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Register a new userMorpho-z: improving photometric redshifts with galaxy morphology
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We conduct a comprehensive study of the effects of incorporating galaxy morphology information in photometric redshift estimation. Using machine learning methods, we assess the changes in the scatter and catastrophic outlier fraction of photometric redshifts when galaxy size, ellipticity, S{e}rsic index and surface brightness are included in training on galaxy samples from the SDSS and the CFHT Stripe-82 Survey (CS82). We show that by adding galaxy morphological parameters to full $ugriz$ photometry, only mild improvements are obtained, while the gains are substantial in cases where fewer passbands are available. For instance, the combination of $grz$ photometry and morphological parameters almost fully recovers the metrics of $5$-band photometric redshifts. We demonstrate that with morphology it is possible to determine useful redshift distribution $N(z)$ of galaxy samples without any colour information. We also find that the inclusion of quasar redshifts and associated object sizes in training improves the quality of photometric redshift catalogues, compensating for the lack of a good star-galaxy separator. We further show that morphological information can mitigate biases and scatter due to bad photometry. As an application, we derive both point estimates and posterior distributions of redshifts for the official CS82 catalogue, training on morphology and SDSS Stripe-82 $ugriz$ bands when available. Our redshifts yield a 68th percentile error of $0.058(1+z)$, and a catastrophic outlier fraction of $5.2$ per cent. We further include a deep extension trained on morphology and single $i$-band CS82 photometry.
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Machine learning (ML) is a standard approach for estimating the redshifts of galaxies when only photometric information is available. ML photo-z solutions have traditionally ignored the morphological information available in galaxy images or partly included it in the form of hand-crafted features, with mixed results. We train a morphology-aware photometric redshift machine using modern deep learning tools. It uses a custom architecture that jointly trains on galaxy fluxes, colors and images. Galaxy-integrated quantities are fed to a Multi-Layer Perceptron (MLP) branch while images are fed to a convolutional (convnet) branch that can learn relevant morphological features. This split MLP-convnet architecture, which aims to disentangle strong photometric features from comparatively weak morphological ones, proves important for strong performance: a regular convnet-only architecture, while exposed to all available photometric information in images, delivers comparatively poor performance. We present a cross-validated MLP-convnet model trained on 130,000 SDSS-DR12 galaxies that outperforms a hyperoptimized Gradient Boosting solution (hyperopt+XGBoost), as well as the equivalent MLP-only architecture, on the redshift bias metric. The 4-fold cross-validated MLP-convnet model achieves a bias $delta z / (1+z) =-0.70 pm 1 times 10^{-3} $, approaching the performance of a reference ANNZ2 ensemble of 100 distinct models trained on a comparable dataset. The relative performance of the morphology-aware and morphology-blind models indicates that galaxy morphology does improve ML-based photometric redshift estimation.
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 a robust method to estimate the redshift of galaxies using Pan-STARRS1 photometric data. Our method is an adaptation of the one proposed by Beck et al. (2016) for the SDSS Data Release 12. It uses a training set of 2313724 galaxies for which the spectroscopic redshift is obtained from SDSS, and magnitudes and colours are obtained from the Pan-STARRS1 Data Release 2 survey. The photometric redshift of a galaxy is then estimated by means of a local linear regression in a 5-dimensional magnitude and colour space. Our method achieves an average bias of $overline{Delta z_{rm norm}}=-2.01 times 10^{-4}$, a standard deviation of $sigma(Delta z_{rm norm})=0.0298$, and an outlier rate of $P_o=4.32%$ when cross-validating on the training set. Even though the relation between each of the Pan-STARRS1 colours and the spectroscopic redshifts is noisier than for SDSS colours, the results obtained by our method are very close to those yielded by SDSS data. The proposed method has the additional advantage of allowing the estimation of photometric redshifts on a larger portion of the sky ($sim 3/4$ vs $sim 1/3$). The training set and the code implementing this method are publicly available at www.testaddress.com.
Although extensively investigated, the role of the environment in galaxy formation is still not well understood. In this context, the Galaxy Stellar Mass Function (GSMF) is a powerful tool to understand how environment relates to galaxy mass assembly and the quenching of star-formation. In this work, we make use of the high-precision photometric redshifts of the UltraVISTA Survey to study the GSMF in different environments up to $z sim 3$, on physical scales from 0.3 to 2 Mpc, down to masses of $M sim 10^{10} M_{odot}$. We witness the appearance of environmental signatures for both quiescent and star-forming galaxies. We find that the shape of the GSMF of quiescent galaxies is different in high- and low-density environments up to $z sim 2$ with the high-mass end ($M gtrsim 10^{11} M_{odot}$) being enhanced in high-density environments. On the contrary, for star-forming galaxies a difference between the GSMF in high- and low density environments is present for masses $M lesssim 10^{11} M_{odot}$. Star-forming galaxies in this mass range appear to be more frequent in low-density environments up to $z < 1.5$. Differences in the shape of the GSMF are not visible anymore at $z > 2$. Our results, in terms of general trends in the shape of the GSMF, are in agreement with a scenario in which galaxies are quenched when they enter hot gas-dominated massive haloes which are preferentially in high-density environments.
We present a bright galaxy sample with accurate and precise photometric redshifts (photo-zs), selected using $ugriZYJHK_mathrm{s}$ photometry from the Kilo-Degree Survey (KiDS) Data Release 4 (DR4). The highly pure and complete dataset is flux-limited at $r<20$ mag, covers $sim1000$ deg$^2$, and contains about 1 million galaxies after artifact masking. We exploit the overlap with Galaxy And Mass Assembly (GAMA) spectroscopy as calibration to determine photo-zs with the supervised machine learning neural network algorithm implemented in the ANNz2 software. The photo-zs have mean error of $|langle delta z rangle| sim 5 times 10^{-4}$ and low scatter (scaled mean absolute deviation of $sim 0.018(1+z)$), both practically independent of the $r$-band magnitude and photo-z at $0.05 < z_mathrm{phot} < 0.5$. Combined with the 9-band photometry, these allow us to estimate robust absolute magnitudes and stellar masses for the full sample. As a demonstration of the usefulness of these data we split the dataset into red and blue galaxies, use them as lenses and measure the weak gravitational lensing signal around them for five stellar mass bins. We fit a halo model to these high-precision measurements to constrain the stellar-mass--halo-mass relations for blue and red galaxies. We find that for high stellar mass ($M_star>5times 10^{11} M_odot$), the red galaxies occupy dark matter halos that are much more massive than those occupied by blue galaxies with the same stellar mass. The data presented here are publicly released via the KiDS webpage at http://kids.strw.leidenuniv.nl/DR4/brightsample.php.
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