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Register a new userUsing a Neural Network Classifier to Select Galaxies with the Most Accurate Photometric Redshifts
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The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce several billion photometric redshifts (photo-$z$s), enabling cosmological analyses to select a subset of galaxies with the most accurate photo-$z$. We perform initial redshift fits on Subaru Strategic Program galaxies with deep $grizy$ photometry using Trees for Photo-Z (TPZ) before applying a custom neural network classifier (NNC) tuned to select galaxies with $(z_mathrm{phot} - z_mathrm{spec})/(1+z_mathrm{spec}) < 0.10$. We consider four cases of training and test sets ranging from an idealized case to using data augmentation to increase the representation of dim galaxies in the training set. Selections made using the NNC yield significant further improvements in outlier fraction and photo-$z$ scatter ($sigma_z$) over those made with typical photo-$z$ uncertainties. As an example, when selecting the best third of the galaxy sample, the NNC achieves a 35% improvement in outlier rate and a 23% improvement in $sigma_z$ compared to using uncertainties from TPZ. For cosmology and galaxy evolution studies, this method can be tuned to retain a particular sample size or to achieve a desired photo-$z$ accuracy; our results show that it is possible to retain more than a third of an LSST-like galaxy sample while reducing $sigma_z$ by a factor of two compared to the full sample, with one-fifth as many photo-$z$ outliers. For surveys like LSST that are not limited by shot noise, this method enables a larger number of tomographic redshift bins and hence a significant increase in the total signal-to-noise of galaxy angular power spectra.
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The scientific value of the next generation of large continuum surveys would be greatly increased if the redshifts of the newly detected sources could be rapidly and reliably estimated. Given the observational expense of obtaining spectroscopic redshifts for the large number of new detections expected, there has been substantial recent work on using machine learning techniques to obtain photometric redshifts. Here we compare the accuracy of the predicted photometric redshifts obtained from Deep Learning(DL) with the k-Nearest Neighbour (kNN) and the Decision Tree Regression (DTR) algorithms. We find using a combination of near-infrared, visible and ultraviolet magnitudes, trained upon a sample of SDSS QSOs, that the kNN and DL algorithms produce the best self-validation result with a standard deviation of {sigma} = 0.24. Testing on various sub-samples, we find that the DL algorithm generally has lower values of {sigma}, in addition to exhibiting a better performance in other measures. Our DL method, which uses an easy to implement off-the-shelf algorithm with no filtering nor removal of outliers, performs similarly to other, more complex, algorithms, resulting in an accuracy of {Delta}z < 0.1$ up to z ~ 2.5. Applying the DL algorithm trained on our 70,000 strong sample to other independent (radio-selected) datasets, we find {sigma} < 0.36 over a wide range of radio flux densities. This indicates much potential in using this method to determine photometric redshifts of quasars detected with the Square Kilometre Array.
Context. Studies of galaxy pairs can provide valuable information to jointly understand the formation and evolution of galaxies and galaxy groups. Consequently, taking into account the new high precision photo-z surveys, 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 photometric 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 develop an identification algorithm that considers the projected distance between the galaxies, the projected velocity difference and an isolation criteria in order to restrict the sample to isolated systems. We apply our identification algorithm using a mock galaxy catalog that mimics the features of PAUS. To evaluate the feasibility of our pair finder, we compare the identified photometric samples with a test sample that considers that both members are included in the same halo. Also, taking advantage of the lensing properties provided by the mock catalog, we apply a weak lensing analysis to determine the mass of the selected systems. Results. Photometric 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.
We present measurements of the redshift-dependent clustering of a DESI-like luminous red galaxy (LRG) sample selected from the Legacy Survey imaging dataset, and use the halo occupation distribution (HOD) framework to fit the clustering signal. The photometric LRG sample in this study contains 2.7 million objects over the redshift range of $0.4 < z < 0.9$ over 5655 deg$^2$. We have developed new photometric redshift (photo-$z$) estimates using the Legacy Survey DECam and WISE photometry, with $sigma_{mathrm{NMAD}} = 0.02$ precision for LRGs. We compute the projected correlation function using new methods that maximize signal-to-noise ratio while incorporating redshift uncertainties. We present a novel algorithm for dividing irregular survey geometries into equal-area patches for jackknife resampling. For a five-parameter HOD model fit using the MultiDark halo catalog, we find that there is little evolution in HOD parameters except at the highest redshifts. The inferred large-scale structure bias is largely consistent with constant clustering amplitude over time. In an appendix, we explore limitations of Markov chain Monte Carlo fitting using stochastic likelihood estimates resulting from applying HOD methods to N-body catalogs, and present a new technique for finding best-fit parameters in this situation. Accompanying this paper we have released the Photometric Redshifts for the Legacy Surveys (PRLS) catalog of photo-$z$s obtained by applying the methods used in this work to the full Legacy Survey Data Release 8 dataset. This catalog provides accurate photometric redshifts for objects with $z < 21$ over more than 16,000 deg$^2$ of sky.
We introduce a new effective strategy to assign group and cluster membership probabilities $P_{mem}$ to galaxies using photometric redshift information. Large dynamical ranges both in halo mass and cosmic time are considered. The method takes the magnitude distribution of both cluster and field galaxies as well as the radial distribution of galaxies in clusters into account using a non-parametric formalism and relies on Bayesian inference to take photometric redshift uncertainties into account. We successfully test the method against 1,208 galaxy clusters within redshifts $z=0.05-2.58$ and masses $10^{13.29-14.80}~M_odot$ drawn from wide field simulated galaxy mock catalogs developed for the Euclid mission. Median purity $(55^{+17}_{-15})%$ and completeness $(95^{+5}_{-10})%$ are reached for galaxies brighter than 0.25$L_ast$ within $r_{200}$ of each simulated halo and for a statistical photometric redshift accuracy $sigma((z_s-z_p)/(1+z_s))=0.03$. The mean values $p=56%$ and $c=93%$ have sub-percent uncertainties. Accurate photometric redshifts ($sigma((z_s-z_p)/(1+z_s))lesssim0.05$) and robust estimates for the cluster redshift and the center coordinates are required. The method is applied to derive accurate richness estimates. A statistical comparison between the true ($N_{rm true}$) vs estimated richness ($lambda=sum P_{mem}$) yields on average to unbiased results, $Log(lambda/N_{rm true})=-0.0051pm0.15$. The scatter around the mean of the logarithmic difference between $lambda$ and the halo mass is 0.10~dex, for massive halos $gtrsim10^{14.5}~M_odot$. Our estimates could be useful to calibrate independent cluster mass estimates from weak lensing, SZ, and X-ray studies. Our method can be applied to any list of galaxy clusters or groups in both present and forthcoming surveys such as SDSS, CFHTLS, DES, LSST, and Euclid.
We apply clustering-based redshift inference to all extended sources from the Sloan Digital Sky Survey photometric catalogue, down to magnitude r = 22. We map the relationships between colours and redshift, without assumption of the sources spectral energy distributions (SED). We identify and locate star-forming, quiescent galaxies, and AGN, as well as colour changes due to spectral features, such as the 4000 AA{} break, redshifting through specific filters. Our mapping is globally in good agreement with colour-redshift tracks computed with SED templates, but reveals informative differences, such as the need for a lower fraction of M-type stars in certain templates. We compare our clustering-redshift estimates to photometric redshifts and find these two independent estimators to be in good agreement at each limiting magnitude considered. Finally, we present the global clustering-redshift distribution of all Sloan extended sources, showing objects up to z ~ 0.8. While the overall shape agrees with that inferred from photometric redshifts, the clustering redshift technique results in a smoother distribution, with no indication of structure in redshift space suggested by the photometric redshift estimates (likely artifacts imprinted by their spectroscopic training set). We also infer a higher fraction of high redshift objects. The mapping between the four observed colours and redshift can be used to estimate the redshift probability distribution function of individual galaxies. This work is an initial step towards producing a general mapping between redshift and all available observables in the photometric space, including brightness, size, concentration, and ellipticity.
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