The SIMPLE survey: observations, reduction, and catalog
We present the Spitzer IRAC/MUSYC Public Legacy Survey in the Extended CDF-South (SIMPLE), which consists of deep IRAC observations covering the 1,600 arcmin area surrounding GOODS-S. The limiting magnitudes of the SIMPLE IRAC mosaics typically are 23.8, 23.6, 21.9, and 21.7, at 3.6 m, 4.5 m, 5.8 m, and 8.0 m, respectively (5- total point source magnitudes in AB). The SIMPLE IRAC images are combined with the 10′ 15′ GOODS IRAC mosaics in the center. We give detailed descriptions of the observations, data reduction, and properties of the final images, as well as the detection and photometry methods used to build a catalog. Using published optical and near-infrared data from the Multiwavelength Survey by Yale-Chile (MUSYC), we construct an IRAC-selected catalog, containing photometry in , [3.6 m], [4.5 m], [5.8 m], and [8.0 m]. The catalog contains 43,782 sources with S/N at 3.6 m, 19,993 of which have 13-band photometry. We compare this catalog to the publicly available MUSYC and FIREWORKS catalogs and discuss the differences. Using a high signal-to-noise sub-sample of 3,391 sources with , we investigate the star formation rate history of massive galaxies out to . We find that at at least 30%% of the most massive galaxies ( ) are passively evolving, in agreement with earlier results from surveys covering less area.
Subject headings:catalogs – galaxies: evolution – galaxies: observations – galaxies: photometry – infrared: galaxies
Our understanding of galaxy formation and evolution has dramatically
increased through the rise of large and deep galaxy surveys that have
opened up the high-redshift universe for research. The best studied
high-redshift galaxies are arguably the Lyman break galaxies (LBGs)
that can be identified by their rest-frame UV colors (Steidel et
al. 1996; 1999). Although much has been learned from studying their
properties, LBGs are not representative for all high-redshift galaxy
Since they are based on selection in the rest-frame UV, optical surveys of high-redshift galaxies are heavily affected by dust obscuration and are not sensitive to old stellar populations. The rest-frame optical is less influenced by the contribution from young stars and dust and provides a more reliable means of tracing the bulk of the stellar mass at high redshift. For instance, near-infrared observations have uncovered a significant population of massive, red galaxies, particularly at high redshift (Elston, Rieke & Rieke 1988, Spinrad et al. 1997, Barger et al. 1999, Daddi et al. 2000, Franx et al. 2003, Labbé et al. 2003, Cimatti et al. 2004, van Dokkum et al. 2006).
With the arrival of the Spitzer Space Telescope and its Infrared Array Camera (IRAC; Fazio et al. 2004), constructing large surveys to study high-redshift galaxies has become even more attainable, since the IRAC wavelengths provide coverage of the rest-frame optical bands out to higher redshifts. Using deep IRAC observations at 4.5 m it is possible to trace the rest-frame -band out to a redshift 4.
|Program||area||channel||depth (AB mag)||S/N||integration time|
|GOODS-S||138 arcmin||3.6 m||26.15||3||23 hr|
|SIMPLE||1,600 arcmin||3.6 m||23.86||5||0.9-2.5 hr|
|S-COSMOS||2 deg||3.6 m||24.0||5||1200 s|
|SWIRE||60 deg||3.6 m||21.4||10||120-480 s|
The massive, red galaxies found at high redshift are important
test-beds for models of galaxy formation and evolution. To be able to
place constraints on the models we need a clear picture of the
evolution and star formation history of these massive galaxies. This
requires large, statistically powerful samples, or in other words,
surveys that extend over a great area and depth.
It is also critical to do these observations in areas that already have been observed at many wavelengths and ideally in areas that are accessible to future telescopes such as ALMA. The 30′ 30′ Extended Chandra Deep Field South (E-CDFS) is perfect in this sense as it is one of the most extensively observed fields available. There is a large set of ground-based data providing imaging (MUSYC (Gawiser et al. 2006, Quadri et al. 2007, Taylor et al. 2009b), COMBO-17 (Wolf et al. 2004), LCIRS, (McCarthy et al. 2001)), radio coverage (Miller et al. 2008), and spectroscopy (e.g., GOODS (VIMOS: Popesso et al. 2009, FORS2: Szokoly et al. 2004, Vanzella et al. 2008), MUSYC (Treister et al. 2009a), K20 (Cimatti et al. 2002), VVDS (le Fèvre et al. 2004)). The area has been targeted intensely from space too. There is HST ACS imaging from GEMS (Rix et al. 2004), observations from CHANDRA (Lehmer et al. 2005, Luo et al. 2008), XMM (PI: J. Bergeron), GALEX (Martin et al. 2005), and ultra deep multiwavelength coverage from the Great Observatories Origins Deep Survey (GOODS, Dickinson et al. 2003) in the central 10′ 15′. The rich multiwavelength coverage includes also deep 24, 70, and 160 m observations from the Far-Infrared Deep Extragalactic Legacy Survey (FIDEL).
In this context, we initiated Spitzer’s IRAC + MUSYC Public Legacy of the E-CDFS (SIMPLE), which aims to provide deep, public IRAC imaging of a 1,600 arcmin area on the sky. In this paper, we present the full IRAC data set, with an IRAC-selected multicolor catalog of sources with 13-band optical-to-infrared photometry (covering 0.3-8.0 m). The optical to near-infrared (NIR) data come from the Multiwavelength Survey by Yale-Chile (MUSYC; Taylor et al. 2009), which are publicly available
In addition to the study of massive galaxies, the SIMPLE survey can be used to analyze properties of active galactic nuclei (AGNs). Luminous optically unobscured AGN can be selected based on their IRAC colors (Lacy et al. 2005, Stern et al. 2005). In the case of dust-obscured AGNs, the energy absorbed at optical to X-ray wavelengths is later re-emitted in the mid-IR. AGNs should therefore by very bright mid-IR sources (e.g., Daddi et al. 2007, Alexander et al. 2008, Donley et al. 2008). The SIMPLE survey has proved valuable in this context (Cardamone et al. 2008, Treister et al. 2009a, 2009b) and the full photometric data set in the E-CDFS can provide strong constraints on the redshifts, masses, and stellar populations of the host galaxies. One example is the study by Luo et al. (2010), who use the SIMPLE survey (among other data sets) to improve photometric redshifts of X-ray AGN hosts. Their work also shows the value of the SIMPLE survey regarding the identification of X-ray selected AGNs. These sources can be very difficult to identify at faint counterpart magnitudes. Luo et al. (2010) quantify a counterpart recovery rate and found that the SIMPLE 3.6 m data score high in that respect (see their Table 1). To conclude the list of SIMPLE applications, IRAC observations have been useful in investigating the stellar populations of Ly-emitting galaxies (Lai et al. 2008).
Here, we focus on the observations, data reduction processes, and the construction of the catalog. This paper is structured as follows. In Section 2, we describe the observations with IRAC. Section 3 explains the reduction processes and the combined IRAC mosaics. The ancillary data from the MUSYC and FIDEL surveys that we use are described in Section 4. Source detection and photometry are discussed in Section 5. In Section 6.1, we examine our photometric redshifts by comparing them to a compilation of spectroscopic redshifts. The catalog parameters are listed and explained in Section 7 and Section 8 describes the comparison of the SIMPLE catalog with two other catalogs of the (E-)CDFS.
In a recent paper (Damen et al. 2009a), we used the SIMPLE catalog to study the evolution of star formation in massive galaxies. Those results were based on a preliminary version of the catalog and we update the conclusions in Section 9. Finally, Section 10 provides a summary of the paper.
Throughout the paper we assume a CDM cosmology with , , and km s Mpc. All magnitudes are given in the AB photometric system. We denote magnitudes from the four Spitzer IRAC channels as , , , and , respectively. Stellar masses are determined assuming a Kroupa (2001) initial mass function (IMF).
The SIMPLE IRAC Legacy survey consists of deep observations with IRAC (Fazio et al. 2004) covering the
1600 arcmin area centered on the GOODS-IRAC imaging (Dickinson et
al. 2003) of the CDFS (Giacconi et
al. 2002). The survey is complementary in area and depth to other
legacy programs, such as GOODS-IRAC (138 arcmin, 1380 minutes (Dickinson
et al. 2003)), S-COSMOS (2 deg, 20 minutes (Sanders et al. 2007)), the
Spitzer Wide-Area InfraRed Extragalactic Survey (SWIRE; 60 deg,
2-8 minutes (Lonsdale et al. 2003)), the Spitzer Ultra Deep Survey (SpUDS;
0.8 deg, 120 minutes (PI: J. Dunlop)), and the Spitzer
Deep, Wide-Field Survey (SDFWS; 10 deg, 6 minutes, including the
Irac Shallow Survey (ISS) (Ashby et al. 2009)) (see Table
1 for more details). The goal of
the SIMPLE survey was to map a large area around the CDFS, with an
optimum overlap with existing surveys such as the GEMS project (Galaxy
Evolution from Morphology and SEDs), COMBO-17, and MUSYC. The area of
the CDFS appears as a hole in the center of the mosaic. The central
coordinates of the field are: , J2000). Figure 1
illustrates the field of the main surveys of the E-CDFS: GOODS (IRAC
and ACS), GEMS, COMBO-17, MUSYC, and SIMPLE.
The SIMPLE IRAC Legacy program was observed under program number GO
20708 (PI: van Dokkum). The complete set of observations consists of 36
pointings. On each pointing the mapping mode was used to observe a 2
3 rectangular grid, with each grid position receiving 30
minutes integration, for a total of 3 hr per pointing. The grand
total exposure time was 105 hr. The 2 3 map grids
partially overlap, leading to an average exposure time on the sky of
1.5 hr. The observations were split in two epochs,
approximately 6 months apart.
The observations were split in two epochs, approximately 6 months
apart. The telescope orientation was rotated between
the two epochs and this ensured that the area of the E-CDFS was fully
covered in all four IRAC bands. This is illustrated in
Fig. 2, which shows the exposure coverage of channel 1 (3.6
m; left) and channel 2 (4.5 m; right). Solid lines indicate the
outline of all observations from the first epoch, dashed lines those
of the second. IRAC observes in pairs: 3.6 and 5.8 m
simultaneously on one field and 4.5 and 8.0 m on an adjacent
field. Due to this construction and the telescope rotation between the
two epochs, the full area was covered by all bands after completion of
the observations. A summary of the observations is given in Table
2. The raw data and the observational details can be
obtained from the Spitzer Archive with the Leopard software
|Spitzer program ID||20708|
|Start date ep1||2005 Aug 19 (week 91)|
|End date ep1||2005 Aug 23 (week 91)|
|Start date ep2||2006 Feb 06 (week 115)|
|End date ep2||2006 Feb 11 (week 116)|
3. Data Reduction
The reduction of the IRAC data was carried out using the Basic
Calibrated Data (BCD) generated by the Spitzer Science Center
(SSC) pipeline and a custom-made pipeline that post-processes and
mosaicks the BCD frames. The reduction includes the following steps:
3.1. SSC Pipeline Processing
The starting point for the reduction are the BCD frames produced by
SSC pipeline. The epoch 1 observations were processed by BCD pipeline
version S12.4.0. The epoch 2 data were processed using pipeline
version S13.2.0. The main differences between these two versions are
related to pointing refinement, muxstriping, and flux conversion. These
issues are all addressed separately in our own reduction pipeline, and
hence these updates have no effect on the end product. An additional
enhancement of S13.2.0 is the introduction of a super sky flat image,
based on the first two years of IRAC of flat-field data. This has only
a small effect on the data of at most 0.5%. The most significant
steps of the SSC IRAC reduction pipeline are dark subtraction,
detector linearization, flat-fielding, and cosmic ray detection. The
data are calibrated in units of . The pipeline also identifies
bad pixels, which it flags and writes to a mask image, and constructs
initial masks for cosmic rays (called ”brmsk”).
3.2. Post-processing of the BCD Frames
We post-process the BCD frames to correct for several artifacts caused
by highly exposed pixels (primarily bright stars and cosmic rays) and
calibrate the astrometry. In this section, we briefly describe some of
the artifacts and how we try to remove them. More detailed information
can be found in the IRAC Data Handbook, Section
We start with discarding the two leading short exposures of each series of observations, which can suffer from the so-called first-frame effect and cannot be calibrated correctly
Prior to correction for the artifacts, a median sky image is constructed based on the data taken in each series of observations. This sky image is subtracted from each individual frame to remove both residual structure or gradients in the background caused by bias or flat fielding, and long-term persistence effects.
One of the principal artifacts in IRAC data is column pulldown. When a
bright star or cosmic ray reaches a level of 35,000 DN
in the channel 1 and 2 arrays (3.6 and 4.5 m), the intensity of the
column in which the bright object lies is affected. Since the
intensity decreases throughout the column, this effect is called
”column pulldown”. While column pulldown is slightly different below
and above the bright object and has a small slope, the effect is
nearly constant in practice. We therefore correct for the effect by (1)
locating the columns of 35,000 DN
pixels (2) masking all bright sources in the frame, (3) calculating the
median of the affected columns excluding any sources, and (4)
subtracting the median. We favor this simple correction because its
implementation is more robust than fitting, e.g., a general two-segment
Besides column-pulldown, channels 1 and 2 suffer from an effect known as muxbleed, which appears as a trail of pixels with an enhanced and additive output level. When a bright source is read out, the readout multiplexers do not return to their cold state for some time, resulting in a pattern that trails bright sources on the row. Since columns are read simultaneously in groups of four, the effect repeats every fourth column. The amplitude of the effect decreases with increasing distance to the bright object, but it does not scale with its flux. It is therefore not possible to fit muxbleed by a simple function, and we choose for a very straightforward cosmetic correction. For each offending pixel ( 30 ), we generate a list of pixels selecting every fourth pixel next in the row and previous in the row. Then, we median filter the pixel list with a filter width of 20 pixels and subtract the result. The data products (see Section 3.6) include a map that shows which pixels were muxbleed corrected.
This procedure removes the bulk of the muxbleed signal, but not all of it. However, the effect of a residual muxbleed signal in the final mosaic is reduced because of the rotation of the field between the two epochs. At different times, the muxbleed trail affects different pixels relative to the source position.
Bright stars, hot pixels, and particle or radiation hits can also generate a muxstripe pattern. Where muxbleed only affects pixels on the same row, the muxstripe pattern may extend over a significant part of the image, albeit to lower levels. Muxstriping appears as an extended jailbar pattern preceding and/or following the bright pixel. It is a fairly subtle effect, usually only slightly visible in individual frames around very bright stars, but it becomes easily visible in deeper combined frames. Muxstriping is caused by the increase of relaxation time of the multiplexer after a bright pixel is read out. It takes 10 s to clock the next pixel onto an output, whereas the recovery time after the imprint of a bright pixel is of the order of tens of seconds. The muxstripe effect also repeats every fourth column and extends below each source. Each horizontal band of the image between two bright sources, contains the pattern induced by all sources above it and needs to be corrected accordingly.
We remove this effect by applying an offset in the zones surrounding the offending pixels using a program kindly provided by Leonidas Moustakas of the GOODS-team. In brief, this algorithm identifies the bright sources in each frame and produces a model of the corresponding muxstripe pattern, which can then be subtracted.
Figures 3 and 4 show the treatment of the artifacts just described. In the left panel, a BCD frame is affected by column pulldown, muxbleed, and muxstriping. The middle panel shows the corrections, this frame is subtracted from the affected one which results in the image on the right, a clean frame.
Finally, bright sources leave positive residuals on subsequent readouts of the array (persistence), although much of the signal subsides after 6-10 frames. We correct for persistence by creating a mask of all highly exposed pixels in a frame and then masking those pixels in the six subsequent frames. Any residual contamination through persistence will be diminished by the final combination of all exposures.
After correction for artifacts, the pipeline subtracts a constant background by (1) iteratively thresholding and masking pixels associated with sources and calculating the mode and RMS of the remaining background pixels and (2) subtracting the mode of the image.
Cosmic Ray Rejection
For each series of observations, a first pass registered mosaic is
created from the post-processed BCD frames. For the construction of
this mosaic, the BCD ”brmsk”-frames are used as a first guess to mask
candidate cosmic rays. The image is median combined, so it should be
free of any deviant pixels.
Next, the first pass image is aligned and subtracted from each exposure. To create a cosmic ray detection image, the result is divided by the associated BCD ”bunc” image, which contains estimates of the uncertainties in each pixel based on a noise model
The SIMPLE astrometry is calibrated to a compact-source catalog
detected in a combined deep image from MUSYC
The astrometric differences between the reference catalog and the SIMPLE pointings are small (up to 1) and can be corrected by applying a simple shift. There is no evidence for rotation, or higher order geometric distortion. We therefore apply a simple offset to the WCS CRVAL1 and CRVAL2 of the BCD frames to refine the pointing. The pointing refinement solutions determined for the 3.6 and 4.5 m BCDs are applied to the 5.8 and 8.0 m images, respectively, as there are generally few bright sources at 5.8 and 8.0 m to derive them independently.
The resulting astrometry accuracy relative to the MUSYC E-CDFS catalog is typically (averaged per IRAC channel), with source-to-source 2--clipped RMS of 0.12 in channel 1/2 and 0.14 in channel 3/4. Large-scale shears, systematic variations on scales of a few arcminutes, are 0.2 or less. Figure 5 shows the residual shifts of the [3.6 m] mosaic with respect to the MUSYC image. The quoted astrometric uncertainties are relative to the MUSYC catalog, but we also verified that the astrometry agrees very well (0.1 level) with the ”wfiRdeep” image (Giavalisco et al. 2004), which is used as a basis for the ACS GOODS astrometry.
3.3. Image Combination and Mosaicking
After individual processing, the individual BCD frames are mosaicked onto an astrometric reference grid using the refined astrometric solution in the frame headers.
For the reference grid, we adopt the tangent point, pixel size, and
orientation of the GOODS-IRAC images (, 0.6 pixel. The
pixel axes are aligned with the J2000 celestial axes
Also following GOODS, we put the tangent point (CRVAL1,2) at a half-integer pixel position (CRPIX1,2). This ensures that images with integer pixel scale ratios (e.g., 0.3, 0.6, 1.2) can (in principle) be directly rebinned (block summed or replicated) into pixel alignment with one another. This puts GOODS, SIMPLE, and FIDEL (a deep 24/70 m survey in the E-CDFS) on the same astrometric grid. The final SIMPLE mosaic extends 38′ 48′ (3876 4868 pixels).
For each epoch, the individual post-processed BCD frames are
transformed to the reference grid using bicubic interpolation, taking
into account geometric distortion of the BCD frame. Cosmic rays and
bad pixels are masked and the frames are average combined without
Finally, the separate epoch 1 and epoch 2 mosaics are combined, weighted by their exposure times. By design, the SIMPLE E-CDFS observational strategy maps around the GOODS-S field, which leaves a hole in the combined mosaic. To facilitate the analysis, we add the GOODS-S IRAC data (DR3, mosaic version 0.3 , to the center of the SIMPLE mosaic. We shift the GOODS-S IRAC mosaics by 0.2 to bring its astrometry in better agreement with SIMPLE. To ensure a seamless combination between the epoch 1, epoch 2, and GOODS-S images, we subtract an additional background from the images before combination. The background algorithm masks sources and measures the mode of the background in tiles of 1′ 1′. The ”mode map” is then smoothed on scales of 3′ 3′ and subtracted from the image, resulting in extremely flat images and a zero background level on scales 1′.
|(m)||(Jy (DN s))||(AB)||(″)||(″)|
Note. – The FWHM of the images is 1″.5. To convolve those to the PSF of ch4, we use a sigma of 1.34.
3.4. Flux Calibration
The SSC data are calibrated using aperture photometry in 12″
apertures. Since ground-based IR calibrators are too bright to use for
IRAC, the actual flux for each channel needs to be predicted using
models (Cohen et al. 2003). The resulting calibration factors were
determined by Reach et al. (2005) and are listed in the image headers
and Table 3.
The epoch 1 and epoch 2 science images were scaled to a common zeropoint so that their data units agree. For convenience, we calibrate our images to the GOODS-S IRAC data (in DN s). This is done using the original calibration factors from Table 3. The relative accuracy of the zeropoint can be estimated by minimizing the count rate differences of bright, non-saturated stars in circular apertures in regions where the images overlap. This indicates that the fluxes agree within 3%.
3.5. Additional Data Products
Exposure Time and RMS Maps
The exposure time maps are created by multiplying, at each position,
the number of BCD frames that were used to form the final image by the
integration time of each frame. The exposure map thus reflects the
exposure time in seconds on that position of the sky, not the average
exposure time per final output pixel.
The 25%, 50%, and 75% percentiles of the final exposure maps (excluding GOODS-S) are 3100, 5500 and 9100 s (0.9, 1.5 and 2.5 hr) for all channels. The corresponding area with at least that exposure time is 1200, 800, and 400 arcmin, respectively. In addition, the central GOODS-S mosaic has 23 hours per pointing over 138 arcmin.
This release also provides RMS maps. The RMS maps were created by (1) multiplying the final mosaic by the (where is the exposure time map), to create an exposure normalized image, (2) iteratively rejecting pixels deviating and their directly neighboring pixels, (3) binning the image by a factor 4 4, and (4) calculating the RMS statistic of the binned pixels in a moving window of 15 15 bins. The result is approximately the local RMS background variation at scales of 2″.4 at the median exposure time, which does not suffer from correlations due to resampling. We multiply this value by to get the approximate per-pixel RMS variation at the mosaic pixel scale for other exposure times (see, e.g., Labbé et al. 2003). This RMS map does not directly reflect the contribution to the uncertainty of source confusion. The variations in the RMS due to instrumental effects are mitigated by the addition of the observed epochs under 180 different roll angles.
We provide a flag map, which identifies pixels corrected for muxbleed in channel 1 and channel 2. These corrections are not optimal, and when analyzing the images or constructing source catalogs, it may be useful to find pixels which may have been affected. The flag image is a bit map, i.e., an integer map that represents the sum of bit-wise added values (flag = 1 indicates a muxbleed correction in the first epoch, flag = 2 indicates a correction in the second epoch).
3.6. Final Images
The final images of SIMPLE are publicly
4. Additional Data
4.1. The Data
To cover the optical to NIR regime, we use the imaging from
the COMBO-17 and ESO DPS surveys (Wolf et al. 2004 and Arnouts et
al. 2001, respectively) in the re-reduced version of the GaBoDS
consortium (Erben et al. 2005; Hildebrandt et al. 2006). We include
the images from the Multiwavelength Survey by Yale-Chile
(MUSYC, Gawiser et al. 2006), which are available
4.2. The MIPS 24 m Data
The E-CDFS was also observed extensively by the Multi-band Imaging Photometer for Spitzer (MIPS) as part of FIDEL (PI: M. Dickinson). The survey contains images at 24, 70, and 160 m. We only consider the 24 m image, due to its utility as an indicator of star formation, its sensitivity, and the fact that the source confusion at 24 m is less severe compared to the longer wavelengths. The FIDEL 24 m image reaches a 5- sensitivity ranging from 40 to 70 Jy, depending on the source position (Magnelli et al. 2009). We use the v0.2 mosaic, which was released on a scale of 1″.2 pixel.
5. Source Detection and Photometry
Sources are detected and extracted using the SExtractor software
(Bertin & Arnouts 1996) on a detection image. The detection image is
an inverse-variance weighted average of the 3.6 and 4.5 m
images. The 3.6 and 4.5 m bands are the most sensitive IRAC bands
and the combination of the two leads to a very deep detection
image. To enable detection to a similar signal-to-noise limit over the
entire field, we multiply the [3.6]+[4.5] image by the square root of
the combined exposure map. This produces a ”noise-equalized” image
with approximately constant signal to noise, but different depth, over
the entire field. Figure 1 shows the noise-equalized detection
image in the background.
Subsequently, we run SExtractor on the detection map with a 2- detection threshold. We choose this detection limit to be as complete as possible, at risk of severe confusion. We will discuss the matter of confusion later. In the detection process, SExtractor first convolves the detection map with a detection kernel optimized for point sources. We use a 5 5 convolution mask of a Gaussian PSF with an FWHM of 3 pixels. Furthermore, we require a minimum of two adjacent pixels above the detection threshold to trigger a detection. The resulting catalog contains 61,233 sources, 43,782 of which have a signal-to-noise ratio (S/N) at 3.6 m.
Instead of our exposure time-detection image, we could have used the RMS map for detection. In practice, the RMS should be proportional to and the choice of detection image should not significantly influence the output catalog. To test the correspondence of RMS and , we multiplied the RMS by the square root of the exposure time map, which results in a tight Gaussian distribution with a width of = 0.003. Our exposure time detection image is therefore very similar to a detection image based on an RMS map.
As an aside, we note that SExtractor’s RMS map underestimates the true noise as the pixels are correlated (see, e.g., Labbé et al. 2003). If we use SExtractor’s RMS map, we find 10 % more objects than with our method, as expected. Many of these objects are near the edges of the image; none of them have an S/N 5.
Image Quality and PSF Matching
In order to obtain consistent photometry in all bands, we smooth all images (except MIPS) to a common PSF, corresponding to that of the 8.0 m , which has the broadest FWHM. To determine the FWHM, we compile a list of stars with . We select five different areas of the E-CDFS to check whether the PSF changes over the field. This is in particular important for the IRAC bands, which have a triangular-shaped PSF. Because of the rotation between the two epochs, the final IRAC PSF is a combination of two triangular-shaped PSFs that are rotated with respect to each other. This combined PSF can vary with position in the field of view and we first need to check how large these variations are. Radial profiles of the stars are determined using the IRAF task imexam. We find that the variation of the mean FWHM over the whole field of view is 5% for all IRAC bands and there is no clear trend between the mean FWHM and the position on the field for any IRAC band. We convolve all images with a Gaussian to produce similar PSFs in all bands. The mean original FWHM per band and the Gaussian sigma values used for convolution are listed in Table 3.
We run SExtractor in dual-image mode, meaning that the program
determines the location of sources in the combined [3.6]+[4.5]
detection image, and then measures the fluxes in the smoothed science
images in the exact same apertures. We perform photometry in fixed
circular aperture measurements in all bands for each object, at radii
of 1.5, 2.0, and 3.0. In addition, we use
SExtractor’s autoscaling apertures based on Kron (1980)
radii. Following Labbé et al. (2003), we refer to these apertures as
APER(1.5), APER(2.0), APER(3.0), and APER(AUTO). We use these
apertures to derive both color fluxes and total fluxes (see Labbé et
SExtractor provides a flag to identify blended sources that we include in our catalog as “flag_blended”. In the SIMPLE catalog, 60%
Given the large number of blended sources, it is useful to be able to identify only the most extreme cases of blending. If the sum of the Kron aperture radii of a source and its nearest neighbor exceeds their separating distance and if the neighbor’s flux is brighter than its own, we set the ’flag_blended’ entry to 4. The percentage of sources suffering from this form of extreme blending is 32% for all sources with S/N at 3.6 m.
While performing photometry, we treat blended sources separately. Following Labbé et al (2003) and Wuyts et al. (2008), we use the flux in the color aperture to derive the total flux for sources that suffer from severe blending. For the identification of blended sources, we prefer our own conservative blending criterion over SExtractor’s blending flag, since the latter identifies too many sources as blended, even sources for which the photometry is essentially unaffected
To determine the color fluxes, we use the circular apertures with 2″ radius for all sources in all bands:
We calculate the total fluxes from the flux measured in the AUTO aperture. For sources with an aperture diameter smaller than 4″ diameter, we apply a fixed aperture of 4″.
Where is the circularized diameter of the Kron aperture. If the source is blended (FLAG_BLENDED = 4), then
Finally, we apply an aperture correction to the total fluxes using the
growth curve of bright stars to correct for the minimal flux lost
because it fell outside the ”total” aperture.
For the IRAC data, we apply individual growth curves for each band. The zeropoint for the aperture correction is based on the values listed in Table 5.7 of the IRAC Data Handbook
Note. – 4″ is our color aperture, 7″.3 is taken from Table 5.7 from the IRAC Data Handbook (corresponds to 3 pixel radius in that table), and 12″ is the zeropoint aperture (see Section 3.4). The numbers in the second column are derived from our growth curves, the third column contains the corrections from the Data Handbook, and the total corrections are listed in the last column.
The MIPS 24 m Data
The photometry of the MIPS 24 m image is performed in a different
way, because of the larger PSF. Here, we use a deblending model to
mitigate the effects of confusion. We use the source positions of the
IRAC 3.6 m image, which has a smaller PSF, to subtract modeled
sources from MIPS sources that show close neighbors, thus cleaning the
image. After this procedure we perform aperture photometry in
apertures of 6″ diameter, and correct fluxes to total fluxes
using the published values in Table 3.12 of the MIPS Data Handbook.
In principle a similar approach could have been attempted for the IRAC images themselves. Ground-based -band data and space-based NICMOS imaging have been successfully used to deblend IRAC images (Labbé et al. 2006, Wuyts et al. 2008). However, whereas the resolution of our -band image is appropriately high, the image is not deep enough for this kind of modeling.
5.3. Background and Limiting Depths
The determination of the limiting depth depends on the noise
properties of the images. To analyze those, we place 4000
circular apertures on the registered and convolved images and measure
the total flux inside the apertures. Apertures are placed across the
field in a random way, excluding all positions associated with sources
using the SExtractor segmentation map. We use identical aperture
positions for all bands, and repeat the measurements for different
aperture sizes. The distribution of empty aperture fluxes can be
fitted by a Gaussian, which provides the flux dispersion of the
distribution. The RMS depends on aperture size and is larger for
larger apertures (see Fig. 8). The left panel shows the
distribution of empty aperture fluxes for channel 1 for apertures of
sizes 2″, 3″, and 4″. The right panel shows how the
RMS increases with aperture size for all IRAC bands. The noise level
is higher than can be expected from uncorrelated Gaussian noise. The
reason for this is that correlations between neighboring pixels were
introduced while performing the data reduction and PSF matching (see
also Labbé et al. 2003).
The depth of our SIMPLE IRAC mosaic is a function of position, as some parts have longer exposure times than others. Table 5 lists the total AB magnitude depths at 5- for point sources and the area over which this depth is achieved. Figure 9 provides a graphic representation of the limiting depths of all wavelength bands.
To investigate whether our measurement of the uncertainties in the
IRAC photometry is reasonable, we compare the IRAC fluxes of epoch 1
with those of epoch 2.
The results are shown in
Fig. 10. The median offsets between the two epochs are
printed in the lower left corner and are close to zero in each
band. The scatter in each panel is small and comparable to the
estimated RMS values.
|percentile||75%||50%||25%||(percentile of pixels)|
|area||1200||800||400||(area in arcmin with at|
|least this exposure time)|
|3.6 m||23.66||23.86||24.00||(depth at 3.6 m)|
|4.5 m||23.50||23.69||23.82||(depth at 4.5 m)|
|5.8 m||21.68||21.95||22.09||(depth at 5.8 m)|
|8.0 m||21.69||21.84||21.98||(depth at 8.0 m)|
We identify stars by their color and signal to noise () and find 978 stars in the total catalog. To test the validity of this selection criterion, we compare it to the selection technique defined by Daddi et al. (2005). In the diagram stars have colors that are clearly separated from the colors of galaxies and they can be identified with the requirement . From the 978 stars in the SIMPLE catalog with sufficient signal to noise in the - and -bands, 94% obey the criterion. In the diagram, the remaining 6% lie only slightly above the stellar selection limit.
6. Derived Parameters
6.1. Spectroscopic and Photometric Redshifts
The E-CDFS is one of the principal fields for high-redshift studies
and has consequently been the object of many spectroscopic
surveys. Taylor et al. (2009b) compiled a list of reliable
spectroscopic redshifts from several of these surveys, which we
cross-correlated with our SIMPLE catalog. The spectroscopic redshifts
come from: Croom et al. (2001), Cimatti et al. (2002), le Fèvre et
al. (2004), Strolger et al. (2004), Szokoly et al. (2004), van der Wel
et al. (2004, 2005), Daddi et al. (2005), Doherty et al. (2005),
Mignoli et al. (2005), Ravikumar et al. (2007), Kriek et al. (2008),
Vanzella et al. (2008), Popesso et al. (2009), and Treister et
al. (2009a). The list contains 2095 spectroscopic redshifts.
In addition, we include photometric redshifts from the COMBO-17 survey (Wolf et al. 2004) out to = 0.7, which are very reliable at those redshifts. For the remainder of the sources we compute photometric redshifts using the photometric redshift code EAZY (Brammer et al. 2008). The EAZY algorithm provides a parameter that indicates whether a derived photometric redshift is reliable. Brammer et al. (2008) show that for the difference between photometric and spectroscopic redshifts increases sharply and that quality cuts based on can reduce the fraction of outliers significantly. Therefore, when testing the accuracy of our photometric redshifts, we only include sources with .
Figure 11 shows the EAZY photometric redshifts compared
against a list of spectroscopic redshifts. The upper panel shows the
direct comparison for sources with in both band and 3.6
m (a total of 1280 sources, from which we remove 54 sources with
(4%), resulting in a final sample of 1226 sources). The
lower panel shows , where . X-ray detections are shown in gray.
To quantify the scatter, we determine the normalized median absolute deviation (, which is a robust estimator of the scatter, normalized to give the standard deviation for a Gaussian distribution). Overall the of is 0.025, but it varies with redshift, ranging from 0.024 at , 0.055 at , and 0.38 at 2.0. There is a significant fraction (8.2%) of outliers with . This number agrees well with the 11% Taylor et al. (2009b) found for the MUSYC catalog. Many of the outliers are detected in X-ray and are AGN candidates (43%). The high fraction of (candidate) AGN outliers could be explained by the fact that we do not have an AGN spectrum in our template set. EAZY photometric redshifts for X-ray detections are, therefore, uncertain. If we remove them from the sample, the overall accuracy improves and becomes 0.024, 0.041, and 0.16 at redshifts , 1.5, and 2.0, respectively.
Including AGN templates will improve the overall accuracy of the photometric redshift, as can be seen in Luo et al. (2010). Those authors determine the photometric redshifts of a sample of X-ray sources in the E-CDFS, using UV-to-IR data, including the SIMPLE 3.6m-band. Apart from standard galactic templates they used 10 different AGN templates. In the comparison with spectroscopic redshifts, there are only three outliers, which is 1.4% of the sample and the 0.010.
We also check whether the outliers suffer from blending. Out of the 101 outliers, 26 sources have a neighboring source whose APER(AUTO) exceeds their separating distance and whose flux is at least as bright as its own, which can affect their photometry. However, removing these sources from the sample does not decrease , since there are many sources with nearby bright companions whose photometric and spectroscopic redshifts agree well.
6.2. Star Formation Rates, Rest-frame Photometry and Stellar Masses
In this section, we describe the main characteristics of the procedures for deriving star formation rates and stellar masses. For a more detailed description, the reader is referred to Damen et al. (2009a). We estimated SFRs using the UV and IR emission of the sample galaxies. We use IR template spectral energy distributions (SEDs) of star forming galaxies of Dale & Helou (2002) to translate the observed 24 m flux to . First, we convert the observed 24 m flux density to a rest-frame luminosity density at m, then we extrapolate this value to a total IR luminosity using the template SEDs. To convert the UV and IR luminosities to an SFR, we use the calibration from Bell et al. (2005), which is in accordance with Papovich et al. (2006), using a Kroupa IMF:
where is the luminosity at rest frame
2800 Å, a rough estimate of the total integrated UV luminosity (1216-3000Å).
To obtain stellar masses, we fitted the UV-to-8 m SEDs of the galaxies using the evolutionary synthesis code developed by Bruzual & Charlot 2003. We assumed solar metallicity, a Salpeter IMF, and a Calzetti reddening law. We used the publicly available HYPERZ stellar population fitting code (Bolzonella et al. 2000) and let it choose from three star formation histories: a single stellar population (SSP) without dust, a constant star formation (CSF) history, and an exponentially declining star formation history with a characteristic timescale of 300 Myr (), the latter two with varying amounts of dust. The derived masses were subsequently converted to a Kroupa IMF by subtracting a factor of 0.2 dex. We calculated rest-frame luminosities and colors by interpolating between observed bands using the best-fit templates as a guide (see Rudnick et al. 2003 and Taylor et al. 2009a for a detailed description of this approach and an IDL implementation of the technique dubbed ’InterRest’
7. Catalog Contents
The SIMPLE IRAC-selected catalog with full photometry and explanation
is publicly available on the
— A running identification number in catalog order as reported by SExtractor.
— The pixel positions of the objects based on the combined 3.6 m + 4.5 m detection map.
— The right ascension and declination in equinox J2000.0 coordinates, expressed in decimal degrees.
— Observed color flux in bandpass , where in circular apertures of 4″ diameter. All fluxes are normalized to an AB magnitude zeropoint of 25.
— Uncertainty in color flux in band (for derivation see Section 5.3).
— Estimate of the total flux in band , where , corrected for missing flux assuming a PSF profile outside the aperture, as described in Section 5.2.1.
— Uncertainty in total flux in band .
— Aperture diameter (in ″) used for measuring the total flux in band . This corresponds the circularized diameter of APER(AUTO) when the Kron aperture is used. If the circularized diameter is smaller than 4″, the entry is set to APER(COL) = 4″ (see Section 5.2).
— Relative weight for each band . For the IRAC bands, the weights are determined with respect to the deepest area of the SIMPLE mosaic without GOODS.
— Set to 1 if the source meets the criteria of Section 5.4.
— Contains the SExtractor deblending flag, which indicates whether a source suffers from blending (bit = 1) or whether it has a close neighbor (bit = 2). If a source suffers from extreme blending (see Section 5.2) then bit = 4.
— Bitwise added quality flag, that indicates whether a source lies in the GOODS area (bit = 1), lies in a stellar trail (bit = 2), falls outside the MUSYC field (bit = 4) or has been corrected for muxbleed.
Please note that all flux units in the catalog are converted to the
same zeropoint on the AB system: .
8. Comparison to Other Catalogs
In this section, we compare our SIMPLE catalog to the published catalogs of Taylor et al. (2009; MUSYC, E-CDFS) and Wuyts et al. (2008; FIREWORKS, CDFS). All catalogs cover (parts of) the same area in the sky. The important difference is that we detect sources in the IRAC 3.6 m and 4.5 m bands, whereas both the MUSYC and the FIREWORKS catalogs are -band detected. The advantage of an IRAC-selected catalog is that IRAC probes the rest-frame NIR out to high redshift. The downside of IRAC selection is the lower resolution, which leads to confusion. The FIREWORKS catalog used a -band selection specifically for this reason. We will investigate the effect these differences have on the catalogs below.
8.1. SIMPLE versus MUSYC
The optical-NIR part of the SIMPLE catalog () is based on the same data as the MUSYC catalog. The differences lie in the PSF, detection method, and photometry. Taylor et al. (2009b) determine their total fluxes in a similar way as we do. However, they include an extra correction based on the measurement of the background, which they measure themselves instead of using the value derived by SExtractor and they do not make a distinction between blended and non-blended sources. We cross-correlated the two catalogs and in Fig. 12 we present the comparison. Each panel shows sources with S/N 10 in IRAC 4.5 m and in the relevant band of the panel. We also applied a weight cut in , , recommended by Taylor et al. (2009b). We determined the median offsets in different magnitude bins and show them at the bottom of each panel. The first number (in black) represents the median offset of all sources, the gray numbers represent the median offset in each magnitude bin; they are in all bands. The error bars represent the formal expected photometric errors, which are dominated by the Poisson uncertainties in the background. The offsets at bright magnitudes are not caused by Poisson statistics, but most likely by slight systematic differences in methodology. We investigated the bright sources in the -, -, -, and -band, which show an offset of 0.2 in color and found that this is an effect of the aperture sizes that were used. The MUSYC fluxes were determined using SExtractor’s MAG_ISO, enforcing a minimum aperture diameter of 2″.5. For the SIMPLE catalog, we used a fixed 4″.0 aperture diameter. The large color differences at the bright end occur for galaxies for which the differences in aperture size are large too (factor 1.5 and greater).
8.2. SIMPLE versus FIREWORKS
The FIREWORKS catalog is constructed from observations in wavelength
bands that in some cases differ from the ones we use. The and
data come from the Wide Field Imager and are the same as we use,
except for the -band, for which the FIREWORKS uses the
-imaging. The -band image was observed by HST, , and data come from ISAAC. The IRAC images were taken by the
GOODS team and are nearly the same as the ones we use. Figure
13 shows the comparison of all these bands against each
other. As in Fig. 12, we only show sources with S/N
10 in IRAC 4.5 m and in the relevant band of the panel, with a
weight in -band larger than 0.5. The median values are once more
shown at the bottom left and the error bars again represent the
expected formal errors.
The FIREWORKS catalog allows easy identification of blended sources and we have removed these from Fig. 13, since they worsened the comparison. This can be seen in Fig. 19 in Appendix B, which shows the difference in -band magnitude for FIREWORKS and SIMPLE. In that figure, we did include the blended FIREWORKS sources and marked them in red. They form a specific tail and we have removed them from all further analysis. The sources that suffer from extreme blending in the SIMPLE catalog do not take up such a specific locus in the comparison figures. Excluding them from the sample does not significantly affect the comparison and therefore we keep them in the sample (see Appendix B).
In Fig. 13, the comparison between FIREWORKS and SIMPLE tails upward at the faint end. There, the SIMPLE fluxes are brighter than FIREWORKS. This could be due to the fact that the SIMPLE apertures are quite large and will catch some light from neighboring sources.
A direct comparison between SIMPLE and FIREWORKS illustrates the strengths of both data sets as can be seen in Fig. 14, which shows a color-magnitude diagram of both catalogs for sources with S/N 5 in the relevant bands. The envelopes at the bright end agree well, but at the faint end FIREWORKS reaches greater depth. The advantage of the SIMPLE survey is its large area, and thus its large number of sources. Out to a magnitude of 21.5 in [3.6], the SIMPLE catalog contains 4061 sources at 5-, compared to 1,250 for FIREWORKS.
In addition to a comparison of the photometry, we compare derived
quantities of the FIREWORKS and SIMPLE catalogs. Figure 15
shows the comparison between mass, (specific) star formation rate,
MIPS 24 m flux, and redshift. Mean values in bins of equal number
of sources are indicated by the red line and given at the bottom of
The panels with MIPS 24 m flux and SFR show the best agreement, although the scatter in the comparison of the SFR is substantially higher than it is for the MIPS fluxes. This is caused by the difference in photometric redshifts. If we use FIREWORKS photometric redshifts to determine the SIMPLE SFRs, the scatter in the SFRs is reduced to the scatter in MIPS fluxes.
The scatter is highest in the panels where masses and specific star formation rates (sSFRs) are compared, quantities that depend on photometric redshifts and model assumptions. These are, therefore, more susceptible to systematic errors. Since the masses are derived in similar ways for SIMPLE and FIREWORKS (same models, dust extinction law, metallicity, and IMF), systematics in the modeling can not be responsible in this comparison. We redetermined our masses using FIREWORKS photometric redshifts and found that this reduces the number of outliers in the mass-comparison panel, but not the scatter. The main reason for the scatter in mass and sSFR is signal to noise. The mean absolute deviation of the scatter in the mass comparison is 0.5 for sources with (S/N) 10. For sources with a (S/N)20, the scatter is reduced to 0.1. Further discussion on the differences between FIREWORKS and SIMPLE fluxes and derived parameters can be found in Appendix C.
9. Evolution of the Specific Star Formation Rate
In a recent paper (Damen et al. 2009a), we showed how the specific
star formation rate (SFR per unit mass, sSFR) evolves with
redshift. These findings were based on a bright sub-sample of a
preliminary version of the SIMPLE catalog, in which total fluxes were
crudely determined by applying an aperture correction to the color
fluxes. In this section, we briefly revisit the results of Damen et
al. (2009a) and show if and how they change, using the final version
of the catalog. For details on the derivation of star formation rates
and masses, see Damen et al. (2009a).
|sSFR ( yr)|
For this analysis, we created a sub-sample of our catalog out to . We selected all sources with , a limit
that is chosen so that 95% of the selected sources has an S/N 5 in
the K-band. From this sub-sample, we excluded all stars and all X-ray
detections, since they are likely AGNs. The final sample contains
3391 sources. Figure 16 shows how the mean sSFR evolves with
redshift in different mass bins, the mean values are also given in
Table 6. It agrees very well with the corresponding
figure in Damen et al. (2009a), and all conclusions remain the
same. The sSFR increases with redshift for all mass bins and the slope
() does not seem to be a strong function of mass (see
also Damen et al. (2009b)). To quantify this, we fitted the sSFR with
over the redshift range where we are complete with respect
to mass. The value of the slope is 5.10.6 and 4.60.3 for
galaxies with masses and , respectively. These numbers are consistent within 1- with
results based on the FIREWORKS catalog over the same redshift range
(3.80.8 (), and 4.90.9 (), for both mass
bins, respectively; Damen et al. 2009b).
However, the number of galaxies in each bin has changed with the new
version of the catalog and this influences the fraction of quiescent
galaxies at the highest mass bin. We define a galaxy to be quiescent
when its sSFR is smaller than one-third of the inverse of the Hubble
time at its redshift ()). In Damen et
al. (2009a) the fraction of quiescent galaxies decreased with redshift
out to 19%9% at = 1.8. Figure 17 shows the updated
version of this fraction. The old numbers are represented by dashed
lines. The slope is less steep and the fraction of quiescent galaxies
at = 1.8 is higher, 30%%. Although these numbers are
consistent within 1-, we investigate the cause of this change
and find that it can be explained by the increased total fluxes, which
change the masses and redshifts. The new value is in better agreement
with recent estimates Kriek et al. (2008) (36% 9%) and
I. Labbé et al. (in preparation) (35% 7%).
We have checked whether these results are robust against blending. We redetermined mean sSFRs for two different samples, removing all sources that (1) were flagged as blended by SExtractor and (2) we consider blended by our own criterion. In the latter case, the mean sSFRs change by less than 5%, in no preferred direction and the fraction of quiescent galaxies does not change. When all sources that were flagged as blended by SExtractor are removed, less than 10% of the sources remain in each mass bin. Whereas the resulting mean sSFRs can differ up to 40% from the original values, they are scattered around the mean sSFRs that are based on all sources. Hence, the global trends stay remarkably intact and the fact that our sample contains blended sources has no impact on the results.
The Spitzer IRAC/MUSYC Public Legacy Survey in the Extended
Chandra Deep Field South (SIMPLE) consists of deep IRAC observations
(1-1.5 hr per pointing) covering the 1600 arcmin area
surrounding the GOODS CDF-South. This region of the sky has extensive
supporting data, with deep observations from the X-rays to the thermal
infrared. We describe in detail the reduction of the IRAC observations
and the treatment of the main artifacts, such as column pulldown,
muxbleed, and muxstriping. The final SIMPLE IRAC mosaics were
complemented with 10′ 12′ GOODS-IRAC images in
the center and are available online.
We also present a 13-band, IRAC-detected catalog based on the SIMPLE images and existing public optical and NIR data of the MUSYC project. The wavelength bands that are covered are and the four IRAC bands at 3.6, 4.5, 5.8, and 8.0 m. The 5- IRAC depths are 23.8, 23.6, 21.9, and 21.7 for [3.6], [4.5], [5.8], and [8.0], respectively.
The current catalog is an updated version of the one used in Damen et al. (2009a). We have revisited our results in that work and found that the conclusions stay mainly the same. Investigating the evolution of the star formation rate we confirmed that the sSFR increases with redshift in all mass bins and that the rate of increase () does not seem to be a strong function of mass. This is in agreement with previous work by Zheng et al. (2007) and Damen et al. (2009b).
However, the redshift range over which we have determined the slope of the sSFR is small and differs per mass bin due to incompleteness at the low-mass end. We can use the deeper FIREWORKS catalog to investigate the possible (lack of) evolution of with mass out to higher redshift ( = 1.5) in the three mass bins of Fig. 16. The values for the FIREWORKS are consistent with the SIMPLE within 1-, although the number statistics in the highest mass bin are low (on average eight sources per redshift bin). We can conclude that the logarithmic increase of the sSFR with redshift is at least not a strong function of mass.
We investigated the fraction of massive galaxies that show suppressed star formation and found that at , 30%% of the massive galaxies ( ) have , which is our criterion for quenched star formation. This is consistent within 1- with the 19%9% from Damen et al. (2009a) and is in better agreement with values from Kriek et al. (2008) and I. Labbé et al. (in preparation), which are 36% 9% and 35% 7%, respectively.
Appendix A Flux Apertures
When performing photometry we use SExtractor’s AUTO aperture since it is more robust than for instance the ISOCOR aperture, which depends more sensitively on the depth of the image. In addition, it allows an easy comparison with other catalogs such as the MUSYC and FIREWORKS catalogs, which are both based on AUTO apertures. In Fig. 18, we show the effect different apertures have on the comparison between our catalog and the MUSYC catalog. As expected, the AUTO fluxes give the best agreement. The cause of the offset at the bright end of the panel showing the AUTO fluxes is discussed in Section 8.1.
Appendix B Confusion
While building the SIMPLE catalog, we treated blended (or confused) sources very conservatively and only identified the sources that most severely suffered from blending. We were not able to simply use the quality flags SExtractor provided, since those identified 60% of all sources as blended. Performing photometry on these ”blended” sources in a way commonly used for blended sources, exacerbated the disagreement with other catalogs (see Section 5.2). In addition, it was not possible to model blended sources using a deep source map at lower wavelength, since our -band data were not deep enough (see Section 5.2.3). The effect blending has on photometry is clear in, e.g., the FIREWORKS catalog, where blended sources were identified by their SExtractor flags and take up 12% of the sample. Figure 19 shows the comparison between the total band magnitude of SIMPLE and FIREWORKS. Blended sources in the FIREWORKS catalog are shown in red and form a distinct plume of scattered sources. Since the plume contains only blended sources, we removed these sources from all further analysis, since their photometry must be inaccurate (i.e., Figures 13-15). Unfortunately, we could not apply this trick to the SIMPLE catalog. In Section 5, we identified the sources that suffer from severe blending. We have not indicated them in Fig. 19, since they do not fill a specific locus, but instead are spread out evenly over the whole figure. It is, therefore, not possible to quantify the effect blended sources have on our photometry and derived parameters.
Appendix C Scatter between FIREWORKS and SIMPLE
In the comparison of the photometric and derived properties of the
SIMPLE and FIREWORKS catalogs, we observed a large scatter. In
Fig. 20, we show the comparison between MIPS fluxes. The mean
values of the difference are indicated by the red line and are printed
in red in the lower right corner. Error bars represent the standard
deviation in each bin and are printed in red in the lower right
corner. The FIREWORKS MIPS fluxes have been determined based on a -band
image with high spatial resolution. On the other hand, the SIMPLE
fluxes were determined using our IRAC imaging as a reference (see
Section 5.2.3). The IRAC data are deep, but have a PSF which is much
larger, leading to more confusion. This causes the difference in MIPS
fluxes, which are relatively modest (mean absolute deviation of 10% at
the bright end).
In Section 8.2.2, we stated that the scatter in mass was not caused by photometric redshift errors. This can be inferred from Fig. 21, which shows the difference in masses from FIREWORKS and SIMPLE against spectroscopic (left) and photometric (right) redshift. Despite the disappearance of a few dramatic outliers, it is not clear that the scatter is much reduced when using spectroscopic redshifts only.
- affiliation: Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Netherlands
- affiliation: Carnegie Observatories, 813 Santa Barbara Street, Pasadena, CA 91101, USA; Hubble Fellow
- affiliation: Department of Astronomy, Yale University, New Haven, CT, 06520, USA
- affiliation: Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Netherlands
- affiliation: School of Physics, The University of Melbourne, Parkville, 3010, Australia
- affiliation: Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA 16802, USA
- affiliation: NOAO, 950 N. Cherry Avenue, Tucson, AZ 85719, USA
- affiliation: Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854, USA
- affiliation: UCO/Lick Observatory, University of California, Santa Cruz, CA 95064, USA
- affiliation: Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138
- affiliation: Department of Physics and Astronomy, Tufts University, Medford, MA 02155, USA
- affiliation: Department of Astronomy, Yale University, New Haven, CT, 06520, USA
- affiliation: George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, Department of Physics and Astronomy, Texas A&M University, 4242 TAMU, College Station, TX 77843, USA
- affiliation: Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg, Germany
- slugcomment: Accepted for publication in the Astrophysical Journal
- Due to the first-frame effect, the first frame of a series of observations will have a different bias offset than the rest of the observations in the sequence. Since the first image of each series is observed in ”HDR-mode” (a very short exposure time of 0.4 s for identification of saturated sources), the second exposure might still suffer from this effect. It is recommended not to include these frames when building a mosaic.
- The BCD uncertainty images are the sum of estimates of the read noise, the shot noise due to the sky and uncertainties in the dark and flat calibration files
- The astrometry of the MUSYC detection image is tied to the stellar positions of the USNO-B catalog (Monet et al. 2003)
- -Listed as FLUXCONV in the image headers
- Sixty-two percent of the sources suffer from blending (SExtractor’s FLAGS keyword=1), 61% of the sources have a close neighbor (FLAGS = 2), and for 66% of the sources FLAGS=1 FLAGS=2.
- Adopting the SExtractor blending flag would produce a catalog that mostly consists of blended sources ( 90% for sources with a detection at 4.5 m and in the -band). These would all be assigned color fluxes that are, in our case, measured within a fixed aperture. The effect such a large fraction of aperture fluxes has on the comparison with the MUSYC catalog can be seen in Fig. 18 of Appendix A. The upper left panel shows a large tail of bright sources that are significantly offset with respect to a one-to-one relation.
- We use this aperture instead of the more generally used 12″ diameter because of the high density of sources in our field, which would lead to source confusion at large radii. To avoid these complications, we determine the inner part of the growth curve from our data to a 3″.66 radius, and combine it with the tabulated values from the handbook at larger radii. In this way, we minimize the effect of blending.
- Expressed as total flux divided by the flux in APER(COLOR)
- Alexander, D., et al. 2008, AJ, 135, 1968
- Arnouts, S., et al. 2001, A&A, 379, 740
- Ashby, L. N. et al. 2009, ApJ, 701, 428
- Barger, A. J., Cowie, L. L., Smail, I., Ivison, R. J., Blain, A. W., Kneib, J.-P. 1999, AJ, 117, 2656
- Bell, E. F., et al., 2005, ApJ, 625, 23
- Bertin, E. & Arnouts, S. 1996, A&AS, 117, 393
- Bolzonella, M., Miralles, J.-M., Pellø, R. 2000, A&A, 363, 476
- Brammer, G. B., van Dokkum, P. G., Coppi, P. 2008, ApJ, 686, 1503
- Bruzual, G., & Charlot, S. 2003, MNRAS, 2, 344, 1000
- Cardamone, C. N., et al. 2008, ApJ, 680, 130
- Cimatti, A., et al. 2002, A&A, 392, 395
- Cimatti, A., et al. 2004, Nature, 430, 184
- Cohen, M., Megeath, S. T., Hammersley, P. L., Martín-Luis, F., Stauffer, J. 2003, AJ125, 2645
- Croom, S. M., Smith, R. J., Boyle, B. J., Shanks, T., Loaring, N. S., Miller, L., Lewis, I. J. 2001, MNRAS, 322, 29
- Daddi, E., Cimatti, A., Pozzetti, L., Hoekstra, H., Röttgering, H. J. A., Renzini, A., Zamorani, G., Mannucci, F. 2000, A&A, 361, 535
- Daddi, E., et al. 2005, ApJ, 626, 680
- Daddi, E., et al. 2007, ApJ, 670, 173
- Dale, D. A. & Helou, G. 2002, ApJ, 576, 159
- Damen, M., Labbé, I., Franx, M, van Dokkum, P. G., Taylor, E. N., Gawiser, E. J. 2009a, ApJ, 690, 937
- Damen, M., Förster Schreiber, N. M., Franx, M., Labbé, I., Toft, S., van Dokkum, P. G., Wuyts, S. 2009b, ApJ, 705, 617
- Dickinson, M, et al. in The Mass of Galaxies at Low and High Redshift: Proc. of the European Southern Observatory and Universitäts-Sternwarte München Workshop, ESO Astrophysics Symposia, Venice, Italy, 2001 October 24-26, ed. R. Bender & A. Renzini (Berlin: Springer), 324
- Doherty, M., Bunker, A. J., Ellis, R. S., McCarthy, P. J. 2005, MNRAS, 361, 525
- Donley, J. L., Rieke, G. H., Pérez-González, P. G., Barro, G. 2008, ApJ, 687, 111
- Elston, R., Rieke, G. H., & Rieke, M. J. 1988, ApJ, 331, 77
- Erben T., et al. 2005, Astron. Nachr., 326, 432
- Fazio, G. G., et al. 2004, ApJS, 154, 10
- Franx, M. et al. 2003, ApJ, 587, 79
- Gawiser, E., et al. 2006, ApJS, 162, 1
- Giacconi, R., et al. 2002 ApJS, 139, 369
- Giavalisco, M., & The GOODS Team 2004, ApJ, 600, L93
- Hildebrandt, H., et al. 2006, A&A, 452, 1121
- Kriek, M., et al. 2008, ApJ, 677, 219
- Kroupa, P. 2001, MNRAS, 322, 231
- Labbé, I. et al. 2003, AJ, 125, 1107
- Labbé, I., Bouwens, R., Illingworth, G, D., Franx, M. 2006, ApJ, 649, 67
- Lacy, M., Canalizo, G., Rawlings, S., Sajina, A., Storrie-Lombardi, L., Armus, L., Marleau, F. R., Muzzin, A. 2005, Mem. Soc. Astron. Ital., 76, 154
- ai, K., et al. 2008, ApJ, 674, 70
- Lehmer et al. 2005, ApJS, 161, 21
- le Fèvre, O., et al. 2004, A&A, 428, 1043
- Lonsdale, C. J. et al. 2003, PASP, 115, 897
- Luo, B., et al. 2008, ApJS, 179, 19
- Luo, B., et al. 2010, ApJS, 187, 560
- Magnelli, B., Elbaz, D., Chary, R. R., Dickinson, M., Le Borgne, D., Frayer, D. T., Willmer, C. N. A. 2009, A&A, 496, 57
- Martin, D. C., et al. 2005, ApJ, 619, 1
- Mignoli, M., et al. 2005, A&A, 437, 883
- Miller, N. A., Formalont, E. B., Kellermann, K. I., Mainieri, V., Norman, C., Padovani, P., Rosati, P., Tozzi, P. 2008, ApJS, 179, 114
- McCarthy, P. J., et al. 2001, ApJ, 560, 131
- Monet, D. G. et al. 2003, AJ, 125, 984
- Papovich, C., et al. 2006, ApJ, 640, 92
- Popesso, P., et al. 2009, A&A, 494, 443
- Quadri, R., et al. 2007, AJ, 134, 1103
- Ravikumar, C. et al. 2007, A&A, 465, 1099
- Reach, W. T., et al. 2005, PASP, 117, 978
- Rix, H.-W, et al. 2004, ApJS, 152, 163
- Rudnick, G., et al. 2003, ApJ, 599, 847
- Sanders, D. B., et al. 2007, ApJS, 172, 86
- Spinrad, H., Dey, A., Stern, D., Dunlop, J., Peacock, J., Jimenez, R., Windhorst, R. 1997, ApJ, 484, 581
- Steidel, C. C., Giavalisco, M., Dickinson, M., Adelberger, K, L. 1996, AJ, 112, 352
- Steidel, C. C., Adelberger, K. L., Giavalisco, M., Dickinson, M., Pettini, M. 1999, ApJ, 519, 1
- Stern, D., et al. 2005, ApJ, 631, 163
- Strolger, L.-G., 2004, ApJ, 613, 200
- Szokoly, G. P., et al. 2004, ApJS, 155, 271 bibitem[Taylor et al. (2009)]enta Taylor, E. N., et al. 2009a, ApJ, 694, 1171
- Taylor, E. N., et al. 2009b, ApJS, 183, 295
- Treister, E., et al. 2009a, ApJ, 693, 1713
- Treister, E., et al. 2009b, ApJ, 706, 535
- Vanzella, E., et al. 2008, A&A, 478, 83
- van der Wel, A., Franx, M., van Dokkum, P. G., Rix, H.-W., Illingworth, G. D., Rosati, P. 2005, ApJ, 631, 145
- van der Wel, A., Franx, M., van Dokkum, P. G., Rix, H.-W. 2004, ApJ, 601, 5
- van Dokkum, P. G., et al. 2006 ApJ, 638, 59
- Wolf, C., et al. 2004, A&A, 421, 913
- Wuyts, S., Labbé, I., Förster-Schreiber, N. M., Franx, M., Rudnick, G., Brammer, G. B., van Dokkum, P. G. 2008, ApJ, 682, 985
- Zheng, X. Z., Bell, E. F., Papovich, C., Wolf, C., Meisenheimer, K., Rix, H.-W., Rieke, G. H., Somerville, R. 2007, ApJ, 661, 41