Redshift Precision Research Articles

Multimodality for improved CNN photometric redshifts

Photometric redshift estimation plays a crucial role in modern cosmological surveys for studying the universe’s large-scale structures and the evolution of galaxies. Deep learning has emerged as a powerful method to produce accurate photometric redshift estimates from multiband images of galaxies. Here, we introduce a multimodal approach consisting of the parallel processing of several subsets of prior image bands, the outputs of which are then merged for further processing through a convolutional neural network (CNN). We evaluate the performance of our method using three surveys: the Sloan Digital Sky Survey (SDSS), the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS), and the Hyper Suprime-Cam (HSC). By improving the model’s ability to capture information embedded in the correlation between different bands, our technique surpasses state-of-the-art photometric redshift precision. We find that the positive gain does not depend on the specific architecture of the CNN and that it increases with the number of photometric filters available.

Constraining cosmology with the Gaia-unWISE Quasar Catalog and CMB lensing: structure growth

We study the angular clustering of Quaia, a Gaia- and unWISE-based catalog of over a million quasars with an exceptionally well-defined selection function. With it, we derive cosmology constraints from the amplitude and growth of structure across cosmic time. We divide the sample into two redshift bins, centered at z = 1.0 and z = 2.1, and measure both overdensity auto-correlations and cross-correlations with maps of the Cosmic Microwave Background convergence measured by Planck. From these data, and including a prior from measurements of the baryon acoustic oscillations scale, we place constraints on the amplitude of the matter power spectrum σ 8 = 0.766 ± 0.034, and on the matter density parameter Ω m = 0.343+0.017 -0.019. These measurements are in reasonable agreement with Planck at the ∼ 1.4σ level, and are found to be robust with respect to observational and theoretical uncertainties. We find that our slightly lower value of σ 8 is driven by the higher-redshift sample, which favours a low amplitude of matter fluctuations. We present plausible arguments showing that this could be driven by contamination of the CMB lensing map by high-redshift extragalactic foregrounds, which should also affect other cross-correlations with tracers of large-scale structure beyond z ∼ 1.5. Our constraints are competitive with those from state-of-the-art 3×2-point analyses, but arise from a range of scales and redshifts that is highly complementary to those covered by cosmic shear data and most galaxy clustering samples. This, coupled with the unprecedented combination of volume and redshift precision achieved by Quaia, allows us to break the usual degeneracy between Ω m and σ 8.

The miniJPAS survey quasar selection

Aims. Quasar catalogues from photometric data are used in a variety of applications including those targeting spectroscopic follow-up, measurements of supermassive black hole masses, Baryon Acoustic Oscillations, or non-Gaussianities. Here, we present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE. miniJPAS is a small proof-of-concept survey covering 1 deg2 with the full J-PAS filter system, consisting of 54 narrow filters and 2 broader filters covering the entire optical wavelength range. Methods. This work is based on the machine-learning classification of photometric data of quasar candidates using SQUEzE. It has the advantage that its classification procedure can be explained to some extent, making it less of a ‘black box’ when compared with other classifiers. Another key advantage is that the use of user-defined metrics means the user has more control over the classification. While SQUEzE was designed for spectroscopic data, we have adapted it for multi-band photometric data; that is we treat multiple narrow-band filters as very low-resolution spectra. We trained our models using specialised mocks. We estimated our redshift precision using the normalised median absolute deviation, σNMAD, applied to our test sample. Results. Our test sample returns an f1 score (effectively the purity and completeness) of 0.49 for high-z quasars (with z ≥ 2.1) down a to magnitude of r = 24.3 and 0.24 for low-z quasars (with z < 2.1), also down to a magnitude of r = 24.3. For high-z quasars, this goes up to 0.9 for magnitudes of r < 21.0. We present two catalogues of quasar candidates including redshift estimates: 301 from point-like sources and 1049 when also including extended sources. We discuss the impact of including extended sources in our predictions (they are not included in the mocks), as well as the impact of changing the noise model of the mocks. We also give an explanation of SQUEzE reasoning. Our estimates for the redshift precision using the test sample indicate a σNMAD = 0.92% for the entire sample, reduced to 0.81% for r < 22.5 and 0.74% for r < 21.3. Spectroscopic follow-up of the candidates is required in order to confirm the validity of our findings.

Performance of the Quasar Spectral Templates for the Dark Energy Spectroscopic Instrument

Millions of quasar spectra will be collected by the Dark Energy Spectroscopic Instrument (DESI), leading to a fourfold increase in the number of known quasars. High-accuracy quasar classification is essential to tighten constraints on cosmological parameters measured at the highest redshifts DESI observes (z > 2.0). We present spectral templates for identification and redshift estimation of quasars in the DESI Year 1 data release. The quasar templates are comprised of two quasar eigenspectra sets, trained on spectra from the Sloan Digital Sky Survey. The sets are specialized to reconstruct quasar spectral variation observed over separate yet overlapping redshift ranges and, together, are capable of identifying DESI quasars from 0.05 < z < 7.0. The new quasar templates show significant improvement over the previous DESI quasar templates regarding catastrophic failure rates, redshift precision and accuracy, quasar completeness, and the contamination fraction in the final quasar sample.

The cross-correlation of galaxies in absorption with the Lyman α forest

ABSTRACT We present the first clustering measurement of Strong Blended Lyman α (SBLA) absorption systems by measuring their cross-correlation with the Lyman α forest. SBLAs are a new population of absorbers detected within the Lyman α forest. We find a bias of 2.329 ± 0.057, consistent with that of Damped Lyman α absorbers (DLAs). For DLAs, we recover a bias of 2.331 ± 0.057 larger than previously reported. We also find a redshift space distortion parameter β = 0.417 ± 0.010, also consistent with the recovered value for DLAs (β = 0.416 ± 0.010). This is consistent with SBLA and DLA systems tracing different portions of the circumgalactic medium of a broadly common population of galaxies. Given these common clustering properties, we combined them to perform a cross-correlation of galaxies in absorption with the Lyα forest. We find that the BAO scale uncertainty of this new measurement is 1.75 × that of Lyα autocorrelation and 1.6 × that of the quasar cross-correlation with the Lyα forest. We note that the current preferred metal contamination model for fitting the correlation functions with respect to the Lyα forest is not realistic enough for SBLA systems, likely due to their status as high redshift precision sites of high metal enrichment. Mock spectra including SBLA systems and their associated metal absorption are required to understand this sample fully. We conclude that SBLAs have the potential to complement the standard Lyα cosmological analyses in future surveys.

The DESI Survey Validation: Results from Visual Inspection of the Quasar Survey Spectra

A key component of the Dark Energy Spectroscopic Instrument (DESI) survey validation (SV) is a detailed visual inspection (VI) of the optical spectroscopic data to quantify key survey metrics. In this paper we present results from VI of the quasar survey using deep coadded SV spectra. We show that the majority (≈70%) of the main-survey targets are spectroscopically confirmed as quasars, with ≈16% galaxies, ≈6% stars, and ≈8% low-quality spectra lacking reliable features. A nonnegligible fraction of the quasars are misidentified by the standard spectroscopic pipeline, but we show that the majority can be recovered using post-pipeline “afterburner” quasar-identification approaches. We combine these “afterburners” with our standard pipeline to create a modified pipeline to increase the overall quasar yield. At the depth of the main DESI survey, both pipelines achieve a good-redshift purity (reliable redshifts measured within 3000 km s−1) of ≈99%; however, the modified pipeline recovers ≈94% of the visually inspected quasars, as compared to ≈86% from the standard pipeline. We demonstrate that both pipelines achieve a median redshift precision and accuracy of ≈100 km s−1 and ≈70 km s−1, respectively. We constructed composite spectra to investigate why some quasars are missed by the standard pipeline and find that they are more host-galaxy dominated (i.e., distant analogs of “Seyfert galaxies”) and/or more dust reddened than the standard-pipeline quasars. We also show example spectra to demonstrate the overall diversity of the DESI quasar sample and provide strong-lensing candidates where two targets contribute to a single spectrum.

Redshift requirements for cosmic shear with intrinsic alignment

Intrinsic alignment (IA) modelling and photometric redshift estimation are two of the main sources of systematic uncertainty in weak lensing surveys. We investigate the impact of redshift errors and their interplay with different IA models. Generally, errors on the mean δz and on the width σz of the redshift bins can both lead to biases in cosmological constraints. We find that such biases can, however, only be partially resolved by marginalizing over δz and σz . For Stage-III surveys, δz and σz cannot be well constrained due to limited statistics. The resulting biases are thus sensitive to prior volume effects. For Stage-IV surveys, we observe that marginalizing over the redshift parameters has an impact and reduces the bias. We derive requirements on the uncertainty of σz and δz for both Stage-III and Stage-IV surveys. We assume that the redshift systematic errors on S 8 should be less than half of the statistical errors, and the median bias should be smaller than 0.25σ. We find that the uncertainty on δz has to be ≲ 0.025 for the NLA IA model with a Stage-III survey. We find no requirement threshold for σz since the requirements are met even for our maximum prior width of 0.3. For the TATT IA model, the uncertainty on δz has to be ≲ 0.02 and the uncertainty on σz has to be ≲ 0.2. Current redshift precision of Stage-III surveys is therefore high enough to meet these requirements. For Stage-IV surveys, systematic effects will be more important due to the higher statistical precision. In this case, the uncertainty on δz has to be ≲ 0.005 and the uncertainty on σz should be ≲ 0.1, with no significant dependence on the IA model. This required high precision will be a challenge for the redshift calibration of these future surveys. Finally, we investigate whether the interplay between redshift systematics and IA modelling can explain the S 8-tension between cosmic shear results and CMB measurements. We find that this is unlikely to explain the current S 8-tension. The code that was used to conduct this analysis is publicly available.[refrigerator: https://cosmo-gitlab.phys.ethz.ch/cosmo_public/refrigerator.]

Three-dimensional weak gravitational lensing of the 21-cm radiation background

ABSTRACT We study weak gravitational lensing by the cosmic large-scale structure of the 21-cm radiation background in the 3D weak-lensing formalism. The interplay between source distance measured at finite resolution, visibility, and lensing terms is analysed in detail and the resulting total covariance Cℓ(k, k′) is derived. The effect of lensing correlates different multipoles through convolution, breaking the statistical homogeneity of the 21-cm radiation background. This homogeneity breaking can be exploited to reconstruct the lensing field $\hat{\phi }_{\rm \ell m}(\kappa)$ and noise-lensing reconstruction $N_{\ell }^{\hat{\phi }}$ by means of quadratic estimators. The effects related to the actual measurement process (redshift precision and visibility terms) change drastically the values of the off-diagonal terms of the total covariance Cℓ(k, k′). It is expected that the detection of lensing effects on a 21-cm radiation background will require sensitive studies and high-resolution observations by future low-frequency radio arrays such as the SKA survey.

COSMOS2020: A Panchromatic View of the Universe to z ∼ 10 from Two Complementary Catalogs

Abstract The Cosmic Evolution Survey (COSMOS) has become a cornerstone of extragalactic astronomy. Since the last public catalog in 2015, a wealth of new imaging and spectroscopic data have been collected in the COSMOS field. This paper describes the collection, processing, and analysis of these new imaging data to produce a new reference photometric redshift catalog. Source detection and multiwavelength photometry are performed for 1.7 million sources across the 2 deg2 of the COSMOS field, ∼966,000 of which are measured with all available broadband data using both traditional aperture photometric methods and a new profile-fitting photometric extraction tool, The Farmer, which we have developed. A detailed comparison of the two resulting photometric catalogs is presented. Photometric redshifts are computed for all sources in each catalog utilizing two independent photometric redshift codes. Finally, a comparison is made between the performance of the photometric methodologies and of the redshift codes to demonstrate an exceptional degree of self-consistency in the resulting photometric redshifts. The i &lt; 21 sources have subpercent photometric redshift accuracy and even the faintest sources at 25 &lt; i &lt; 27 reach a precision of 5%. Finally, these results are discussed in the context of previous, current, and future surveys in the COSMOS field. Compared to COSMOS2015, it reaches the same photometric redshift precision at almost one magnitude deeper. Both photometric catalogs and their photometric redshift solutions and physical parameters will be made available through the usual astronomical archive systems (ESO Phase 3, IPAC-IRSA, and CDS).

Introducing the FLAMINGOS-2 Split-K Medium-band Filters: The Impact on Photometric Selection of High-z Galaxies in the FENIKS-pilot survey

Abstract Deep near-infrared photometric surveys are efficient in identifying high-redshift galaxies, however, they can be prone to systematic errors in photometric redshift. This is particularly salient when there is limited sampling of key spectral features of a galaxy’s spectral energy distribution (SED), such as for quiescent galaxies where the expected age-sensitive Balmer/4000 Å break enters the K-band at z &gt; 4. With single-filter sampling of this spectral feature, degeneracies between SED models and redshift emerge. A potential solution to this comes from splitting the K band into multiple filters. We use simulations to show an optimal solution is to add two medium-band filters, K blue (λ cen = 2.06 μm, Δλ = 0.25 μm) and K red (λ cen = 2.31 μm, Δλ = 0.27 μm), that are complementary to the existing K s filter. We test the impact of the K-band filters with simulated catalogs comprised of galaxies with varying ages and signal-to-noise. The results suggest that the K-band filters do improve photometric redshift constraints on z &gt; 4 quiescent galaxies, increasing precision and reducing outliers by up to 90%. We find that the impact from the K-band filters depends on the signal-to-noise, the redshift, and the SED of the galaxy. The filters we designed were built and used to conduct a pilot of the FLAMINGOS-2 Extragalactic Near-Infrared K-band Split survey. While no new z &gt; 4 quiescent galaxies are identified in the limited area pilot, the K blue and K red filters indicate strong Balmer/4000 Å breaks in existing candidates. Additionally, we identify galaxies with strong nebular emission lines, for which the K-band filters increase photometric redshift precision and in some cases indicate extreme star formation.

The miniJPAS survey: A preview of the Universe in 56 colors

The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will scan thousands of square degrees of the northern sky with a unique set of 56 filters using the dedicated 2.55 m Javalambre Survey Telescope (JST) at the Javalambre Astrophysical Observatory. Prior to the installation of the main camera (4.2 deg2field-of-view with 1.2 Gpixels), the JST was equipped with the JPAS-Pathfinder, a one CCD camera with a 0.3 deg2field-of-view and plate scale of 0.23 arcsec pixel−1. To demonstrate the scientific potential of J-PAS, the JPAS-Pathfinder camera was used to perform miniJPAS, a ∼1 deg2survey of the AEGIS field (along the Extended Groth Strip). The field was observed with the 56 J-PAS filters, which include 54 narrow band (FWHM ∼ 145 Å) and two broader filters extending to the UV and the near-infrared, complemented by theu, g, r, iSDSS broad band filters. In this miniJPAS survey overview paper, we present the miniJPAS data set (images and catalogs), as we highlight key aspects and applications of these unique spectro-photometric data and describe how to access the public data products. The data parameters reach depths of magAB ≃ 22−23.5 in the 54 narrow band filters and up to 24 in the broader filters (5σin a 3″ aperture). The miniJPAS primary catalog contains more than 64 000 sources detected in therband and with matched photometry in all other bands. This catalog is 99% complete atr = 23.6 (r = 22.7) mag for point-like (extended) sources. We show that our photometric redshifts have an accuracy better than 1% for all sources up tor = 22.5, and a precision of ≤0.3% for a subset consisting of about half of the sample. On this basis, we outline several scientific applications of our data, including the study of spatially-resolved stellar populations of nearby galaxies, the analysis of the large scale structure up toz ∼ 0.9, and the detection of large numbers of clusters and groups. Sub-percent redshift precision can also be reached for quasars, allowing for the study of the large-scale structure to be pushed toz &gt; 2. The miniJPAS survey demonstrates the capability of the J-PAS filter system to accurately characterize a broad variety of sources and paves the way for the upcoming arrival of J-PAS, which will multiply this data by three orders of magnitude.

The PAU Survey: an improved photo-zsample in the COSMOS field

ABSTRACTWe present – and make publicly available – accurate and precise photometric redshifts in the ACS footprint from the COSMOS field for objects with iAB ≤ 23. The redshifts are computed using a combination of narrow-band photometry from PAUS, a survey with 40 narrow bands spaced at $100\,\mathring{\rm A}$ intervals covering the range from 4500 to $8500\,\mathring{\rm A}$, 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 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 O ii 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 O ii line and Hα, Hβ and $\mathrm{O\,{\small III}}$ are similarly measured and used to generate the emission-line templates. Comparing to public spectroscopic surveys via the quantity Δz ≡ (zphoto − zspec)/(1 + zspec), we find the photometric redshifts to be more precise than previous estimates, with σ68(Δz) ≈ (0.003, 0.009) for galaxies at magnitude iAB ∼ 18 and iAB ∼ 23, respectively, which is three times and 1.66 times tighter than COSMOS2015. Additionally, we find the redshifts to be very accurate on average, yielding a median of the Δz distribution compatible with |median(Δz)| ≤ 0.001 at all redshifts and magnitudes considered. Both the added PAUS data and new methodology contribute significantly to the improved results. The catalogue produced with the technique presented here is expected to provide a robust redshift calibration for current and future lensing surveys, and allows one to probe galaxy formation physics in an unexplored luminosity-redshift regime, thanks to its combination of depth, completeness, and excellent redshift precision and accuracy.

The PAU Survey: Photometric redshifts using transfer learning from simulations

ABSTRACT In this paper, we introduce the 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. deepz reduces the σ68 scatter statistic by 50 per cent at iAB = 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 per cent. 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.

Optimizing galaxy samples for clustering measurements in photometric surveys

ABSTRACT When analysing galaxy clustering in multiband imaging surveys, there is a trade-off between selecting the largest galaxy samples (to minimize the shot noise) and selecting samples with the best photometric redshift (photo-z) precision, which generally includes only a small subset of galaxies. In this paper, we systematically explore this trade-off. Our analysis is targeted towards the third-year data of the Dark Energy Survey (DES), but our methods hold generally for other data sets. Using a simple Gaussian model for the redshift uncertainties, we carry out a Fisher matrix forecast for cosmological constraints from angular clustering in the redshift range z = 0.2–0.95. We quantify the cosmological constraints using a figure of merit (FoM) that measures the combined constraints on Ωm and σ8 in the context of Λ cold dark matter (ΛCDM) cosmology. We find that the trade-off between sample size and photo-z precision is sensitive to (1) whether cross-correlations between redshift bins are included or not, and (2) the ratio of the redshift bin width δz to the photo-z precision σz. When cross-correlations are included and the redshift bin width is allowed to vary, the highest FoM is achieved when δz ∼ σz. We find that for the typical case of 5−10 redshift bins, optimal results are reached when we use larger, less precise photo-z samples, provided that we include cross-correlations. For samples with higher σz, the overlap between redshift bins is larger, leading to higher cross-correlation amplitudes. This leads to the self-calibration of the photo-z parameters and therefore tighter cosmological constraints. These results can be used to help guide galaxy sample selection for clustering analysis in ongoing and future photometric surveys.

The PAU Survey: early demonstration of photometric redshift performance in the COSMOS field

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 100\AA\ intervals covering the range 4500\AA\ to 8500\AA\ 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.

Crowded Field Galaxy Photometry: Precision Colors in the CLASH Clusters

Abstract We present a new method for photometering objects in galaxy clusters. We introduce a mode-filtering technique for removing spatially variable backgrounds, improving both detection and photometric accuracy (roughly halving the scatter in the red sequence compared to previous catalogs of the same clusters). This method is based on robustly determining the distribution of background pixel values and should provide comparable improvement in photometric analysis of any crowded fields. We produce new multiwavelength catalogs for the 25 CLASH cluster fields in all 16 bandpasses from the UV through the near-IR, as well as rest-frame magnitudes. A comparison with spectroscopic values from the literature finds a decrease in the redshift deviation from previously released CLASH photometry. This improvement in redshift precision, in combination with a detection scheme designed to maximize purity, yields a substantial upgrade in cluster member identification over the previous CLASH galaxy catalog. We construct luminosity functions for each cluster, reliably reaching depths of at least 4.5 mag below M* in every case, and deeper still in several clusters. We measure M* , α, and their redshift evolution, assuming the cluster populations are coeval, and find little to no evolution of , and M* values consistent with passive evolution. We present a catalog of galaxy photometry, photometric and spectroscopic redshifts, and rest-frame photometry for the full fields of view of all 25 CLASH clusters. Not only will our new photometric catalogs enable new studies of the properties of CLASH clusters, but mode-filtering techniques, such as those presented here, should greatly enhance the data quality of future photometric surveys of crowded fields.

Calibrating photometric redshifts with intensity mapping observations

Imaging surveys of galaxies will have a high number density and angular resolution yet a poor redshift precision. Intensity maps of neutral hydrogen (HI) will have accurate redshift resolution yet will not resolve individual sources. Using this complementarity, we show how the clustering redshifts approach, proposed for spectroscopic surveys can also be used in combination with intensity mapping observations to calibrate the redshift distribution of galaxies in an imaging survey and, as a result, reduce uncertainties in photometric redshift measurements. We show how the intensity mapping surveys to be carried out with the MeerKAT, HIRAX and SKA instruments can improve photometric redshift uncertainties to well below the requirements of DES and LSST. The effectiveness of this method as a function of instrumental parameters, foreground subtraction and other potential systematic errors is discussed in detail.

ELDAR, a new method to identify AGN in multi-filter surveys: the ALHAMBRA test case

We present ELDAR, a new method that exploits the potential of medium- and narrow-band filter surveys to securely identify active galactic nuclei (AGN) and determine their redshifts. Our methodology improves on traditional approaches by looking for AGN emission lines expected to be identified against the continuum, thanks to the width of the filters. To assess its performance, we apply ELDAR to the data of the ALHAMBRA survey, which covered an effective area of $2.38\,{\rm deg}^2$ with 20 contiguous medium-band optical filters down to F814W$\simeq 24.5$. Using two different configurations of ELDAR in which we require the detection of at least 2 and 3 emission lines, respectively, we extract two catalogues of type-I AGN. The first is composed of 585 sources ($79\,\%$ of them spectroscopically-unknown) down to F814W$=22.5$ at $z_{\rm phot}>1$, which corresponds to a surface density of $209\,{\rm deg}^{-2}$. In the second, the 494 selected sources ($83\,\%$ of them spectroscopically-unknown) reach F814W$=23$ at $z_{\rm phot}>1.5$, for a corresponding number density of $176\,{\rm deg}^{-2}$. Then, using samples of spectroscopically-known AGN in the ALHAMBRA fields, for the two catalogues we estimate a completeness of $73\,\%$ and $67\,\%$, and a redshift precision of $1.01\,\%$ and $0.86\,\%$ (with outliers fractions of $8.1\,\%$ and $5.8\,\%$). At $z>2$, where our selection performs best, we reach $85\,\%$ and $77\,\%$ completeness and we find no contamination from galaxies.

The need for accurate redshifts in supernova cosmology

Recent papers have shown that a small systematic redshift shift (Δ z∼ 10−5) in measurements of type Ia supernovae can cause a significant bias (∼1%) in the recovery of cosmological parameters. Such a redshift shift could be caused, for example, by a gravitational redshift due to the density of our local environment. The sensitivity of supernova data to redshift shifts means supernovae make excellent probes of inhomogeneities. We therefore invert the analysis, and try to diagnose the nature of our local gravitational environment by fitting for Δ z as an extra free parameter alongside the usual cosmological parameters. Using the Joint Light-curve SN Ia dataset we find the best fit includes a systematic redshift shift of Δ z = (2.6+2.7−2.8) × 10−4. This is a larger shift than would be expected due to gravitational redshifts in a standard Λ-Cold Dark Matter universe (though still consistent with zero), and would correspond to a monopole Doppler shift of about 100 km s−1 moving away from the Milky-Way. However, since most supernova measurements are made to a redshift precision of no better than 10−3, it is possible that a systematic error smaller than the statistical error remains in the data and is responsible for the shift; or that it is an insignificant statistical fluctuation. We find that when Δ z is included as a free parameter while fitting to the JLA SN Ia data, the constraints on the matter density shifts to Ωm = 0.313+0.042−0.040, bringing it into better agreement with the CMB cosmological parameter constraints from Planck. A positive Δ z∼ 2.6×10−4 would also cause us to overestimate the supernova measurement of Hubble's constant by Δ H0 ∼ 1 kms−1Mpc−1. However this overestimation should diminish as one increases the low-redshift cutoff, and this is not seen in the most recent data.

The 2-degree Field Lensing Survey: photometric redshifts from a large new training sample tor &lt; 19.5

We present a new training set for estimating empirical photometric redshifts of galaxies, which was created as part of the 2dFLenS project. This training set is located in a 700 sq deg area of the KiDS South field and is randomly selected and nearly complete at r<19.5. We investigate the photometric redshift performance obtained with ugriz photometry from VST-ATLAS and W1/W2 from WISE, based on several empirical and template methods. The best redshift errors are obtained with kernel-density estimation, as are the lowest biases, which are consistent with zero within statistical noise. The 68th percentiles of the redshift scatter for magnitude-limited samples at r<(15.5, 17.5, 19.5) are (0.014, 0.017, 0.028). In this magnitude range, there are no known ambiguities in the colour-redshift map, consistent with a small rate of redshift outliers. In the fainter regime, the KDE method produces p(z) estimates per galaxy that represent unbiased and accurate redshift frequency expectations. The p(z) sum over any subsample is consistent with the true redshift frequency plus Poisson noise. Further improvements in redshift precision at r<20 would mostly be expected from filter sets with narrower passbands to increase the sensitivity of colours to small changes in redshift.