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Register a new userPhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys
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Improving distance measurements in large imaging surveys is a major challenge to better reveal the distribution of galaxies on a large scale and to link galaxy properties with their environments. Photometric redshifts can be efficiently combined with the cosmic web (CW) extracted from overlapping spectroscopic surveys to improve their accuracy. We apply a similar method using a new generation of photometric redshifts based on a convolution neural network (CNN). The CNN is trained on the SDSS images with the main galaxy sample (SDSS-MGS, $r leq 17.8$) and the GAMA spectroscopic redshifts up tor $sim 19.8$. The mapping of the CW is obtained with 680,000 spectroscopic redshifts from the MGS and BOSS surveys. The redshift probability distribution functions (PDF), which are well calibrated (unbiased and narrow, $leq 120$ Mpc), intercept a few CW structure along the line of sight. Combining these PDFs with the density field distribution provides new photometric redshifts, $z_{web}$, whose accuracy is improved by a factor of two (i.e.,${sigma} sim 0.004(1+z)$) for galaxies with $r leq 17.8$. For half of them, the distance accuracy is better than 10 cMpc. The narrower the original PDF, the larger the boost in accuracy. No gain is observed for original PDFs wider than 0.03. The final $z_{web}$ PDFs also appear well calibrated. The method performs slightly better for passive galaxies than star-forming ones, and for galaxies in massive groups since these populations better trace the underlying large-scale structure. Reducing the spectroscopic sampling by a factor of 8 still improves the photometric redshift accuracy by 25%. Extending the method to galaxies fainter than the MGS limit still improves the redshift estimates for 70% of the galaxies, with a gain in accuracy of 20% at low $z$ where the resolution of the CW is the highest.
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We investigate the photometric redshift accuracy achievable with the AKARI infrared data in deep multi-band surveys, such as in the North Ecliptic Pole field. We demonstrate that the passage of redshifted policyclic aromatic hydrocarbons and silicate features into the mid-infrared wavelength window covered by AKARI is a valuable means to recover the redshifts of starburst galaxies. To this end we have collected a sample of ~60 galaxies drawn from the GOODS-North Field with spectroscopic redshift 0.5<~z_spec<~1.5 and photometry from 3.6 to 24 micron, provided by the Spitzer, ISO and AKARI satellites. The infrared spectra are fitted using synthetic galaxy Spectral Energy Distributions which account for starburst and active nuclei emission. For ~90% of the sources in our sample the redshift is recovered with an accuracy |z_phot-z_spec|/(1+z_spec)<~10%. A similar analysis performed on different sets of simulated spectra shows that the AKARI infrared data alone can provide photometric redshifts accurate to |z_phot-z_spec|/(1+z_spec)<~10% (1-sigma) at z<~2. At higher redshifts the PAH features are shifted outside the wavelength range covered by AKARI and the photo-z estimates rely on the less prominent 1.6 micron stellar bump; the accuracy achievable in this case on (1+z) is ~10-15%, provided that the AGN contribution to the infrared emission is subdominant. Our technique is no more prone to redshift aliasing than optical-uv photo-z, and it may be possible to reduce this aliasing further with the addition of submillimetre and/or radio data.
Future radio surveys will generate catalogues of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating the redshifts of radio sources. As a starting point in this process, we evaluate a number of machine-learning techniques for estimating redshift, together with a conventional template-fitting technique. We pay special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multi-wavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best with high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts.
The kinetic Sunyaev Zeldovich effect (kSZ) effect is a potentially powerful probe to the missing baryons. However, the kSZ signal is overwhelmed by various contaminations and the cosmological application is hampered by loss of redshift information due to the projection effect. We propose a kSZ tomography method to alleviate these problems, with the aid of galaxy spectroscopic redshift surveys. We propose to estimate the large scale peculiar velocity through the 3D galaxy distribution, weigh it by the 3D galaxy density and adopt the product projected along the line of sight with a proper weighting as an estimator of the true kSZ temperature fluctuation $Theta$. We thus propose to measure the kSZ signal through the $Hat{Theta}$-$Theta$ cross correlation. This approach has a number of advantages (see details in the abstract of the paper). We test the proposed kSZ tomography against non-adiabatic and adiabatic hydrodynamical simulations. We confirm that $hat{Theta}$ is indeed tightly correlated with $Theta$ at $kla 1h/$Mpc, although nonlinearities in the density and velocity fields and nonlinear redshift distortion do weaken the tightness of the $hat{Theta}$-$Theta$ correlation. We further quantify the reconstruction noise in $Hat{Theta}$ from galaxy distribution shot noise. Based on these results, we quantify the applicability of the proposed kSZ tomography for future surveys. We find that, in combination with the BigBOSS-N spectroscopic redshift survey, the PLANCK CMB experiment will be able to detect the kSZ with an overall significance of $sim 50sigma$ and further measure its redshift distribution at many redshift bins over $0<z<2$.
With a recently constructed composite quasar spectrum and the chi^2 minimization technique, we demonstrated a general method to estimate the photometric redshifts of a large sample of quasars by deriving the theoretical color-redshift relations and comparing the theoretical colors with the observed ones. We estimated the photometric redshifts from the 5-band SDSS photometric data of 18678 quasars in the first major data release of SDSS and compare them with the spectroscopic redshifts. The redshift difference is smaller than 0.1 for 47% of quasars and 0.2 for 68 % of them. Based on the calculation of the theoretical color-color diagrams of stars, galaxies and quasars in both the SDSS and BATC photometric systems, we expected that with the BATC system of 15 intermediate filters we would be able to select candidates of high redshift quasars more efficiently than in the SDSS, provided the BATC survey could detect objects with magnitude fainter than 21.
The coming decade will be an exciting period for dark energy research, during which astronomers will address the question of what drives the accelerated cosmic expansion as first revealed by type Ia supernova (SN) distances, and confirmed by later observations. The mystery of dark energy poses a challenge of such magnitude that, as stated by the Dark Energy Task Force (DETF), nothing short of a revolution in our understanding of fundamental physics will be required to achieve a full understanding of the cosmic acceleration. The lack of multiple complementary precision observations is a major obstacle in developing lines of attack for dark energy theory. This lack is precisely what next-generation surveys will address via the powerful techniques of weak lensing (WL) and baryon acoustic oscillations (BAO) -- galaxy correlations more generally -- in addition to SNe, cluster counts, and other probes of geometry and growth of structure. Because of their unprecedented statistical power, these surveys demand an accurate understanding of the observables and tight control of systematics. This white paper highlights the opportunities, approaches, prospects, and challenges relevant to dark energy studies with wide-deep multiwavelength photometric redshift surveys. Quantitative predictions are presented for a 20000 sq. deg. ground-based 6-band (ugrizy) survey with 5-sigma depth of r~27.5, i.e., a Stage 4 survey as defined by the DETF.
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