The Garching-Bonn Deep Survey (GaBoDS) is a virtual 12 square degree cosmic shear and cluster lensing survey, conducted with the [email protected] MPG/ESO telescope at La Silla. It consists of shallow, medium and deep random fields taken in R-band in subarcsecond seeing conditions at high galactic latitude. A substantial amount of the data was taken from the ESO archive, by means of a dedicated ASTROVIRTEL program. In the present work we describe the main characteristics and scientific goals of GaBoDS. Our strategy for mining the ESO data archive is introduced, and we comment on the Wide Field Imager data reduction as well. In the second half of the paper we report on clusters of galaxies found in the background of NGC 300, a random archival field. We use weak gravitational lensing and the red cluster sequence method for the selection of these objects. Two of the clusters found were previously known and already confirmed by spectroscopy. Based on the available data we show that there is significant evidence for substructure in one of the clusters, and an increasing fraction of blue galaxies towards larger cluster radii. Two other mass peaks detected by our weak lensing technique coincide with red clumps of galaxies. We estimate their redshifts and masses.
Aims. We present a cosmic shear analysis and data validation of 15 square degree high-quality R-band data of the Garching-Bonn Deep Survey obtained with the Wide Field Imager of the MPG/ESO 2.2m telescope. Methods. We measure the two-point shear corr
elation functions to calculate the aperture mass dispersion. Both statistics are used to perform the data quality control. Combining the cosmic shear signal with a photometric redshift distribution of a galaxy sub-sample obtained from two square degree of UBVRI-band observations of the Deep Public Survey we determine constraints for the matter density Omega_m, the mass power spectrum normalisation sigma_8 and the dark energy density Omega_Lambda in the magnitude interval R in [21.5,24.5]. In this magnitude interval the effective number density of source galaxies is n=12.5/sq. arcmin, and their mean redshift is z_m=0.78. To estimate the posterior likelihood we employ the Monte Carlo Markov Chain method. Results. Using the aperture mass dispersion we obtain for the mass power spectrum normalisation sigma_8=0.80 +- 0.10 (1 sigma statistical error) at a fixed matter density Omega_m=0.30 assuming a flat universe with negligible baryon content and marginalising over the Hubble parameter and the uncertainties in the fitted redshift distribution.
[ABRIDGED] The weak gravitational lensing effect is used to infer matter density fluctuations within the field-of-view of the Garching-Bonn Deep Survey (GaBoDS). This information is employed for a statistical comparison of the galaxy distribution to
the total matter distribution. The result of this comparison is expressed by means of the linear bias factor, b, the ratio of density fluctuations, and the correlation factor $r$ between density fluctuations. The total galaxy sample is divided into three sub-samples using R-band magnitudes and the weak lensing analysis is applied separately for each sub-sample. Together with the photometric redshifts from the related COMBO-17 survey we estimate the typical mean redshifts of these samples with $bar{z}=0.35, 0.47, 0.61$, respectively. For all three samples, a slight galaxy anti-bias, b~0.8+-0.1, on scales of a few Mpc/h is found; the bias factor shows evidence for a slight scale-dependence. The correlation between galaxy and (dark) matter distribution is high, r~0.6+-0.2, indicating a non-linear or/and stochastic biasing relation between matter and galaxies. Between the three samples no significant evolution with redshift is found.
Aims. The clustering properties of a large sample of U-dropouts are investigated and compared to very precise results for B-dropouts from other studies to identify a possible evolution from z=4 to z=3. Methods. A population of ~8800 candidates for st
ar-forming galaxies at z=3 is selected via the well-known Lyman-break technique from a large optical multicolour survey (the ESO Deep Public Survey). The selection efficiency, contamination rate, and redshift distribution of this population are investigated by means of extensive simulations. Photometric redshifts are estimated for every Lyman-break galaxy (LBG) candidate from its UBVRI photometry yielding an empirical redshift distribution. The measured angular correlation function is deprojected and the resulting spatial correlation lengths and slopes of the correlation function of different subsamples are compared to previous studies. Results. By fitting a simple power law to the correlation function we do not see an evolution in the correlation length and the slope from other studies at z=4 to our study at z=3. In particular, the dependence of the slope on UV-luminosity similar to that recently detected for a sample of B-dropouts is confirmed also for our U-dropouts. For the first time number statistics for U-dropouts are sufficient to clearly detect a departure from a pure power law on small scales down to ~2 reported by other groups for B-dropouts.
We present first results of our search for high-redshift galaxies in deep CCD mosaic images. As a pilot study for a larger survey, very deep images of the Chandra Deep Field South (CDFS), taken withWFI@MPG/ESO2.2m, are used to select large samples of
1070 U-band and 565 B-band dropouts with the Lyman-break method. The data of these Lyman-break galaxies are made public as an electronic table. These objects are good candidates for galaxies at z~3 and z~4 which is supported by their photometric redshifts. The distributions of apparent magnitudes and the clustering properties of the two populations are analysed, and they show good agreement to earlier studies. We see no evolution in the comoving clustering scale length from z~3 to z~4. The techniques presented here will be applied to a much larger sample of U-dropouts from the whole survey in near future.
We present our image processing system for the reduction of optical imaging data from multi-chip cameras. In the framework of the Garching Bonn Deep Survey (GaBoDS; Schirmer et al. 2003) consisting of about 20 square degrees of high-quality data from
WFI@MPG/ESO 2.2m, our group developed an imaging pipeline for the homogeneous and efficient processing of this large data set. Having weak gravitational lensing as the main science driver, our algorithms are optimised to produce deep co-added mosaics from individual exposures obtained from empty field observations. However, the modular design of our pipeline allows an easy adaption to different scientific applications. Our system has already been ported to a large variety of optical instruments and its products have been used in various scientific contexts. In this paper we give a thorough description of the algorithms used and a careful evaluation of the accuracies reached. This concerns the removal of the instrumental signature, the astrometric alignment, photometric calibration and the characterisation of final co-added mosaics. In addition we give a more general overview on the image reduction process and comment on observing strategies where they have significant influence on the data quality.
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