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Register a new userPhotometric Redshifts with Surface Brightness Priors
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Hans F. Stabenau
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We use galaxy surface brightness as prior information to improve photometric redshift (photo-z) estimation. We apply our template-based photo-z method to imaging data from the ground-based VVDS survey and the space-based GOODS field from HST, and use spectroscopic redshifts to test our photometric redshifts for different galaxy types and redshifts. We find that the surface brightness prior eliminates a large fraction of outliers by lifting the degeneracy between the Lyman and 4000 Angstrom breaks. Bias and scatter are improved by about a factor of 2 with the prior for both the ground and space data. Ongoing and planned surveys from the ground and space will benefit, provided that care is taken in measurements of galaxy sizes and in the application of the prior. We discuss the image quality and signal-to-noise requirements that enable the surface brightness prior to be successfully applied.
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Observations in the rest frame ultraviolet from various space missions are used to define the nearby starburst regions having the highest surface brightness on scales of several hundred pc. The bright limit is found to be 6x10^-16 ergs/cm^2-s-A-arcsec^2 for rest frame wavelength of 1830 A. Surface brightness in the brightest pixel is measured for 18 galaxies in the Hubble Deep Field having z > 2.2. After correcting for cosmological dimming, we find that the high redshift starbursts have intrinsic ultraviolet surface brightness that is typically four times brighter than low redshift starbursts. It is not possible to conclude whether this difference is caused by decreased dust obscuration in the high redshift starburst regions or by intrinsically more intense star formation. Surface brightness enhancement of starburst regions may be the primary factor for explaining the observed increase with redshift of the ultraviolet luminosity arising from star formation.
Upcoming imaging surveys, such as LSST, will provide an unprecedented view of the Universe, but with limited resolution along the line-of-sight. Common ways to increase resolution in the third dimension, and reduce misclassifications, include observing a wider wavelength range and/or combining the broad-band imaging with higher spectral resolution data. The challenge with these approaches is matching the depth of these ancillary data with the original imaging survey. However, while a full 3D map is required for some science, there are many situations where only the statistical distribution of objects (dN/dz) in the line-of-sight direction is needed. In such situations, there is no need to measure the fluxes of individual objects in all of the surveys. Rather a stacking procedure can be used to perform an `ensemble photo-z. We show how a shallow, higher spectral resolution survey can be used to measure dN/dz for stacks of galaxies which coincide in a deeper, lower resolution survey. The galaxies in the deeper survey do not even need to appear individually in the shallow survey. We give a toy model example to illustrate tradeoffs and considerations for applying this method. This approach will allow deep imaging surveys to leverage the high resolution of spectroscopic and narrow/medium band surveys underway, even when the latter do not have the same reach to high redshift.
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.
Forming a three dimensional view of the Universe is a long-standing goal of astronomical observations, and one that becomes increasingly difficult at high redshift. In this paper we discuss how tomography of the intergalactic medium (IGM) at $zsimeq 2.5$ can be used to estimate the redshifts of massive galaxies in a large volume of the Universe based on spectra of galaxies in their background. Our method is based on the fact that hierarchical structure formation leads to a strong dependence of the halo density on large-scale environment. A map of the latter can thus be used to refine our knowledge of the redshifts of halos and the galaxies and AGN which they host. We show that tomographic maps of the IGM at a resolution of $2.5,h^{-1}$Mpc can determine the redshifts of more than 90 per cent of massive galaxies with redshift uncertainty $Delta z/(1+z)=0.01$. Higher resolution maps allow such redshift estimation for lower mass galaxies and halos.
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