-AGB stars. In fact, the SEAGAL models with no young stars, but with
significant populations of 100Myr-1Gyr stars resemble remarkably well our
average LINER spectrum shown in Figure 11.

We calculated population synthesis models for our average spectra of LINERs
with early-type and late-type morphologies. The results, shown in Figure 15
and Table LABEL:earlylinerpop, indicate that both early-type and late-type
LINERs have significant populations of young and intermediate age stars, but
late-type (S) LINERs have much younger populations. In fact, the residuals of
the S-LINER fit show $\rm H\beta$ in emission stronger than [OIII], consistent
with the idea that they are red spirals with diluted star formation.

Table 10: Stellar population properties of an average of LINERs with early-type morphology and with morphology of later types Type | log Age | $\rm\chi^{2}$
---|---|---
| $<8$ | $8-8.7$ | $8.7-9$ | $9-9.4$ | $>9.4$ |
Early | 18.2 | 0 | 57.6 | 0 | 24.2 | 1.4
Later-type | 32.7 | 0 | 53.4 | 0 | 13.9 | 1.3

Figure 15: Spectral fitting with SSP models for an average spectrum of LINERs
with early-type morphology (left), and with morphology of later type (right).

Thus, our results are consistent with the interpretation that most early-type
LINERs at intermediate redshifts are in fact post-starburst galaxies, as
postulated by the SEAGAL collaboration for lower redshift objects. These
results indicate that LINERs and E+As depict the quenching phase in the
evolution of galaxies massive enough to retain significant amounts of gas
after the stellar-wind and supernova phases of the most massive stars.

###  5.4 The red limit of emission-line galaxies

At each $z$ we have selected galaxies with the reddest continuum (the reddest
quartile at each redshift bin) to construct the combined spectra of the red
envelope or red limit of emission line galaxies. Since we are working with
small numbers of galaxies, typically 5-10, it was necessary to combine the
samples at $z=0.82$ and $z=0.99$ to improve statistics. Nevertheless, because
our red emission-line galaxies are rather luminous, the combined spectra still
have high continuum S/N ratios (Table LABEL:redest). The common parts of the
red envelopes of spectra at $<z>=0.29$, $<z>=0.43$, $<z>=0.65$ are similar,
while the red limit at $<z>=0.9$ has noticeably stronger UV continuum. The
spectra in different bins are shown in Figure 7 of Giraud et al. (2010). Both
the continuum and the indexes of the red limit at $z\leq 0.65$ (i.e. $\rm
D(4000)\sim 1.35-1.45;EQW([OII])\sim 4-8$), are typical of nearby spirals with
prominent bulges and low star formation (Kennicutt, 1992; Kinney et al., 1996;
Balogh et al., 1999), or early-type LINERs. Up to $z\sim 0.7$ the populations
of red spirals and early-types can be well separated by their morphology. The
higher UV continuum and lower $\rm D(4000)$ of the limit spectrum at $<z>=0.9$
indicate that such red objects become rare at $z\geq 0.68$ in our sample.

Absorption systems have (by definition) already lost enough gas to suppress
any detected star formation by the time they first appear in our sample at
$z\simeq 1$. At $z=0.8-1$ emission-line galaxies in our sample are found to
have very strong star formation, which declines at lower $z$, the reddest
quartile being bluer than at lower $z$. Therefore the evolutionary paths of
bright absorption and emission systems might have been more separated at
$z\simeq 1$ than at lower redshift suggesting two different physical processes
of different time scales. In one we have early-type LINERs and E+A galaxies
that define the ”entrance gate” to the red sequence of passively evolving
galaxies. In the other we have red spirals with diluted star formation, that
are in a final phase of smooth star formation, possibly of a “main sequence”
(Noeske et al., 2007).

In Section 5.3 we found a large fraction of LINERs in layers at intermediate
$z$. More precisely, in the volume-limited range $0.35\leq z\leq 0.65$, we
find, gathering the counts of Section 5.3, that LINERs are $\rm 23\%$ of all
early-type galaxies with measured redshifts.

Table 11: Equivalent width of [OII], 4000 Å break amplitude, $\rm H\delta$ index, and the G-step of the red envelope of emission-line galaxies. The continuum S/N ratios are given in the last column.  $<z>$ | EQW([OII]) | D(4000) | $\rm EQW(H\delta)$ | G step | $\rm S/N$
---|---|---|---|---|---
0.29 | $4.2\pm 0.3$ | $1.35\pm 0.07$ | $-1.8\pm 0.3$ | $1.224\pm 0.017$ | $\rm 19$
$0.43$ | $8.5\pm 0.2$ | $1.39\pm 0.06$ | $-2.5\pm 0.2$ | $1.268\pm 0.014$ | $\rm 24$
$0.65$ | $8.3\pm 0.2$ | $1.44\pm 0.06$ | $-3.0\pm 0.2$ | $1.273\pm 0.012$ | $\rm 29$
$0.9$ | $9.0\pm 0.3$ | $1.30\pm 0.07$ | $-3.9\pm 0.2$ | - | $\rm 18$

##  6 Summary and Conclusions

We have presented a catalogue of galaxy spectra in a pencil beam survey of
$\sim 10.75^{\prime}\times 7.5^{\prime}$, and used these data to make an
analysis of the spectral energy distribution of a magnitude limited sample up
to $z\sim 1$, concentrating on absorption and low ionization emission-line
systems. The redshift range has been divided in bins centered on the
structures that were detected in the (RA, Dec, $z$) pseudo-volume, and
corresponding to cosmic time slices of $\rm\sim 1Gyr$. Our sample is
reasonably complete for galaxies brighter than $\rm M_{R}=-18.8$ up to
$z\approx 0.5$; at $z\geq 0.75$ the cutoff is at -20.5.

From this analysis we reach the following conclusions:

  1. 1.

We confirm in our pencil-beam sample the well known result Hamilton (1985)
that absorption-line galaxies do not show significant variations in their
continuum energy distributions up to $z=0.6$, and a moderate decrease of the
4000 Å break amplitude of 5% at $z\sim 0.65$, 7% at $z\sim 0.82$, and up to
15% at $z\sim 1$. Using stellar population synthesis models we find that
absorption-line galaxies at $z\geq 0.65$ show more than 50% of stars younger
than 2.5Gyrs, while those at $z\geq 0.8$ had star formation as recently as
1Gyr ago. This suggests that the red sequence is still in a buildup phase at
$z\leq 1$.

The faint absorption-line galaxies in our dynamically young cluster at
$z=0.29$ have indexes similar to those of bright absorption-line systems at
$z=0.8$, suggesting that faint galaxies without emission lines tend to be
younger than more massive galaxies with similar spectra. Our population
synthesis models indicate that about 50% of the stars contributing to the
luminosity of faint absorption-line galaxies at z = 0.29 were formed at $z<1$.
This is consistent with cases of truncated red sequences observed in some
high-$z$ clusters and suggests that clusters with truncated red-sequences may
be dynamically young.

  2. 2.

Combining simple emission-line diagnostics with galaxy morphology we identify
a significant population of early-type LINERs at $0.35\leq z\leq 0.65$. In
that redshift range early-type LINERs constitute about 23% of all early-types
galaxies, a much larger fraction than E+A post-starburst galaxies. However,
our population synthesis models show that early-type LINERs contain
substantial populations of intermediate age stars that can easily explain the
observed line emission, as recently proposed by Cid-Fernandes et al. (2010).
This led us to conclude that most LINERs in our sample are in fact post-
starburst galaxies.

  3. 3.

The red limit in the spectral energy distribution of emission-line galaxies at
$z\leq 0.65$ is typical of bulge-dominated spirals with moderate star
formation, and of early-type LINERs. Thus, early-type LINERs and E+As define
the “entrance gate” of the red sequence of passively evolving galaxies, while
bulge-dominated spirals have diluted star formation.

###### Acknowledgements.

EG thanks the hospitality of ESO and Universidad Catolica in Santiago during
the initial phase of this work. JME thanks the hospitality of Nanjing
University during the initial phase of this research. QGU would like to
acknowledge the financial support from the China Scholarship Council (CSC),
the National Natural Science Foundation of China under grants 10878010,
10221001, and 10633040, and the National Basic Research Program (973 program
No. 2007CB815405). HQU thanks partial support from FONDAP “Centro de
Astrofísica”. PZE acknowledge a studentship from CONICYT. We thank S. di
Serego Alighieri for reading a preliminary version of the manuscript and for
his suggestions, and R. Cid-Fernandes for fruitful discussions.

## References

  * Adelman-McCarthy et al (2006) Adelman-McCarthy, J.K., et al, 2006, ApJS 162, 38
  * Baldwin et al. (1981) Baldwin, J.A., Phillips, M.M., & Terlevitch, R., 1981, PASP 93, 5
  * Balogh et al. (1999) Balogh, M. L., Morris, S. L., Yee, H. K. C., Carlberg, R. G., & Ellingson, E., 1999, ApJ 527, 54
  * Baugh et al. (1996) Baugh, C.M.,Cole, S., & Frenk, C.S., 1996, MNRAS.283, 1361
  * Bell et al. (2005) Bell, E. F., et al, 2005, ApJ 625, 23
  * Blake et al. (2004) Blake, C., et al., 2004, MNRAS 355, 713
  * Bundy et al (2009) Bundy, K., et al., 2009, astro-ph 0912.1077v1
  * Bruzual & Charlot (2003) Bruzual G., & Charlot S., 2003, MNRAS, 344, 1000
  * Cid Fernandes et al. (2004) Cid Fernandes R. , et al., 2004, MNRAS, 355, 273
  * Cid Fernandes et al. (2005) Cid Fernandes R., Mateus A., Sodre L., Stasinska G., Gomes J., 2005, MNRAS 358, 363
  * Cid Fernandes et al. (2010) Cid Fernandes R., et al., 2010, MNRAS 403, 1036
  * Cooper et al. (2006) Cooper, M. C. et al, 2006, MNRAS 370, 198
  * Cucciati et al. (2006) Cucciati, O., et al (the VVDS Team), 2006, A&A 458, 39
  * Dickinson et al. (2003) Dickinson, M., Papovich, C., Fergusson, H.C., Budavari, T., 2003, ApJ 587, 25
  * Dressler et al. (1999) Dressler, A., et al, 1999, ApJS 122, 51
  * Dressler et al. (1997) Dressler, A., et al, 1997, ApJ 490, 577
  * Dressler & Gunn (1983) Dressler A., & Gunn J.E., 1983 ApJ 270 7
  * Dressler (1980) Dressler, A., 1980, ApJ 236, 351
  * Faber et al. (2007) Faber, S. et al, 2007, ApJ 665, 265
  * Faure et al. (2007) Faure, C., Giraud, E., Melnick, J., Quintana, H., Selman, F., & Wambganss, J., 2007, A&A, 463, 833
  * Filippenko & Sargent (1985) Filippenko, A.V., & Sargent, W.L., 1985, ApJS 57, 503
  * Fillipenko (2003) Filippenko, A.V., 2003 in ASP Conf. Ser. 290 Active Galactic Nuclei: From Central Engine to Host Galaxy, 369
  * (23) FORS1+2 User Manual VLT-MAN-ESO-13100-1543 Issue 4, 2005
  * FIMS (2006) FORS1+2 FIMS Manual VLT-MAN-ESO-13100-2308 Issue 78, 2006
  * Franzetti et al. (2007) Franzetti, P., et al (the VVDS Team), 2007, A&A 465, 711
  * Garilli et al. (2008) Garilli, B., et al (the VVDS Team), 2008, astro-ph 0804.4568
  * Giraud et al. (2010) Giraud E., Melnick J., Gu Q.S., et al., 2010, in press
  * Goto (2007) Goto, T., 2007, MNRAS 377, 1222
  * Goudfrooij et al. (1994) Goudfrooij, P., et al., 1994, A & AS 105, 341
  * Gu et al. (2006) Gu Q. et al. 2006, MNRAS, 366, 480
  * Hamilton (1985) Hamilton, D., 1985 ApJ, 297, 371
  * Hammer et al. (2005) Hammer, F., et al, 2005, A&A 430, 115
  * (33) Ho, L.C.W., et al., 1997a, ApJS 112, 315
  * (34) Ho, L.C.W., et al., 1997b, ApJ 487, 568
  * Ho (2004) Ho, L.C.W,. 2004, in Coevolution of Black Holes and Galaxies, 293
  * Huang & Gu (2009) Huang, S., & Gu Q., 2009, MNRAS 398, 165
  * Kauffmann et al. (2003) Kauffmann, G., et al, 2003, MNRAS 341, 33
  * Kauffmann et al. (2003) Kauffmann, G., et al, 2003, MNRAS 346, 1055
  * Kennicutt (1992) Kennicutt, R.C.., 1992, ApJS 79, 255
  * Kinney et al. (1996) Kinney, A.L., Calzetti, D., Bohlin, R.C., McQuade, K., Storchi-Bergmann, T., & Schmitt, H.R., 1996, ApJ 467, 38
  * Kodama et al. (2004) Kodama, T., et al, 2004, MNRAS 350, 1005
  * Koyama et al. (2007) Koyama, Y., Kodama, T., Tanaka, M, Shimasaku, K., & Okamura, S., 2007, MNRAS 382, 1719
  * Lin et al. (2008) Lin, L., et al., 2008, ApJ 681, 232
  * Melnick & Sargent (1977) Melnick, J., & Sargent, L. W. L., 1977, ApJ 215, 401
  * Melnick et al. (1999) Melnick, J., Selman, F., & Quintana, H., 1999, PASP 111, 1444
  * Neinstein et al. (2006) Neinstein, E., van den Bosch, F.C., & Dekel, A., 2006, MNRAS 372, 933
  * Noeske et al. (2007) Noeske, K.G., et al., 2007, ApJ 660, L43
  * Norton et al. (2001) Norton, S.A., Gebhardt, K., Zabludoff, A.I., & Zaritsky, D., 2001, ApJ 557, 150
  * Pei & Fall (1995) Pei, Y. C., & Fall, S. M., 1995, ApJ 454, 69
  * Phillips et al. (1986) Phillips, M.M., et al., 1986, AJ 91, 1062
  * (51) p2pp, 2006: http://www.eso.org/observing/p2pp/
  * Postman et al. (2005) Postman, M., et al, 2005, ApJ 623, 721
  * Renzini (2006) Renzini, A., 2006, ARA&A 44, 141
  * Renzini (2007) Renzini, A., 2007, ASPC 390, 309
  * Sarzi et al. (2006) Sarzi, M., et al., 2006, MNRAS 366, 1151
  * Scarlata et al. (2007) Scarlata, C., et al., 2007, ApJS 172, 406
  * Scoville et al. (2007) Scoville, N., et al, 2007a, ApJS 172, 1
  * Scoville et al. (2007) Scoville, N., et al, 2007b, ApJS 172, 150
  * Smith et al. (2005) Smith, G. P., Treu, T., Ellis, R. S., Moran, S. M., & Dressler, A., 2005, ApJ 620, 78
  * Tanaka et al. (2005) Tanaka, M., et al, 2005, MNRAS 362, 268
  * Toledo et al. (2010) Toledo, I., et al. 2010 in preparation
  * (62) Tonry, J., & Davis, M., 1979, AJ 84, 1511.
  * Tran et al. (2007) Tran, K.-V. H., et al, 2007, ApJ 661, 750
  * Tran et al. (2004) Tran, K.-V. H., et al, 2004, ApJ 609, 683
  * Tran et al. (2003) Tran, K.-V. H,. et al, 2003, ApJ 599, 865
  * Veilleux & Osterbrock (1987) Veilleux, S., & Osterbrock, D.E., 1987, ApJS 63, 295
  * Weiner et al. (2005) Weiner, B.J., et al, 2005, ApJ 620, 595
  * Weiner et al. (2006) Weiner, B.J., et al, 2006, ApJ 653, 1027
  * Wild et al (2009) Wild V. et al. 2009 MNRAS 395 144
  * Yan et al. (2009) Yan, R., et al., 2009, MNRAS 398, 735
  * Yan et al. (2006) Yan, R., Newman, J.A., Faber, S., Konidaris, N., Koo, D., & Davis, M., 2006 ApJ 648 281
  * Yang et al. (2008) Yang Y., Zabludoff, A.I., Zaritsky, D., & Mihos, J.C., 2008, ApJ 688, 945
  * Zeilinger et al. (1996) Zeilinger, W.W., et al., 1996, A & AS 120, 257


# Image Inpainting Models are Effective Tools
for Instruction-guided Image Editing
Solution for GenAI Media Generation Challenge Workshop @ CVPR

Xuan Ju1,2, Junhao Zhuang1, Zhaoyang Zhang1∗, Yuxuan Bian1,2, Qiang Xu2, Ying
Shan1
1ARC Lab, Tencent PCG, 2The Chinese University of Hong Kong ∗Project Lead
https://github.com/TencentARC/BrushNet/tree/main/InstructionGuidedEditing

###### Abstract

This is the technique report for the winning solution of the CVPR2024 GenAI
Media Generation Challenge Workshop’s Instruction-guided Image Editing track.
Instruction-guided image editing has been largely studied in recent years. The
most advanced methods, such as SmartEdit and MGIE, usually combine large
language models with diffusion models through joint training, where the former
provides text understanding ability, and the latter provides image generation
ability. However, in our experiments, we find that simply connecting large
language models and image generation models through intermediary guidance such
as masks instead of joint fine-tuning leads to a better editing performance
and success rate. We use a 4-step process IIIE (Inpainting-based Instruction-
guided Image Editing): editing category classification, main editing object
identification, editing mask acquisition, and image inpainting. Results show
that through proper combinations of language models and image inpainting
models, our pipeline can reach a high success rate with satisfying visual
quality.

##  1 Introduction

With the rapid development of diffusion models, the field of text-guided image
generation [7, 19, 13, 15] has seen unprecedented progress in creating images
with superior quality [18], diversity [3], and adherence to text guidance
[14]. However, in image editing tasks, which provide a source image and an
editing instruction as input and expect a target image as output, we do not
observe such success. This implies that the language understanding ability and
image generation ability are not fully explored in editing tasks.

In the aim of applying image generation capabilities to image editing,
previous methods have attempted two strategies: (1) collecting paired “source
image-instruction-target image” data and fine-tuning diffusion models for
editing tasks (e.g., InstructPix2Pix [1] and InstructDiffusion [5]), and (2)
jointly fine-tuning Large Language Models (LLMs) and diffusion models to endow
the diffusion models with a stronger understanding of text (e.g., SmartEdit
[8] and MGIE [4]). For the former strategy, due to the difficulty of
collecting paired manually edited data, the training data are usually
generated by LLMs and inference-based image editing methods (e.g., Prompt-to-
Prompt [6] and Masactrl [2]). Due to the low success rate and unstable editing
quality of these inference-based image editing methods [11], the collected
dataset is usually noisy and unreliable, which lead to an unsatisfying
performance of the trained image editing model. For the latter strategy, a
joint training of the LLMs and diffusion model usually make it hard to fully
use the text understanding capability of LLMs. Although SmartEdit and MGIE
have achieved better performance than previous solutions, we find that they
still do not fully optimize the potential of LLMs and diffusion.

This technique report provides a different solution, connecting large language
models and image generation models simply through intermediary guidance (e.g.,
edit objects and masks), and is the winning solution for The GenAI Media
Generation Challenge (MAGIC). By detracting two models apart in an agent
architecture, we find it easier to fully leverage the capabilities of both.
Specifically, we use a 4-step process IIIE (Inpainting-based Instruction-
guided Image Editing): (1) editing category classification, (2) main editing
object identification, (3) editing mask acquisition, and (4) image inpainting.
Step (1)-(3) use LLMs and detection model to determine the editing type, edit
object, edit masks, and target prompt. Then, step (4) perform image editing in
the way of image inpainting, which fully use generation ability. In this way,
step (1)-(3) use LLMs to extract information in instruction and summarize them
to intermediary guidance that can be used by diffusion models.

Visual results show that the proposed IIIE substantially surpass previous
instruction-guided image editing methods and other solutions in MAGIC
considering visual quality and instruction faithfulness.

##  2 MAGIC

The MAGIC hosts two challenge tracks: (1) text-to-image generation and (2)
text-guided image editing. This technique report presents the solution for the
second track, text-guided image editing. We list the instructions here:

Guidelines For text-guided image editing, we test the capacity of the model to
change a given image’s contents based on some text instructions. The specific
type of instructions that we test for are the following: • Addition: Adding
new objects within the images. • Remove: Removing objects • Local: Replace
local parts of an object and later the object’s attributes, i.e., make it
smile • Global: Edit the entire image, i.e., let’s see it in winter • Inpaint:
Alter an object’s visual appearance without affecting its structure •
Background: Change the scene’s background

Evaluation Protocol We leverage both human and automated evaluations. In
human-based evaluations, we use human annotators. We mainly evaluate the
following aspects: • Edit Faithfulness - whether the edited image follows the
editing instruction • Content Preservation - whether the edited image
preserves the regions of the original image that should not be changed •
Overall Instruction Following - considering both edit faithfulness and content
preservation, whether the edited image is artifact-free, keeping the core
visual features of the original image, etc On automatic evaluation, similar to
the text to image track, we will leverage existing methods that have developed
automatic metrics to help in assessing the outputs of the image based on the
prompt and instruction. To determine winners, we use automatic evaluation to
help prune the total number of entries to 10 finalists. At 10, we would use
human annotation and evaluation to determine the final winners.

More information about the detailed workshop information can be found at the
official website page: https://gamgc.github.io/.

Figure 1: The pipeline of our proposed 4-step image editing process ITIE.

##  3 Method

Previous instruction-guided image editing methods include two categories: (1)
diffusion model finetuning [1, 5], and (2) jointly LLMs and diffusion model
[8, 4]. These methods either not include LLMs in the model, or use joint fine-
tuning of LLMs and diffusion models. Both of these strategies can not fully
unleash the capabilities of LLMs, leading to a weak understanding of
instructions. Consequently, these methods show low success rates and
unsatisfying results. Contrary to these methods, we find that a simple tool-
based combination of LLMs and diffusion models can lead to a much better
visual results, coming from a full utilization of the language understanding
ability of LLMs.

In this competition, we propose a 4-step process for instruction-based image
editing, IIIE (Inpainting-based Instruction-guided Image Editing), as shown in
Fig. 1. Firstly, we use GPT4-o to categorize the current editing instructions
into one of the editing categories: Local Edit, Background Edit, Global Edit,
Addition, and Remove. Local Edit includes local changes such as replacing
object (e.g., change a cat to a dog) and change the attribute of an object
(e.g., change color or texture). For example, Fig. LABEL:fig:teaser row 1 show
two examples of Local Edit. Background Edit means changing the background and
remain the main object unchanged. For example, Fig. LABEL:fig:teaser row 2
column 1 and row 4 column 2 show two examples of Background Edit. Global Edit
transfer the overall style of an image. For example, Fig. LABEL:fig:teaser row
4 column 1 show one example of Global Edit. Addition and Remove separately add
and remove object from an image. For example, Fig. LABEL:fig:teaser row 3 show
two examples of adding object, and row 2 column 2 shows one example of
removing object. We classify editing category to these 4 categories since they
can cover most editing instruction types, and requires different operations in
editing.

Then, in step 2, we find the main editing object by making further
conversations with GPT4-o based on the editing category. For example, editing
instruction “Make the horse into a unicorn” has a main object of “horse”. For
editing categories of global edit and addition that do not contain an editing
object, we leave the editing object blank. After that, we use Grounded-SAM
[17] combined with GPT4-o to obtain the editing mask in step 3. Specifically,
we generate image background as mask for Background Edit. For Local Edit and
Removing, we use the main object of step 2 as the input of Grounded-SAM to
locate the editing mask. For addition editing category, we use the visual
ability of GPT4-o and get a possible location of adding the object. For Global
editing, the mask is default as the whole image.

Finally, in step 4, we use image generation model to perform image editing
based on a target prompt generated by GPT4-o. For Background Edit, Local Edit,
Remove, and Addition, we use image inpainting model BrushNet [10] combined
with PowerPaint [21] to inpaint the masked region based on the target prompt.
For Global Edit, we use InfEdit [20] to change the global style. We find that
a direct utilization of the diffusion model by giving conditions of mask and
text prompt can fully leverage the capabilities of these models, thus leads to
high-quality generation results.

##  4 Experiment Results

Method | Rank | Edit | Content | Overall Instruction
---|---|---|---|---
Faithfulness | Preservation | Following
IIIE | 1 | 0.51 | 0.78 | 0.80
LEdits++ | 2 | 0.46 | 0.64 | 0.74
Tasvir | 3 | 0.40 | 0.49 | 0.62
Table 1: Comparison of IIIE and the other two winning solutions. The score is
calculated with an average of 1.2k images in MAGIC benchmark. A bigger score
means a better user preference. Bold stands for the best results.

Visualization comparison in Fig. LABEL:fig:teaser show a higher success rate
and visual quality of IIIE compared to previous methods. In the MAGIC
workshop, a benchmark of 1.2k images is used for evaluation and 3 winning
solutions are announced. The benchmark measure the quality of different
editing methods using user study on instruct following, edit fidelity, and
content preservation. 3 raters are involved in each job and the majority vote
is taken as the final results. Each image is rated with 1 or 0 for each
metric. Results of the three winning solution of this competition is shown in
Tab. 1. IIIE show better score on all three metrics compared to the other two
winning solutions. We have made our code and edited results publicly available
in the hope of our findings can offer some insights for relevant field [12, 9,
16].

In conclusion, in this technique report presents the winning solution for the
CVPR2024 GenAI Media Generation Challenge’s Instruction-guided Image Editing
track. We show that without cumbersome fine-tuning or training, a simple
combination of LLMs and text-to-image diffusion model can lead to a image
editing results with better performance and higher success rates than previous
methods.

## References

  * [1] Tim Brooks, Aleksander Holynski, and Alexei A Efros.  Instructpix2pix: Learning to follow image editing instructions.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392–18402, 2023.
  * [2] Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng.  Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing.  In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 22560–22570, 2023.
  * [3] Xiaoliang Dai, Ji Hou, Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, et al.  Emu: Enhancing image generation models using photogenic needles in a haystack.  arXiv preprint arXiv:2309.15807, 2023.
  * [4] Tsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang, Yinfei Yang, and Zhe Gan.  Guiding instruction-based image editing via multimodal large language models.  arXiv preprint arXiv:2309.17102, 2023.
  * [5] Zigang Geng, Binxin Yang, Tiankai Hang, Chen Li, Shuyang Gu, Ting Zhang, Jianmin Bao, Zheng Zhang, Houqiang Li, Han Hu, et al.  Instructdiffusion: A generalist modeling interface for vision tasks.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12709–12720, 2024.
  * [6] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or.  Prompt-to-prompt image editing with cross attention control.  arXiv preprint arXiv:2208.01626, 2022.
  * [7] Jonathan Ho, Ajay Jain, and Pieter Abbeel.  Denoising diffusion probabilistic models.  Advances in Neural Information Processing Systems (NIPS), 33:6840–6851, 2020.
  * [8] Yuzhou Huang, Liangbin Xie, Xintao Wang, Ziyang Yuan, Xiaodong Cun, Yixiao Ge, Jiantao Zhou, Chao Dong, Rui Huang, Ruimao Zhang, et al.  Smartedit: Exploring complex instruction-based image editing with multimodal large language models.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8362–8371, 2024.
  * [9] Xuan Ju, Yiming Gao, Zhaoyang Zhang, Ziyang Yuan, Xintao Wang, Ailing Zeng, Yu Xiong, Qiang Xu, and Ying Shan.  Miradata: A large-scale video dataset with long durations and structured captions.  arXiv preprint arXiv:2407.06358, 2024.
  * [10] Xuan Ju, Xian Liu, Xintao Wang, Yuxuan Bian, Ying Shan, and Qiang Xu.  Brushnet: A plug-and-play image inpainting model with decomposed dual-branch diffusion.  arXiv preprint arXiv:2403.06976, 2024.
  * [11] Xuan Ju, Ailing Zeng, Yuxuan Bian, Shaoteng Liu, and Qiang Xu.  Direct inversion: Boosting diffusion-based editing with 3 lines of code.  arXiv preprint arXiv:2310.01506, 2023.
  * [12] Xuan Ju, Ailing Zeng, Jianan Wang, Qiang Xu, and Lei Zhang.  Human-art: A versatile human-centric dataset bridging natural and artificial scenes.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 618–629, 2023.
  * [13] Xuan Ju, Ailing Zeng, Chenchen Zhao, Jianan Wang, Lei Zhang, and Qiang Xu.  Humansd: A native skeleton-guided diffusion model for human image generation.  In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15988–15998, 2023.
  * [14] Yanyu Li, Xian Liu, Anil Kag, Ju Hu, Yerlan Idelbayev, Dhritiman Sagar, Yanzhi Wang, Sergey Tulyakov, and Jian Ren.  Textcraftor: Your text encoder can be image quality controller.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7985–7995, 2024.
  * [15] Xian Liu, Jian Ren, Aliaksandr Siarohin, Ivan Skorokhodov, Yanyu Li, Dahua Lin, Xihui Liu, Ziwei Liu, and Sergey Tulyakov.  Hyperhuman: Hyper-realistic human generation with latent structural diffusion.  arXiv preprint arXiv:2310.08579, 2023.
  * [16] Xian Liu, Yinghao Xu, Qianyi Wu, Hang Zhou, Wayne Wu, and Bolei Zhou.  Semantic-aware implicit neural audio-driven video portrait generation.  In European conference on computer vision, pages 106–125. Springer, 2022.
  * [17] Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, et al.  Grounded sam: Assembling open-world models for diverse visual tasks.  arXiv preprint arXiv:2401.14159, 2024.
  * [18] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer.  High-resolution image synthesis with latent diffusion models.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10684–10695, June 2022.
  * [19] Jiaming Song, Chenlin Meng, and Stefano Ermon.  Denoising diffusion implicit models.  arXiv preprint arXiv:2010.02502, 2020.
  * [20] Sihan Xu, Yidong Huang, Jiayi Pan, Ziqiao Ma, and Joyce Chai.  Inversion-free image editing with natural language.  arXiv preprint arXiv:2312.04965, 2023.
  * [21] Junhao Zhuang, Yanhong Zeng, Wenran Liu, Chun Yuan, and Kai Chen.  A task is worth one word: Learning with task prompts for high-quality versatile image inpainting.  arXiv preprint arXiv:2312.03594, 2023.