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The Revised IRAS-FSC Redshift Catalogue (RIFSCz)

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 Added by Lingyu Wang
 Publication date 2014
  fields Physics
and research's language is English




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We present a Revised IRAS-FSC Redshift Catalogue (RIFSCz) of 60,303 galaxies selected at 60 microns from the IRAS Faint Source Catalogue (FSC). This revision merges in data from the WISE All-Sky Data Release, the Tenth SDSS Data Release (DR10), the GALEX All-Sky Survey Source Catalog (GASC), the 2MASS Redshift Survey (2MRS) and the Planck Catalogue of Compact Sources (PCCS). The RIFSCz consists of accurate position, ultra-violet (UV), optical, near-, mid- and far-infrared, sub-millimetre (sub-mm) and/or radio identifications, spectroscopic redshift (if available) or photometric redshift (if possible), predicted far-infrared and sub-mm fluxes ranging from 12 to 1380 microns based upon the best-fit infrared template. We also provide stellar masses, star-formation rates and dust masses derived from the optical and infrared template fits, where possible. 56 of the galaxies in the RIFSCz have spectroscopic redshifts and a further 26 have photometric redshifts obtained through the template-fitting method. At S60 > 0.36 Jy, the 90% completeness limit of the FSC, 93 of the sources in the RIFSCz have either spectroscopic or photometric redshifts. An interesting subset of the catalogue is the sources detected by Planck at sub-mm wavelengths. 1200 sources have a detection at better than 5 sigma in at least one Planck band and a further 1186 sources have detections at 3-5 sigma in at least one Planck band.



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MiniJPAS is a ~1 deg^2 imaging survey of the AEGIS field in 60 bands, performed to demonstrate the scientific potential of the upcoming JPAS survey. Full coverage of the 3800-9100 AA range with 54 narrow and 6 broad optical filters allow for extremely accurate photo-z, which applied over 1000s of deg^2 will enable new applications of the photo-z technique such as measurement of baryonic acoustic oscillations. In this paper we describe the method used to obtain the photo-z included in the publicly available miniJPAS catalogue, and characterise the photo-z performance. We build 100 AA resolution photo-spectra from the PSF-corrected forced-aperture photometry. Systematic offsets in the photometry are corrected by applying magnitude shifts obtained through iterative fitting with stellar population synthesis models. We compute photo-z with a customised version of LePhare, using a set of templates optimised for the J-PAS filter-set. We analyse the accuracy of miniJPAS photo-z and their dependence on multiple quantities using a subsample of 5,266 galaxies with spectroscopic redshifts from SDSS and DEEP, that we find to be representative of the whole r<23 miniJPAS sample. Formal uncertainties for the photo-z that are calculated with the deltachi^2 method underestimate the actual redshift errors. The odds parameter has the stronger correlation with |Dz|, and accurately reproduces the probability of a redshift outlier (|Dz|>0.03) irrespective of the magnitude, redshift, or spectral type of the sources. We show that the two main summary statistics characterising the photo-z accuracy for a population of galaxies (snmad and eta) can be predicted by the distribution of odds in such population, and use this to estimate them for the whole miniJPAS sample. At r<23 there are 17,500 galaxies/deg^2 with valid photo-z estimates, of which 4,200 are expected to have |Dz|<0.003 (abridged).
86 - A. Canavezes 1997
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The Pan-STARRS1 (PS1) $3pi$ survey is a comprehensive optical imaging survey of three quarters of the sky in the $grizy$ broad-band photometric filters. We present the methodology used in assembling the source classification and photometric redshift (photo-z) catalogue for PS1 $3pi$ Data Release 1, titled Pan-STARRS1 Source Types and Redshifts with Machine learning (PS1-STRM). For both main data products, we use neural network architectures, trained on a compilation of public spectroscopic measurements that has been cross-matched with PS1 sources. We quantify the parameter space coverage of our training data set, and flag extrapolation using self-organizing maps. We perform a Monte-Carlo sampling of the photometry to estimate photo-z uncertainty. The final catalogue contains $2,902,054,648$ objects. On our validation data set, for non-extrapolated sources, we achieve an overall classification accuracy of $98.1%$ for galaxies, $97.8%$ for stars, and $96.6%$ for quasars. Regarding the galaxy photo-z estimation, we attain an overall bias of $left<Delta z_{mathrm{norm}}right>=0.0005$, a standard deviation of $sigma(Delta z_{mathrm{norm}})=0.0322$, a median absolute deviation of $mathrm{MAD}(Delta z_{mathrm{norm}})=0.0161$, and an outlier fraction of $O=1.89%$. The catalogue will be made available as a high-level science product via the Mikulski Archive for Space Telescopes at https://doi.org/10.17909//t9-rnk7-gr88.
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