In this paper, we describe the design and data analysis of the DEEP2 Galaxy Redshift Survey, the densest and largest high-precision redshift survey of galaxies at z ∼ 1 completed to date. The survey was designed to conduct a comprehensive census of massive galaxies, their properties, environments, and large-scale structure down to absolute magnitude M B = −20 at z ∼ 1 via ∼ 90 nights of observation on the Keck telescope. The survey covers an area of 2.8 deg 2 divided into four separate fields observed to a limiting apparent magnitude of R AB = 24.1. Objects with z < ∼ 0.7 are readily identifiable using BRI photometry and rejected in three of the four DEEP2 fields, allowing galaxies with z > 0.7 to be targeted ∼ 2.5 times more efficiently than in a purely magnitude-limited sample. Approximately sixty percent of eligible targets are chosen for spectroscopy, yielding nearly 53,000 spectra and more than 38,000 reliable redshift measurements. Most of the targets which fail to yield secure redshifts are blue objects that lie beyond z ∼ 1.45, where the [O II] 3727 Å doublet lies in the infrared. The DEIMOS 1200-line/mm grating used for the survey delivers high spectral resolution (R ∼ 6000), accurate and secure redshifts, and unique internal kinematic information. Extensive ancillary data are available in the DEEP2 fields, particularly in the Extended Groth Strip, which has evolved into one of the richest multiwavelength regions on the sky. This paper is intended as a handbook for users of the DEEP2 Data Release 4, which includes all DEEP2 spectra and redshifts, as well as for the DEEP2 DEIMOS data reduction pipelines. Extensive details are provided on object selection, mask design, biases in target selection and redshift measurements, the spec2d two-dimensional data-reduction pipeline, the spec1d automated redshift pipeline, and the zspec visual redshift verification process, along with examples of instrumental signatures or other artifacts that in some cases remain after data reduction. Redshift errors and catastrophic failure rates are assessed through more than 2000 objects with duplicate observations. Sky subtraction is essentially photon-limited even under bright OH sky lines; we describe the strategies that permitted this, based on high image stability, accurate wavelength solutions, and powerful b-spline modeling methods. Summary data are given that demonstrate the superiority of DEEP2 over other deep redshift surveys at z ∼ 1 in terms of galaxy numbers, redshift accuracy, sample number density, and amount of spectral information. We also provide an overview of the scientific highlights of the DEEP2 survey thus far.

Many of the cosmological tests to be performed by planned dark energy experiments will require extremely well-characterized photometric redshift measurements. Current estimates for cosmic shear are that the true mean redshift of the objects in each photo-z bin must be known to better than 0.002(1 + z), and the width of the bin must be known to ∼ 0.003(1 + z) if errors in cosmological measurements are not to be degraded significantly. A conventional approach is to calibrate these photometric redshifts with large sets of spectroscopic redshifts. However, at the depths probed by Stage III surveys (such as DES), let alone Stage IV (LSST, JDEM, Euclid), existing large redshift samples have all been highly (25-60%) incomplete, with a strong dependence of success rate on both redshift and galaxy properties. A powerful alternative approach is to exploit the clustering of galaxies to perform photometric redshift calibrations. Measuring the two-point angular cross-correlation between objects in some photometric redshift bin and objects with known spectroscopic redshift, as a function of the spectroscopic z, allows the true redshift distribution of a photometric sample to be reconstructed in detail, even if it includes objects too faint for spectroscopy or if spectroscopic samples are highly incomplete. We test this technique using mock DEEP2 Galaxy Redshift survey light cones constructed from the Millennium Simulation semi-analytic galaxy catalogs. From this realistic test, which incorporates the effects of galaxy bias evolution and cosmic variance, we find that the true redshift distribution of a photometric sample can, in fact, be determined accurately with cross-correlation techniques. We also compare the empirical error in the reconstruction of redshift distributions to previous analytic predictions, finding that additional components must be included in error budgets to match the simulation results. This extra error contribution is small for surveys which sample large areas of sky (>∼10-100 degrees), but dominant for ∼ 1 square degree fields. We conclude by presenting a step-by-step, optimized recipe for reconstructing redshift distributions from cross-correlation information using standard correlation measurements. Subject headings: galaxies: distances and redshifts -large-scale structure of the universe -surveys -cosmology: observations

Ongoing and near-future imaging-based dark energy experiments are critically dependent upon photometric redshifts (a.k.a. photo-z's): i.e., estimates of the redshifts of objects based only on flux information obtained through broad filters. Higher-quality, lower-scatter photo-z's will result in smaller random errors on cosmological parameters; while systematic errors in photometric redshift estimates, if not constrained, may dominate all other uncertainties from these experiments. The desired optimization and calibration is dependent upon spectroscopic measurements for secure redshift information; this is the key application of galaxy spectroscopy for imaging-based dark energy experiments.Hence, to achieve their full potential, imaging-based experiments will require large sets of objects with spectroscopically-determined redshifts, for two purposes:• Training: Objects with known redshift are needed to map out the relationship between object color and z (or, equivalently, to determine empirically-calibrated templates describing the restframe spectra of the full range of galaxies, which may be used to predict the color-z relation). The ultimate goal of training is to minimize each moment of the distribution of differences between photometric redshift estimates and the true redshifts of objects, making the relationship between them as tight as possible. The larger and more complete our "training set" of spectroscopic redshifts is, the smaller the RMS photo-z errors should be, increasing the constraining power of imaging experiments.Requirements: Spectroscopic redshift measurements for ∼30,000 objects over >∼15 widelyseparated regions, each at least ∼20 arcmin in diameter, and reaching the faintest objects used in a given experiment, will likely be necessary if photometric redshifts are to be trained and calibrated with conventional techniques. Larger, more complete samples (i.e., with longer exposure times) can improve photo-z algorithms and reduce scatter further, enhancing the science return from planned experiments greatly (increasing the Dark Energy Task Force figure of merit by up to ∼50%).

Computer navigation assistance in total knee arthroplasty (TKA) results in consistently accurate alignment of prostheses. We aimed to compare the outcome of computer-navigated and conventional TKA and to analyse the radiologically malaligned knees. We analysed 637 primary TKA, carried out by a single surgeon, over five consecutive years and divided them into two cohorts: group 1 = STA (standard instrumentation) and group 2 = CAS (computer-assisted surgery). There was no significant difference between the average Oxford Knee Scores (OKS) of the two groups at any time from one to five years. However, the malaligned TKA at three years had a worse OKS. At medium term there is no difference in clinical outcome measures that can be attributed to the surgeon having used computer-assisted navigation for TKA. But group 1, having a higher proportion of malaligned TKA, might show worsening of OKS at long term.

This paper describes a new catalog that supplements the existing DEEP2 Galaxy Redshift Survey photometric and spectroscopic catalogs with ugriz photometry from two other surveys; the Canada-France-Hawaii Legacy Survey (CFHTLS) and the Sloan Digital Sky Survey (SDSS). Each catalog is cross-matched by position on the sky in order to assign ugriz photometry to objects in the DEEP2 catalogs. We have recalibrated the CFHTLS photometry where it overlaps DEEP2 in order to provide a more uniform dataset. We have also used this improved photometry to predict DEEP2 BRI photometry in regions where only poorer measurements were available previously. In addition, we have included improved astrometry tied to SDSS rather than USNO-A2.0 for all DEEP2 objects. In total this catalog contains ∼ 27, 000 objects with full ugriz photometry as well as robust spectroscopic redshift measurements, 64% of which have r > 23. By combining the secure and accurate redshifts of the DEEP2 Galaxy Redshift Survey with ugriz photometry, we have created a catalog that can be used as an excellent testbed for future photo-z studies, including tests of algorithms for surveys such as LSST and DES.