SUPERNOVA ACCELERATION PROBE: INVESTIGATING PHOTOMETRIC REDSHIFT OPTIMIZATION

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2 − 2.6 redshift range that theEuclidmission will probe. We designed a challenge using emulatedEucliddata drawn from three photometric surveys of the COSMOS field. The data was divided into two samples: one calibration sample for which photometry and redshifts were provided to the participants; and the validation sample, containing only the photometry to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates the sources they consider unfit for use in cosmological analyses. The performance of each method was assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, that is to say sources for which the photo-zdeviates by more than 0.15(1 + z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, for examplez > 1. However they generally perform better than template-fitting methods at low redshift (z < 0.7), indicating that template-fitting methods do not use all of the information contained in the photometry. We introduce metrics that quantify both photo-zprecision and completeness of the samples (post-rejection), since both contribute to the final figure of merit of the science goals of the survey (e.g., cosmic shear fromEuclid). Template-fitting methods provide the best results in these metrics, but we show that a combination of template-fitting results and machine-learning results with rejection criteria can outperform any individual method. On this basis, we argue that further work in identifying how to best select between machine-learning and template-fitting approaches for each individual galaxy should be pursued as a priority.

We present photometric redshifts for the COSMOS survey derived from a new code, optimized to yield accurate and reliable redshifts and spectral types of galaxies down to faint magnitudes and redshifts out to z ~ 1.2. The technique uses χ^2 template fitting, combined with luminosity function priors and with the option to estimate the internal extinction [or E(B - V)]. The median most probable redshift, best-fit spectral type and reddening, absolute magnitude, and stellar mass are derived in addition to the full redshift probability distributions. Using simulations with sampling and noise similar to those in COSMOS, the accuracy and reliability is estimated for the photometric redshifts as a function of the magnitude limits of the sample, S/N ratios, and the number of bands used. We find from the simulations that the ratio of derived 95% confidence interval in the χ^2 probability distribution to the estimated photometric redshift (D_(95)) can be used to identify and exclude the catastrophic failures in the photometric redshift estimates. To evaluate the reliability of the photometric redshifts, we compare the derived redshifts with high-reliability spectroscopic redshifts for a sample of 868 normal galaxies with z < 1.2 from zCOSMOS. Considering different scenarios, depending on using prior, no prior, and/or extinction, we compare the photometric and spectroscopic redshifts for this sample. The rms scatter between the estimated photometric redshifts and known spectroscopic redshifts is σ(Δ(z)) = 0.031, where Δ(z) = (z_(phot) - z_(spec))/(1 + z_(spec)) with a small fraction of outliers (<2.5%) [outliers are defined as objects with Δ(z) > 3σ(Δ(z)), where σ(Δ(z)) is the rms scatter in Δ(z)]. We also find good agreement [σ(Δ(z)) = 0.10] between photometric and spectroscopic redshifts for type II AGNs. We compare results from our photometric redshift procedure with three other independent codes and find them in excellent agreement. We show preliminary results, based on photometric redshifts for the entire COSMOS sample (to i < 25 mag).
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We use the deepest and the most comprehensive photometric data currently available for GOODS-South galaxies to measure their photometric redshifts. The photometry includes VLT/VIMOS (U-band), HST/ACS (F435W, F606W, F775W, and F850LP bands), VLT/ISAAC (J-, H-, and Ks-bands), and four Spitzer/IRAC channels (3.6, 4.5, 5.8, and 8.0 micron). The catalog is selected in the z-band (F850LP) and photometry in each band is carried out using the recently completed TFIT algorithm, which performs PSF matched photometry uniformly across different instruments and filters, despite large variations in PSFs and pixel scales. Photometric redshifts are derived using the GOODZ code, which is based on the template fitting method using priors. The code also implements "training" of the template SED set, using available spectroscopic redshifts in order to minimize systematic differences between the templates and the SEDs of the observed galaxies. Our final catalog covers an area of 153 sq. arcmin and includes photometric redshifts for a total of 32,505 objects. The scatter between our estimated photometric and spectroscopic redshifts is sigma=0.040 with 3.7% outliers to the full z-band depth of our catalog, decreasing to sigma=0.039 and 2.1% outliers at a magnitude limit m(z)<24.5. This is consistent with the best results previously published for GOODS-S galaxies, however, the present catalog is the deepest yet available and provides photometric redshifts for significantly more objects to deeper flux limits and higher redshifts than earlier works. Furthermore, we show that the photometric redshifts estimated here for galaxies selected as dropouts are consistent with those expected based on the Lyman break technique.

We discuss the construction of a photometric redshift catalogue of luminous red galaxies (LRGs) from the Sloan Digital Sky Survey (SDSS), emphasizing the principal steps necessary for constructing such a catalogue: (i) photometrically selecting the sample, (ii) measuring photometric redshifts and their error distributions, and (iii) estimating the true redshift distribution. We compare two photometric redshift algorithms for these data and find that they give comparable results. Calibrating against the SDSS and SDSS–2dF (Two Degree Field) spectroscopic surveys, we find that the photometric redshift accuracy is σ∼ 0.03 for redshifts less than 0.55 and worsens at higher redshift (∼ 0.06 for z < 0.7). These errors are caused by photometric scatter, as well as systematic errors in the templates, filter curves and photometric zero-points. We also parametrize the photometric redshift error distribution with a sum of Gaussians and use this model to deconvolve the errors from the measured photometric redshift distribution to estimate the true redshift distribution. We pay special attention to the stability of this deconvolution, regularizing the method with a prior on the smoothness of the true redshift distribution. The methods that we develop are applicable to general photometric redshift surveys.

We present deep J- and H-band images in the extended Great Observatories Origins Deep Survey-North field covering an area of 0.22 deg2. The observations were taken using WIRCam on the 3.6 m Canada–France–Hawaii Telescope. Together with the reprocessed K s-band image, the 5σ limiting AB magnitudes (in 2″ diameter apertures) are 24.7, 24.2, and 24.4 AB mag in the J, H, and K s bands, respectively. We also release a multiband photometry and photometric redshift catalog containing 93,598 sources. For non-X-ray sources, we obtained a photometric redshift accuracy σ NMAD = 0.036 with an outlier fraction η = 7.3%. For X-ray sources, which are mainly active galactic nuclei (AGNs), we cross-matched our catalog with the updated 2M-CDFN X-ray catalog from Xue et al. and found that 658 out of 683 X-ray sources have counterparts. GALEX UV data are included in the photometric redshift computation for the X-ray sources to give σ NMAD = 0.040 with η = 10.5%. Our approach yields more accurate photometric redshift estimates compared to previous works in this field. In particular, by adopting AGN–galaxy hybrid templates, our approach delivers photometric redshifts for the X-ray counterparts with fewer outliers compared to the 3D-Hubble Space Telescope catalog, which fit these sources with galaxy-only templates.

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches (such as the k-nearest neighbor (KNN) algorithm). In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine (SVM) methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding zphot. Our analysis has found two dense regions with catastrophic failure, one in the range of zphot ϵ [0.3, 1.2] and the other in the range of zphot ϵ [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey (SDSS) quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different |δz| ranges and root mean square (rms) error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN (71.96%, 83.78%, 89.73% and 0.204).

A new approach to estimating photometric redshifts - using Artificial Neural Networks (ANNs) - is investigated. Unlike the standard template-fitting photometric redshift technique, a large spectroscopically-identified training set is required but, where one is available, ANNs produce photometric redshift accuracies at least as good as and often better than the template-fitting method. The Bayesian priors on the underlying redshift distribution are automatically taken into account. Furthermore, inputs other than galaxy colours - such as morphology, angular size and surface brightness - may be easily incorporated, and their utility assessed. Different ANN architectures are tested on a semi-analytic model galaxy catalogue and the results are compared with the template-fitting method. Finally the method is tested on a sample of ~ 20000 galaxies from the Sloan Digital Sky Survey. The r.m.s. redshift error in the range z < 0.35 is ~ 0.021.

Accurate photometric redshift (photo-z) estimation requires support from multiband observational data. However, in the actual process of astronomical observations and data processing, some sources may have missing observational data in certain bands for various reasons. This could greatly affect the accuracy and reliability of photo-z estimation for these sources, and even render some estimation methods unusable. The same situation may exist for the upcoming Chinese Space Station Telescope (CSST). In this study, we employ a deep learning method called generative adversarial imputation networks (GAIN) to impute the missing photometric data in CSST, aiming to reduce the impact of data missing on photo-z estimation and improve estimation accuracy. Our results demonstrate that using the GAIN technique can effectively fill in the missing photometric data in CSST. Particularly, when the data missing rate is below 30 per cent, the imputation of photometric data exhibits high accuracy, with higher accuracy in the g, r, i, z, and y bands compared to the NUV and u bands. After filling in the missing values, the quality of photo-z estimation obtained by the widely used easy and accurate Zphot from Yale (eazy) software is notably enhanced. Evaluation metrics for assessing the quality of photo-z estimation, including the catastrophic outlier fraction (fout), the normalized median absolute deviation ($\rm {\sigma _{NMAD}}$), and the bias of photometric redshift (bias), all show some degree of improvement. Our research will help maximize the utilization of observational data and provide a new method for handling sample missing values for applications that require complete photometry data to produce results.

Despite the high accuracy of photometric redshifts (zphot) derived using Machine Learning (ML) methods, the quantification of errors through reliable and accurate Probability Density Functions (PDFs) is still an open problem. First, because it is difficult to accurately assess the contribution from different sources of errors, namely internal to the method itself and from the photometric features defining the available parameter space. Second, because the problem of defining a robust statistical method, always able to quantify and qualify the PDF estimation validity, is still an open issue. We present a comparison among PDFs obtained using three different methods on the same data set: two ML techniques, METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) and ANNz2, plus the spectral energy distribution template fitting method, BPZ. The photometric data were extracted from the KiDS (Kilo Degree Survey) ESO Data Release 3, while the spectroscopy was obtained from the GAMA (Galaxy and Mass Assembly) Data Release 2. The statistical evaluation of both individual and stacked PDFs was done through quantitative and qualitative estimators, including a dummy PDF, useful to verify whether different statistical estimators can correctly assess PDF quality. We conclude that, in order to quantify the reliability and accuracy of any zphot PDF method, a combined set of statistical estimators is required.

Accurate photometric redshifts are among the key requirements for precision weak lensing measurements. Both the large size of the Sloan Digital Sky Survey (SDSS) and the existence of large spectroscopic redshift samples that are flux-limited beyond its depth have made it the optimal data source for developing methods to properly calibrate photometric redshifts for lensing. Here, we focus on galaxy–galaxy lensing in a survey with spectroscopic lens redshifts, as in the SDSS. We develop statistics that quantify the effect of source redshift errors on the lensing calibration and on the weighting scheme, and show how they can be used in the presence of redshift failure and sampling variance. We then demonstrate their use with 2838 source galaxies with spectroscopy from DEEP2 and zCOSMOS, evaluating several public photometric redshift algorithms, in two cases including a full p(z) for each object, and find lensing calibration biases as low as &lt;1 per cent (due to fortuitous cancellation of two types of bias) or as high as 20 per cent for methods in active use (despite the small mean photoz bias of these algorithms). Our work demonstrates that lensing-specific statistics must be used to reliably calibrate the lensing signal, due to asymmetric effects of (frequently non-Gaussian) photoz errors. We also demonstrate that large-scale structure (LSS) can strongly impact the photoz calibration and its error estimation, due to a correlation between the LSS and the photoz errors, and argue that at least two independent degree-scale spectroscopic samples are needed to suppress its effects. Given the size of our spectroscopic sample, we can reduce the galaxy–galaxy lensing calibration error well below current SDSS statistical errors.

We investigate the photometric redshift accuracy achievable with the AKARI infrared data in deep multiband surveys, such as in the North Ecliptic Pole field. We demonstrate that the passage of redshifted polycyclic aromatic hydrocarbons (PAH) 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 Great Observatories Origins Deep Survey-North Field with spectroscopic redshift 0.5&lt;~zspec&lt;~1.5 and photometry from 3.6 to 24um, provided by the Spitzer, Infrared Space Observatory and AKARI satellites. The infrared spectra are fitted using synthetic galaxy spectral energy distributions which account for starburst and active nuclei emission. For ~90 per cent of the sources in our sample, the redshift is recovered with an accuracy |zphot-zspec|/(1+zspec)~&lt;10%. A similar analysis performed on a set of simulated spectra shows that the AKARI infrared data alone can provide photometric redshifts accurate to |zphot-zspec|/(1+zspec)~10% (1sigma) at z~&lt;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 1.6um stellar bump; the accuracy achievable in this case on (1+z) is ~10-15%, provided that the active galactic nuclei contribution to the infrared emission is subdominant. Our technique is no more prone to redshift aliasing than optical-ultraviolet photo-z, and it may be possible to reduce this aliasing further with the addition of submillimetre and/or radio data.

SNAP is a candidate for the Joint Dark Energy Mission (JDEM) that seeks to place constraints on the dark energy using two distinct methods. The first, Type Ia SN, is discussed in a separate white paper. The second method is weak gravitational lensing, which relies on the coherent distortions in the shapes of background galaxies by foreground mass structures. The excellent spatial resolution and photometric accuracy afforded by a 2-meter space-based observatory are crucial for achieving the high surface density of resolved galaxies, the tight control of systematic errors in the telescope's Point Spread Function (PSF), and the exquisite redshift accuracy and depth required by this project. These are achieved by the elimination of atmospheric distortion and much of the thermal and gravity loads on the telescope. The SN and WL methods for probing dark energy are highly complementary and the error contours from the two methods are largely orthogonal. The nominal SNAP weak lensing survey covers 1000 square degrees per year of operation in six optical and three near infrared filters (NIR) spanning the range 350 nm to 1.7 {micro}m. This survey will reach a depth of 26.6 AB magnitude in each of the nine filters and allow for approximately 100 resolved galaxies per square arcminute, {approx} 3 times that available from the best ground-based surveys. Photometric redshifts will be measured with statistical accuracy that enables scientific applications for even the faint, high redshift end of the sample. Ongoing work aims to meet the requirements on systematics in galaxy shape measurement, photometric redshift biases, and theoretical predictions.

In this paper, we present photometric redshifts for 2.7 million galaxies in the XMM-LSS and COSMOS fields, both with rich optical and near-infrared data from VISTA and HyperSuprimeCam. Both template fitting (using galaxy and active galactic nuclei templates within LePhare) and machine learning (using gpz) methods are run on the aperture photometry of sources selected in the Ks-band. The resulting predictions are then combined using a Hierarchical Bayesian model, to produce consensus photometric redshift point estimates and probability distribution functions that outperform each method individually. Our point estimates have a root mean square error of ∼0.08–0.09, and an outlier fraction of ∼3–4 percent when compared to spectroscopic redshifts. We also compare our results to the COSMOS2020 photometric redshifts, which contain fewer sources, but had access to a larger number of bands and greater wavelength coverage, finding that comparable photo-z quality can be achieved (for bright and intermediate luminosity sources where a direct comparison can be made). Our resulting redshifts represent the most accurate set of photometric redshifts (for a catalogue this large) for these deep multisquare degree multiwavelength fields to date.

The next generation of large scale imaging surveys (such as those conducted with the Large Synoptic Survey Telescope and Euclid) will require accurate photometric redshifts in order to optimally extract cosmological information. Gaussian Processes for photometric redshift estimation (GPz) is a promising new method that has been proven to provide efficient, accurate photometric redshift estimations with reliable variance predictions. In this paper, we investigate a number of methods for improving the photometric redshift estimations obtained using GPz (but which are also applicable to others). We use spectroscopy from the Galaxy and Mass Assembly Data Release 2 with a limiting magnitude of r<19.4 along with corresponding Sloan Digital Sky Survey visible (ugriz) photometry and the UKIRT Infrared Deep Sky Survey Large Area Survey near-IR (YJHK) photometry. We evaluate the effects of adding near-IR magnitudes and angular size as features for the training, validation and testing of GPz and find that these improve the accuracy of the results by ~15-20 per cent. In addition, we explore a post-processing method of shifting the probability distributions of the estimated redshifts based on their Quantile-Quantile plots and find that it improves the bias by ~40 per cent. Finally, we investigate the effects of using more precise photometry obtained from the Hyper Suprime-Cam Subaru Strategic Program Data Release 1 and find that it produces significant improvements in accuracy, similar to the effect of including additional features.

Context.Studies of galaxy pairs can provide valuable information to jointly understand the formation and evolution of galaxies and galaxy groups. Consequently, taking the new high-precision photo-zsurveys into account, it is important to have reliable and tested methods that allow us to properly identify these systems and estimate their total masses and other properties.Aims.In view of the forthcoming Physics of the Accelerating Universe Survey (PAUS), we propose and evaluate the performance of an identification algorithm of projected close isolated galaxy pairs. We expect that the photometrically selected systems can adequately reproduce the observational properties and the inferred lensing mass–luminosity relation of a pair of truly bound galaxies that are hosted by the same dark matter halo.Methods.We developed an identification algorithm that considers the projected distance between the galaxies, the projected velocity difference, and an isolation criterion in order to restrict the sample to isolated systems. We applied our identification algorithm using a mock galaxy catalog that mimics the features of PAUS. To evaluate the feasibility of our pair finder, we compared the identified photometric samples with a test sample that considers that both members are included in the same halo. Taking advantage of the lensing properties provided by the mock catalog, we also applied a weak-lensing analysis to determine the mass of the selected systems.Results.Photometrically selected samples tend to show high purity values, but tend to misidentify truly bounded pairs as the photometric redshift errors increase. Nevertheless, overall properties such as the luminosity and mass distributions are successfully reproduced. We also accurately reproduce the lensing mass–luminosity relation as expected for galaxy pairs located in the same halo.