Two Novel Approaches for Photometric Redshift Estimation based on SDSS and 2MASS

We investigate two training-set methods: support vector machines (SVMs) and Kernel Regression (KR) for photometric redshift estimation with the data from the databases of Sloan Digital Sky Survey Data Release 5 and Two Micron All Sky Survey. We probe the performances of SVMs and KR for different input patterns. Our experiments show that with more parameters considered, the accuracy does not always increase, and only when appropriate parameters are chosen, the accuracy can improve. For different approaches, the best input pattern is different. With different parameters as input, the optimal bandwidth is dissimilar for KR. The rms errors of photometric redshifts based on SVM and KR methods are less than 0.03 and 0.02, respectively. Strengths and weaknesses of the two approaches are summarized. Compared to other methods of estimating photometric redshifts, they show their superiorities, especially KR, in terms of accuracy.     

Two Novel Approaches for Photometric Redshift Estimation based on SDSS and 2MASS

We investigate two training-set methods; support vector machines （SVMs） and Kernel Regression （KR） for photometric redshift estimation with the data from the databases of Sloan Digital Sky Survey Data Release 5 and Two Micron All Sky Survey. We probe the performances of SVMs and KR for different input patterns. Our experiments show that with more parameters considered, the accuracy does not always increase, and only when appropriate parameters are chosen, the accuracy can improve. For different approaches, the best input pattern is different. With different parameters as input, the optimal bandwidth is dissimilar for KR. The rms errors of photometric redshifts based on SVM and KR methods are less than 0.03 and 0.02, respectively. Strengths and weaknesses of the two approaches are summarized. Compared to other methods of estimating photometric redshifts, they show their superiorities, especially KR, in terms of accuracy.     

Photometric redshifts for weak lensing tomography from space: the role of optical and near infrared photometry

We study in detail the photometric redshift requirements needed for tomographic weak gravitational lensing in order to measure accurately the dark energy equation of state. In particular, we examine how ground-based photometry  ( u , g , r , i , z , y )  can be complemented by space-based near-infrared (near-IR) photometry  ( J , H )  , e.g. onboard the planned DUNE satellite. Using realistic photometric redshift simulations and an artificial neural network photo- z method we evaluate the figure of merit for the dark energy parameters  ( w ₀, w _a )  . We consider a DUNE -like broad optical filter supplemented with ground-based multiband optical data from surveys like the Dark Energy Survey, Pan-STARRS and LSST. We show that the dark energy figure of merit would be improved by a factor of 1.3–1.7 if IR filters are added onboard DUNE . Furthermore we show that with IR data catastrophic photo- z outliers can be removed effectively. There is an interplay between the choice of filters, the magnitude limits and the removal of outliers. We draw attention to the dependence of the results on the galaxy formation scenarios encoded into the mock galaxies, e.g. the galaxy reddening. For example, very deep u -band data could be as effective as the IR. We also find that about  10⁵–10⁶  spectroscopic redshifts are needed for calibration of the full survey.     

Photometric redshifts with surface brightness priors

We use galaxy surface brightness as prior information to improve photometric redshift (photo- z ) estimation. We apply our template-based photo- z method to imaging data from the ground-based VVDS survey and the space-based GOODS field from HST , and use spectroscopic redshifts to test our photometric redshifts for different galaxy types and redshifts. We find that the surface brightness prior eliminates a large fraction of outliers by lifting the degeneracy between the Lyman and 4000-Å breaks. Bias and scatter are improved by about a factor of 2 with the prior in each redshift bin in the range  0.4 < z < 1.3  , for both the ground and space data. Ongoing and planned surveys from the ground and space will benefit, provided that care is taken in measurements of galaxy sizes and in the application of the prior. We discuss the image quality and signal-to-noise ratio requirements that enable the surface brightness prior to be successfully applied.     

Photometric redshifts for an optical/near-infrared catalogue in the Chandra Deep Field South

Photometric redshifts have proven a powerful tool in identifying galaxies over a large range of lookback times. We have been generalising this technique to incorporate the selection of candidate high redshift QSOs. We have applied this to a large optical/near-infrared imaging survey in 6 wavebands aiming to push farther in redshift (and fainter in luminosity) than previous studies. We believe that study of these very faint and distant objects provides valuable insights into galaxy formation and evolution. Here we present work in progress and preliminary results for a catalogue of objects detected as part of the Las Campanas Infrared Survey. This is a stepping stone to the type of survey data that will become available in the next few years from projects such as UKIDSS and VISTA. This revised version was published online in August 2006 with corrections to the Cover Date.     

Kernel regression for determining photometric redshifts from Sloan broad-band photometry

We present a new approach, namely kernel regression, to determine photometric redshifts for 399 929 galaxies in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS). Kernel regression is a weighted average of spectral redshifts of the neighbours for a query point, and higher weights are associated with points that are closer to the query point. One important design decision when using kernel regression is the choice of bandwidth. We apply 10-fold cross-validation to choose the optimal bandwidth, which is obtained as the cross-validation error approaches its minimum. The results show that the optimal bandwidth is different for different input patterns: the lowest rms error of photometric redshift estimation arrives at 0.019 using colour+eClass as the inputs, the lowest rms errors comes to 0.020 using ugriz +eClass as the inputs. Where eClass is a galaxy spectral type, and 0.021 using colour+ r as the inputs. Thus, in addition to parameters such as magnitude and colour, eClass is a valid parameter with which to predict photometric redshifts. Moreover, the results suggest that the accuracy of estimating photometric redshifts is improved when the sample is divided into early-type and late-type galaxies; in particular, for early-type galaxies, the rms scatter is 0.016 with colour+eClass as the inputs. In addition, kernel regression achieves high accuracy when predicting the photometric eClass  (σ_rms= 0.034)  using colour+ r as the input pattern. For kernel regression, the accuracy of the photometric redshifts does not always increase with the number of parameters considered, but is satisfactory only when appropriate parameters are chosen. Kernel regression is a comprehensible and accurate regression method. Experiments reveal the superiority of kernel regression over other empirical training approaches.     

SDSS galaxy clustering: luminosity and colour dependence and stochasticity

Differences in clustering properties between galaxy subpopulations complicate the cosmological interpretation of the galaxy power spectrum, but can also provide insights about the physics underlying galaxy formation. To study the nature of this relative clustering, we perform a counts-in-cells analysis of galaxies in the Sloan Digital Sky Survey in which we measure the relative bias between pairs of galaxy subsamples of different luminosities and colours. We use a generalized  χ²  test to determine if the relative bias between each pair of subsamples is consistent with the simplest deterministic linear bias model, and we also use a maximum likelihood technique to further understand the nature of the relative bias between each pair. We find that the simple, deterministic model is a good fit for the luminosity-dependent bias on scales above  ∼2  h ⁻¹ Mpc  , which is good news for using magnitude-limited surveys for cosmology. However, the colour-dependent bias shows evidence for stochasticity and/or non-linearity which increases in strength towards smaller scales, in agreement with previous studies of stochastic bias. Also, confirming hints seen in earlier work, the luminosity-dependent bias for red galaxies is significantly different from that of blue galaxies: both luminous and dim red galaxies have higher bias than moderately bright red galaxies, whereas the biasing of blue galaxies is not strongly luminosity dependent. These results can be used to constrain galaxy formation models and also to quantify how the colour and luminosity selection of a galaxy survey can impact measurements of the cosmological matter power spectrum.     

MegaZ-LRG: a photometric redshift catalogue of one million SDSS luminous red galaxies

We describe the construction of MegaZ-LRG, a photometric redshift catalogue of over one million luminous red galaxies (LRGs) in the redshift range  0.4 < z < 0.7  with limiting magnitude   i < 20  . The catalogue is selected from the imaging data of the Sloan Digital Sky Survey (SDSS) Data Release 4. The 2dF-SDSS LRG and Quasar (2SLAQ) spectroscopic redshift catalogue of 13 000 intermediate-redshift LRGs provides a photometric redshift training set, allowing use of ann z, a neural network-based photometric-redshift estimator. The rms photometric redshift accuracy obtained for an evaluation set selected from the 2SLAQ sample is  σ _z = 0.049  averaged over all galaxies, and  σ _z = 0.040  for a brighter subsample  ( i < 19.0)  . The catalogue is expected to contain ∼5 per cent stellar contamination. The ann z code is used to compute a refined star/galaxy probability based on a range of photometric parameters; this allows the contamination fraction to be reduced to 2 per cent with negligible loss of genuine galaxies. The MegaZ-LRG catalogue is publicly available on the World Wide Web from http://www.2slaq.info .     

The final two redshifts for radio sources from the equatorial BRL sample

Best, Röttgering & Lehnert (BRL) defined a new sample of powerful radio sources from the Molonglo Reference Catalogue, for which redshifts were compiled or measured for 177 of the 178 objects. For the final object, MRC1059−010 (3C 249), the host galaxy is identified here using near-infrared imaging, and the redshift is determined from Very Large Telescope (VLT) spectroscopy. For one other object in the sample, MRC0320+053 (4C05.14), the literature redshift has been questioned: new spectroscopic observations of this object are presented, deriving a corrected redshift. With these two results, the spectroscopic completeness of this sample is now 100 per cent.
New redshifts are also presented for PKS0742+10 from the Wall & Peacock 2.7-GHz catalogue, and for PKS1336+003 from the Parkes Selected Regions. PKS0742+10 shows a strong neutral hydrogen absorption feature in its Lyman α emission profile.     

More redshifts of powerful equatorial radio sources from the Best, Röttgering & Lehnert sample

A new sample of very powerful radio sources, defined from the Molonglo Reference Catalogue, was recently compiled by Best, Röttgering & Lehnert. These authors provided redshifts for 174 of the 178 objects in the sample, making the sample 98 per cent spectroscopically complete. Here, redshifts for three of the remaining galaxies are presented, confirming the optical identifications and raising the spectroscopic completeness of the sample to 99.5 per cent; only 1059−010 (3C 249) is currently without redshift.     

Reconstructing galaxy fundamental distributions and scaling relations from photometric redshift surveys. Applications to the SDSS early-type sample

Noisy distance estimates associated with photometric rather than spectroscopic redshifts lead to a biased estimate of the luminosity distribution, and produce a correlated misestimate of the sizes. We consider a sample of early-type galaxies from the Sloan Digital Sky Survey Data Release 6 for which both spectroscopic and photometric information is available, and apply the generalization of the V _max method to correct for these biases. We show that our technique recovers the true redshift, magnitude and size distributions, as well as the true size–luminosity relation. We find that using only 10 per cent of the spectroscopic information randomly spaced in our catalogue is sufficient for the reconstructions to be accurate within  ∼3 per cent  , when the photometric redshift error is  δ z ≃ 0.038  . We then address the problem of extending our method to deep redshift catalogues, where only photometric information is available. In addition to the specific applications outlined here, our technique impacts a broader range of studies, when at least one distance-dependent quantity is involved. It is particularly relevant for the next generation of surveys, some of which will only have photometric information.     

Clustering Property of Wolf-Rayet Galaxies in the SDSS

We have analysed, for the first time, the clustering properties of Wolf-Rayet (W-R) galaxies, using a large sample of 846 W-R galaxies selected from the Data Release 4 (DR4) of the Sloan Digital Sky Survey (SDSS). We compute the cross-correlation function between W- R galaxies and a reference sample of galaxies drawn from the DR4. We compare the function to the results for control samples of non-W-R star-forming galaxies that are matched closely in redshift, luminosity, concentration, 4000-A break strength and specific star formation rate (SSFR). On scales larger than a few Mpc, W-R galaxies have almost the same clustering amplitude as the control samples, indicating that W-R galaxies and non-W-R control galax- ies populate dark matter haloes of similar masses. On scales between 0.1-1 h-1 Mpc, W-R galaxies are less clustered than the control samples, and the size of the difference depends on the SSFR. Based on both observational and theoretical considerations, we speculate that this negative bias can be interpreted by W-R galaxies residing preferentially at the centers of their dark matter haloes. We examine the distribution of W-R galaxies more closely using the SDSS galaxy group catalogue of Yang et al., and find that ～82% of our W-R galaxies are the central galaxies of groups, compared to ～74% for the corresponding control galaxies. We find that W-R galaxies are hosted, on average, by dark matter haloes of masses of 1012,3 M☉, compared to 1012,1 M? For centrally-located W-R galaxies and 1012,7 M☉ For satellite ones. We would like to point out that this finding, which provides a direct observational support to our conjecture, is really very crude due to the small number of W-R galaxies and the incom- pleteness of the group catalogue, and needs more work in future with larger samples.     

A sample of galaxies near the South Celestial Pole

This paper reports the results of a modest redshift survey carried out, at generally low Galactic latitudes, in the vicinity of the South Celestial Pole. Target galaxies were selected as a 'representative' sample of underlying large-scale structures. Dimensions, approximate magnitudes and radial velocity measurements, are reported for 336 galaxies. Two obvious Seyfert 1 galaxies, one probably Seyfert 1 and three Seyfert 2 galaxies have been discovered. The redshifts are used to supplement existing data and serve to map southern voids and features out to 25 000 km s⁻¹ in the region  270° < l < 330°, 0° > b > −45°  . Three distinct superclusters and twenty apparent voids are tentatively identified. One Void, at   l = 300°, b =−20°, cz = 16 000 km s⁻¹  , with a diameter of 6000 km s⁻¹, is as large as any yet mapped. It appears as part of a general underdense region.     

Error analysis of the photometric redshift technique

We present a calculation of the systematic component of the error budget in the photometric redshift technique. We make use of it to describe a simple technique that allows the assignment of confidence limits to redshift measurements obtained through photometric methods. We show that our technique, through the calculation of a redshift probability function, gives complete information on the probable redshift of an object and its associated confidence intervals. This information can and must be used in the calculation of any observable quantity that makes use of the redshift.     

H I observations of galaxies behind the Milky Way in the Puppis region

We report 21-cm H  i line observations of 161 galaxies located behind the Milky Way, in the Puppis region. The observations have been carried out with the Nançay radio telescope, resulting in 101 detections. Most of the galaxies observed appear in the ESO catalogue, and present apparent diameters larger than 1.6 arcmin. We show that the detection rate is strongly related to the apparent diameter; it is as high as 76 per cent for diameters larger than 1.6 arcmin. Half of the non-detections result, in fact, from an insufficient velocity coverage. Global parameters of the detected galaxies are computed, after a careful discussion of the correction for Galactic absorption. Our H  I data are then compared with those obtained by Kraan-Korteweg & Huchtmeier with the Effelsberg radio telescope for 20 galaxies observed in common: the agreement is excellent, implying respective uncertainties on recession velocities and on H  I fluxes lower than 10 km s⁻¹ and 2 Jy km s⁻¹ on average. Finally, we find that the detected galaxies follow well the correlation between the optical linear diameter and the H  I mass found by Haynes & Giovanelli. This result shows that our diameter corrections for Galactic absorption A_B are quite good, except for eight objects which are heavily obscured ( A_B 2.3), or have a very small diameter, lower than 0.5 arcmin.