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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.
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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.
The luminosity functions of galaxies and quasars provide invaluable information about galaxy and quasar formation. Estimating the luminosity function from magnitude limited samples is relatively straightforward, provided that the distances to the objects in the sample are known accurately; techniques for doing this have been available for about thirty years. However, distances are usually known accurately for only a small subset of the sample. This is true of the objects in the Sloan Digital Sky Survey, and will be increasingly true of the next generation of deep multi-color photometric surveys. Estimating the luminosity function when distances are only known approximately (e.g., photometric redshifts are available, but spectroscopic redshifts are not) is more difficult. I describe two algorithms which can handle this complication: one is a generalization of the V_max algorithm, and the other is a maximum likelihood approach. Because these methods account for uncertainties in the distance estimate, they impact a broader range of studies. For example, they are useful for studying the abundances of galaxies which are sufficiently nearby that the contribution of peculiar velocity to the spectroscopic redshift is not negligible, so only a noisy estimate of the true distance is available. In this respect, peculiar velocities and photometric redshift errors have similar effects. The methods developed here are also useful for estimating the stellar luminosity function in samples where accurate parallax distances are not available.
We outline how redshift-space distortions (RSD) can be measured from the angular correlation function w({theta}), of galaxies selected from photometric surveys. The natural degeneracy between RSD and galaxy bias can be minimized by comparing results from bins with top-hat galaxy selection in redshift, and bins based on the radial position of galaxy pair centres. This comparison can also be used to test the accuracy of the photometric redshifts. The presence of RSD will be clearly detectable with the next generation of photometric redshift surveys. We show that the Dark Energy Survey (DES) will be able to measure f(z){sigma}_8(z) to a 1{sigma} accuracy of (17 {times} b)%, using galaxies drawn from a single narrow redshift slice centered at z = 1. Here b is the linear bias, and f is the logarithmic rate of change of the linear growth rate with respect to the scale factor. Extending to measurements of w({theta}) for a series of bins of width 0.02(1 + z) over 0.5 < z < 1.4 will measure {gamma} to a 1{sigma} accuracy of 25%, given the model f = {Omega}_m(z)^{gamma}, and assuming a linear bias model that evolves such that b = 0.5 + z (and fixing other cosmological parameters). The accuracy of our analytic predictions is confirmed using mock catalogs drawn from simulations conducted by the MICE collaboration.
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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