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Register a new userThe PAU Survey: Early demonstration of photometric redshift performance in the COSMOS field
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Added by
Martin B{\\o}rstad Eriksen
Publication date
2018
fields
Physics
and research's language is
English
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The PAU Survey (PAUS) is an innovative photometric survey with 40 narrow bands at the William Herschel Telescope (WHT). The narrow bands are spaced at 100AA intervals covering the range 4500AA to 8500AA and, in combination with standard broad bands, enable excellent redshift precision. This paper describes the technique, galaxy templates and additional photometric calibration used to determine early photometric redshifts from PAUS. Using BCNz2, a new photometric redshift code developed for this purpose, we characterise the photometric redshift performance using PAUS data on the COSMOS field. Comparison to secure spectra from zCOSMOS DR3 shows that PAUS achieves $sigma_{68} /(1+z) = 0.0037$ to $i_{mathrm{AB}} < 22.5$ when selecting the best 50% of the sources based on a photometric redshift quality cut. Furthermore, a higher photo-z precision ($sigma_{68}/(1+z) sim 0.001$) is obtained for a bright and high quality selection, which is driven by the identification of emission lines. We conclude that PAUS meets its design goals, opening up a hitherto uncharted regime of deep, wide, and dense galaxy survey with precise redshifts that will provide unique insights into the formation, evolution and clustering of galaxies, as well as their intrinsic alignments.
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We present -- and make publicly available -- accurate and precise photometric redshifts in the ACS footprint from the COSMOS field for objects with $i_{mathrm{AB}}leq 23$. The redshifts are computed using a combination of narrow band photometry from PAUS, a survey with 40 narrow bands spaced at $100r{A}$ intervals covering the range from $4500r{A}$ to $8500r{A}$, and 26 broad, intermediate, and narrow bands covering the UV, visible and near infrared spectrum from the COSMOS2015 catalogue. We introduce a new method that models the spectral energy distributions (SEDs) as a linear combination of continuum and emission line templates and computes its Bayes evidence, integrating over the linear combinations. The correlation between the UV luminosity and the OII line is measured using the 66 available bands with the zCOSMOS spectroscopic sample, and used as a prior which constrains the relative flux between continuum and emission line templates. The flux ratios between the OII line and $mathrm{H}_{alpha}$, $mathrm{H}_{beta}$ and $mathrm{OIII}$ are similarly measured and used to generate the emission line templates. Comparing to public spectroscopic surveys via the quantity $Delta_zequiv(z_{mathrm{photo}}-z_{mathrm{spec}})/(1+z_{mathrm{spec}})$, we find the photometric redshifts to be more precise than previous estimates, with $sigma_{68}(Delta_z) approx (0.003, 0.009)$ for galaxies at magnitude $i_{mathrm{AB}}sim18$ and $i_{mathrm{AB}}sim23$, respectively, which is $3times$ and $1.66times$ tighter than COSMOS2015. Additionally, we find the redshifts to be very accurate on average, yielding a median of the $Delta_z$ distribution compatible with $|mathrm{median}(Delta_z)|leq0.001$ at all redshifts and magnitudes considered. Both the added PAUS data and new methodology contribute significantly to the improved results.
In this paper we introduce the textsc{Deepz} deep learning photometric redshift (photo-$z$) code. As a test case, we apply the code to the PAU survey (PAUS) data in the COSMOS field. textsc{Deepz} reduces the $sigma_{68}$ scatter statistic by 50% at $i_{rm AB}=22.5$ compared to existing algorithms. This improvement is achieved through various methods, including transfer learning from simulations where the training set consists of simulations as well as observations, which reduces the need for training data. The redshift probability distribution is estimated with a mixture density network (MDN), which produces accurate redshift distributions. Our code includes an autoencoder to reduce noise and extract features from the galaxy SEDs. It also benefits from combining multiple networks, which lowers the photo-$z$ scatter by 10 percent. Furthermore, training with randomly constructed coadded fluxes adds information about individual exposures, reducing the impact of photometric outliers. In addition to opening up the route for higher redshift precision with narrow bands, these machine learning techniques can also be valuable for broad-band surveys.
We present the first measurements of the projected clustering and intrinsic alignments (IA) of galaxies observed by the Physics of the Accelerating Universe Survey (PAUS). With photometry in 40 narrow optical passbands ($450rm{nm}-850rm{nm}$), the quality of photometric redshift estimation is $sigma_{z} sim 0.01(1 + z)$ for galaxies in the $19,rm{deg}^{2}$ Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) W3 field, allowing us to measure the projected 3D clustering and IA for flux-limited, faint galaxies ($i < 22.5$) out to $zsim0.8$. To measure two-point statistics, we developed, and tested with mock photometric redshift samples, `cloned random galaxy catalogues which can reproduce data selection functions in 3D and account for photometric redshift errors. In our fiducial colour-split analysis, we made robust null detections of IA for blue galaxies and tentative detections of radial alignments for red galaxies ($sim1-3sigma$), over scales of $0.1-18,h^{-1}rm{Mpc}$. The galaxy clustering correlation functions in the PAUS samples are comparable to their counterparts in a spectroscopic population from the Galaxy and Mass Assembly survey, modulo the impact of photometric redshift uncertainty which tends to flatten the blue galaxy correlation function, whilst steepening that of red galaxies. We investigate the sensitivity of our correlation function measurements to choices in the random catalogue creation and the galaxy pair-binning along the line of sight, in preparation for an optimised analysis over the full PAUS area.
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).
We present a catalog of 10718 objects in the COSMOS field observed through multi-slit spectroscopy with the Deep Imaging Multi-Object Spectrograph (DEIMOS) on the Keck II telescope in the wavelength range ~5500-9800A. The catalog contains 6617 objects with high-quality spectra (two or more spectral features), and 1798 objects with a single spectroscopic feature confirmed by the photometric redshift. For 2024 typically faint objects we could not obtain reliable redshifts. The objects have been selected from a variety of input catalogs based on multi-wavelength observations in the field, and thus have a diverse selection function, which enables the study of the diversity in the galaxy population. The magnitude distribution of our objects is peaked at I_AB~23 and K_AB~21, with a secondary peak at K_AB~24. We sample a broad redshift distribution in the range 0<z<6, with one peak at z~1, and another one around z~4. We have identified 13 redshift spikes at z>0.65 with chance probabilities <4xE-4$, some of which are clearly related to protocluster structures of sizes >10 Mpc. An object-to-object comparison with a multitude of other spectroscopic samples in the same field shows that our DEIMOS sample is among the best in terms of fraction of spectroscopic failures and relative redshift accuracy. We have determined the fraction of spectroscopic blends to about 0.8% in our sample. This is likely a lower limit and at any rate well below the most pessimistic expectations. Interestingly, we find evidence for strong lensing of Ly-alpha background emitters within the slits of 12 of our target galaxies, increasing their apparent density by about a factor of 4.
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