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Register a new userStructure detection in the D1 CFHTLS deep field using accurate photometric redshifts: a benchmark
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We investigate structures in the D1 CFHTLS deep field in order to test the method that will be applied to generate homogeneous samples of clusters and groups of galaxies in order to constrain cosmology and detailed physics of groups and clusters. Adaptive kernel technique is applied on galaxy catalogues. This technique needs none of the usual a-priori assumptions (luminosity function, density profile, colour of galaxies) made with other methods. Its main drawback (decrease of efficiency with increasing background) is overcame by the use of narrow slices in photometric redshift space. There are two main concerns in structure detection. One is false detection and the second, the evaluation of the selection function in particular if one wants complete samples. We deal here with the first concern using random distributions. For the second, comparison with detailed simulations is foreseen but we use here a pragmatic approach with comparing our results to GalICS simulations to check that our detection number is not totally at odds compared to cosmological simulations. We use XMM-LSS survey and secured VVDS redshifts up to z~1 to check individual detections. We show that our detection method is basically capable to recover (in the regions in common) 100% of the C1 XMM-LSS X-ray detections in the correct redshift range plus several other candidates. Moreover when spectroscopic data are available, we confirm our detections, even those without X-ray data.
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In order to enlarge publicly available optical cluster catalogs, in particular at high redshift, we have performed a systematic search for clusters of galaxies in the CFHTLS. We used the Le Phare photometric redshifts for the galaxies detected with magnitude limits of i=25 and 23 for the Deep and Wide fields respectively. We then constructed galaxy density maps in photometric redshift bins of 0.1 based on an adaptive kernel technique and detected structures with SExtractor. In order to assess the validity of our cluster detection rates, we applied a similar procedure to galaxies in Millennium simulations. We measured the correlation function of our cluster candidates. We analyzed large scale properties and substructures by applying a minimal spanning tree algorithm both to our data and to the Millennium simulations. We have detected 1200 candidate clusters with various masses (minimal masses between 1.0 10$^{13}$ and 5.5 10$^{13}$ and mean masses between 1.3 10$^{14}$ and 12.6 10$^{14}$ M$_odot$), thus notably increasing the number of known high redshift cluster candidates. We found a correlation function for these objects comparable to that obtained for high redshift cluster surveys. We also show that the CFHTLS deep survey is able to trace the large scale structure of the universe up to z$geq$1. Our detections are fully consistent with those made in various CFHTLS analyses with other methods. We now need accurate mass determinations of these structures to constrain cosmological parameters.
We present photometric redshifts for an uniquely large and deep sample of 522286 objects with i_{AB}<25 in the Canada-France Legacy Survey ``Deep Survey fields, which cover a total effective area of 3.2 deg^2. We use 3241 spectroscopic redshifts with 0<z<5 from the VIMOS VLT Deep Survey as a calibration to derive these photometric redshifts. We devise a robust calibration method which removes systematic trends in the photometric redshifts and significantly reduces the fraction of catastrophic errors. We use our unique spectroscopic sample to present a detailed assessment of the robustness of the photometric redshift sample. For a sample selected at i_{AB}<24, we reach a redshift accuracy of sigma_{Delta z/(1+z)}=0.037 with eta=3.7% of catastrophic error. The reliability of our photometric redshifts is lower for fainter objects: we find sigma_{Delta z/(1+z)}=0.029, 0.043 and eta=1.7%, 5.4% for samples selected at i_{AB}=17.5-22.5 and 22.5-24 respectively. We find that the photometric redshifts of starburst galaxies in our sample are less reliable: although these galaxies represent only 18% of the spectroscopic sample they are responsible for 54% of the catastrophic errors. We find an excellent agreement between the photometric and the VVDS spectroscopic redshift distributions at i_{AB}<24. Finally, we compare the redshift distributions of i selected galaxies on the four CFHTLS deep fields, showing that cosmic variance is already present on fields of 0.8 deg^2.
Photometric redshifts, which have become the cornerstone of several of the largest astronomical surveys like PanStarrs, DES, J-PAS or the LSST, require precise measurements of galaxy photometry in different bands using a consistent physical aperture. This is not trivial, due to the variation in the shape and width of the Point Spread Function (PSF) introduced by wavelength differences, instrument positions and atmospheric conditions. Current methods to correct for this effect rely on a detailed knowledge of the PSF characteristics as a function of the survey coordinates, which can be difficult due to the relative paucity of stars tracking the PSF behaviour. Here we show that it is possible to measure accurate, consistent multicolour photometry without knowing the shape of PSF. The Chebyshev-Fourier Functions (CHEFs) can fit the observed profile of each object and produce high signal-to-noise integrated flux measurements unaffected by the PSF. These total fluxes, which encompass all the galaxy populations, are much more useful for Galaxy Evolution studies than aperture photometry. We compare the total magnitudes and colours obtained using our software to traditional photometry with SExtractor, using real data from the COSMOS survey and the Hubble Ultra Deep Field. We also apply the CHEFs technique to the recently published Extreme Deep Field and compare the results to those from ColorPro on the HUDF. We produce a photometric catalogue with 35732 sources (10823 with S/N>5), reaching a photometric redshift precision of 2% due to the extraordinary depth and wavelength coverage of the XDF images.
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce several billion photometric redshifts (photo-$z$s), enabling cosmological analyses to select a subset of galaxies with the most accurate photo-$z$. We perform initial redshift fits on Subaru Strategic Program galaxies with deep $grizy$ photometry using Trees for Photo-Z (TPZ) before applying a custom neural network classifier (NNC) tuned to select galaxies with $(z_mathrm{phot} - z_mathrm{spec})/(1+z_mathrm{spec}) < 0.10$. We consider four cases of training and test sets ranging from an idealized case to using data augmentation to increase the representation of dim galaxies in the training set. Selections made using the NNC yield significant further improvements in outlier fraction and photo-$z$ scatter ($sigma_z$) over those made with typical photo-$z$ uncertainties. As an example, when selecting the best third of the galaxy sample, the NNC achieves a 35% improvement in outlier rate and a 23% improvement in $sigma_z$ compared to using uncertainties from TPZ. For cosmology and galaxy evolution studies, this method can be tuned to retain a particular sample size or to achieve a desired photo-$z$ accuracy; our results show that it is possible to retain more than a third of an LSST-like galaxy sample while reducing $sigma_z$ by a factor of two compared to the full sample, with one-fifth as many photo-$z$ outliers. For surveys like LSST that are not limited by shot noise, this method enables a larger number of tomographic redshift bins and hence a significant increase in the total signal-to-noise of galaxy angular power spectra.
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