The PAU Survey: Photometric redshifts using transfer learning from simulations
M. Eriksen, A. Alarcon, L. Cabayol, J. Carretero, R. Casas, F. J. Castander, J. De Vicente, E. Fernandez, J. Garcia-Bellido, E. Gaztanaga, H. Hildebrandt, H. Ho… 展开作者
M. Eriksen, A. Alarcon, L. Cabayol, J. Carretero, R. Casas, F. J. Castander, J. De Vicente, E. Fernandez, J. Garcia-Bellido, E. Gaztanaga, H. Hildebrandt, H. Hoekstra, B. Joachimi, R. Miquel, C. Padilla, E. Sanchez, I. Sevilla-Noarbe, P. Tallada
In this paper we introduce the \textsc{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. \textsc{Deepz} reduces the $σ_{68}$ scatter statistic by 50\% at $i_{\rm AB}=22.5$ 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 percent. 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.
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 100Å intervals covering the range 4500Å to 8500Å 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. Comp…
We present the first measurements of the projected clustering and intrinsic alignments (IA) of galaxies observed by the Physics of the Accelerating Universe Survey (PAUS). With photometry in 40 narrow optical passbands ($450\rm{nm}-850\rm{nm}$), the quality of photometric redshift estimation is $σ_{z} \sim 0.01(1 + z)$ for galaxies in the $19\,\rm{deg}^{2}$ Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) W3 field, allowing us to measure the projected 3D clustering and IA for flux-limited, faint galaxies ($i < 22.5$) out to $z\sim0.8$. To measure two-point statistics, we developed, and te…
Future cosmological galaxy surveys such as the Large Synoptic Survey Telescope (LSST) will photometrically observe very large numbers of galaxies. Without spectroscopy, the redshifts required for the analysis of these data will need to be inferred using photometric redshift techniques that are scalable to large sample sizes. The high number density of sources will also mean that around half are blended. We present a Bayesian photometric redshift method for blended sources that uses Gaussian mixture models to learn the joint flux-redshift distribution from a set of unblended training galaxies, …
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 Å break, redshifting through specific filters. Our mapping is globally in good agreement with colour-redshift tracks computed with SED templates, but reveals informative differences, suc…
We introduce Z-Sequence, a novel empirical model that utilises photometric measurements of observed galaxies within a specified search radius to estimate the photometric redshift of galaxy clusters. Z-Sequence itself is composed of a machine learning ensemble based on the k-nearest neighbours algorithm. We implement an automated feature selection strategy that iteratively determines appropriate combinations of filters and colours to minimise photometric redshift prediction error. We intend for Z-Sequence to be a standalone technique but it can be combined with cluster finders that do not intri…
Measuring environment for large numbers of distant galaxies is still an open problem, for which we need galaxy positions and redshifts. Photometric redshifts are more easily available for large numbers of galaxies, but at the price of larger uncertainties than spectroscopic ones. In this work we study how photometric redshifts affect the measurement of galaxy environment and how this may limit an analysis of the galaxy stellar mass function (GSMF) in different environments. Using mock galaxy catalogues, we measured the environment with a fixed aperture method, using each galaxy's true and phot…
The properties of galaxies in the local universe have been shown to depend upon their environment. Future large scale photometric surveys such as DES and Euclid will be vital to gain insight into the evolution of galaxy properties and the role of environment. Large samples come at the cost of redshift precision and this affects the measurement of environment. We study this by measuring environments using SDSS spectroscopic and photometric redshifts and also simulated photometric redshifts with a range of uncertainties. We consider the Nth nearest neighbour and fixed aperture methods and evalua…
We develop a novel method to explore the galaxy-halo connection using the galaxy imaging surveys by modeling the projected two-point correlation function measured from the galaxies with reasonable photometric redshift measurements. By assuming a Gaussian form of the photometric redshift errors, we are able to simultaneously constrain the halo occupation distribution (HOD) models and the effective photometric redshift uncertainties. Tests with mock galaxy catalogs demonstrate that this method can successfully recover (within $\sim 1σ$) the intrinsic large-scale galaxy bias, as well as the HOD m…
We describe a new test of photometric redshift performance given a spectroscopic redshift sample. This test complements the traditional comparison of redshift {\it differences} by testing whether the probability density functions $p(z)$ have the correct {\it width}. We test two photometric redshift codes, BPZ and EAZY, on each of two data sets and find that BPZ is consistently overconfident (the $p(z)$ are too narrow) while EAZY produces approximately the correct level of confidence. We show that this is because EAZY models the uncertainty in its spectral energy distribution templates, and tha…
Noisy distance estimates associated with photometric rather than spectroscopic redshifts lead to a mis-estimate of the luminosities, and produce a correlated mis-estimate of the sizes. We consider a sample of early-type galaxies from the SDSS DR6 for which both spectroscopic and photometric information is available, and apply the generalization of the V_max method to correct for these biases. We show that our technique recovers the true redshift, magnitude and size distributions, as well as the true size-luminosity relation. We find that using only 10% of the spectroscopic information randomly…
We present a description of the second data release for the photometric redshift (photo-$z$) of the Subaru Strategic Program for the Hyper-Suprime Cam survey. Our photo-$z$ products for the entire area in the Data Release 2 are publicly available, and both our point estimate catalog products and full PDFs can be retrieved from the data release site, \url{https://hsc-release.mtk.nao.ac.jp/}.
We construct a set of model spectra specifically designed to match the colours of the BOSS CMASS galaxies and to be used with photometric redshift template fitting techniques. As a basis we use a set of spectral energy distributions (SEDs) of single and composite stellar population models. These models cannot describe well the whole colour range populated by the CMASS galaxies at all redshifts, wherefore we modify them by multiplying the SEDs with $λ^{-β}$ for $λ>λ_i$ for different values of $λ_i$ and $β$. When fitting these SEDs to the colours of the CMASS sample, with a burst and dust compon…
In this paper we present and characterize a nearest-neighbors color-matching photometric redshift estimator that features a direct relationship between the precision and accuracy of the input magnitudes and the output photometric redshifts. This aspect makes our estimator an ideal tool for evaluating the impact of changes to LSST survey parameters that affect the measurement errors of the photometry, which is the main motivation of our work (i.e., it is not intended to provide the "best" photometric redshifts for LSST data). We show how the photometric redshifts will improve with time over the…
Accurate weak lensing mass estimates of clusters are needed in order to calibrate mass proxies for the cosmological exploitation of galaxy cluster surveys. Such measurements require accurate knowledge of the redshift distribution of the weak lensing source galaxies. In this context, we investigate the accuracy of photometric redshifts (photo-$z$s) computed by the 3D-HST team for the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey fields, which provide a relevant photometric reference data set for deep weak lensing studies. Through the comparison to spectroscopic redshifts and ph…
We present a multi-band analysis of the six Hubble Frontier Field clusters and their parallel fields, producing catalogs with measurements of source photometry and photometric redshifts. We release these catalogs to the public along with maps of intracluster light and models for the brightest galaxies in each field. This rich data set covers a wavelength range from 0.2 to 8 $μm$, utilizing data from the Hubble Space Telescope, Keck Observatories, Very Large Telescope array, and Spitzer Space Telescope. We validate our products by injecting into our fields and recovering a population of synthet…
Photometric redshifts are necessary for enabling large-scale multicolour galaxy surveys to interpret their data and constrain cosmological parameters. While the increased depth of future surveys such as the Large Synoptic Survey Telescope (LSST) will produce higher precision constraints, it will also increase the fraction of sources that are blended. In this paper, we present a Bayesian photometric redshift method for blended sources with an arbitrary number of intrinsic components. This method generalises existing template-based Bayesian photometric redshift (BPZ) methods, and produces joint …
We tested the performance of photometric redshifts for galaxies in the Hubble Ultra Deep field down to 30th magnitude. We compared photometric redshift estimates from three spectral fitting codes from the literature (EAZY, BPZ and BEAGLE) to high quality redshifts for 1227 galaxies from the MUSE integral field spectrograph. All these codes can return photometric redshifts with bias |Dzn|=|z-z_phot|/(1+z)<0.05 down to F775W=30 and spectroscopic incompleteness is unlikely to strongly modify this statement. We have, however, identified clear systematic biases in the determination of photometric r…
In this study, we propose a three-stage training approach of neural networks for both photometric redshift estimation of galaxies and detection of out-of-distribution (OOD) objects. Our approach comprises supervised and unsupervised learning, which enables using unlabeled (UL) data for OOD detection in training the networks. Employing the UL data, which is the dataset most similar to the real-world data, ensures a reliable usage of the trained model in practice. We quantitatively assess the model performance of photometric redshift estimation and OOD detection using in-distribution (ID) galaxi…
We present results of using individual galaxies' redshift probability information derived from a photometric redshift (photo-z) algorithm, SPIDERz, to identify potential catastrophic outliers in photometric redshift determinations. By using two test data sets comprised of COSMOS multi-band photometry spanning a wide redshift range (0<z<4) matched with reliable spectroscopic or other redshift determinations we explore the efficacy of a novel method to flag potential catastrophic outliers in an analysis which relies on accurate photometric redshifts. SPIDERz is a custom support vector machine cl…
The PAU Survey: The $i$-band galaxy luminosity function from the present-day to $z = 2$
Durham University（杜伦大学）；National Astronomical Research Institute of Thailand（泰国国家天文研究所）；The Hong Kong University of Science and Technology（香港科技大学）；Leiden University（莱顿大学）；Institute of Space Sciences (ICE, CSIC)（空间科学研究所（ICE, CSIC））；Institut d’Estudis Espacials de Catalunya (IEEC)（加泰罗尼亚空间研究学院（IEEC））；Ruhr University Bochum（波鸿鲁尔大学）
We present a study of photometric redshift accuracy in the 3D-HST photometric catalogs, using 3D-HST grism redshifts to quantify and dissect trends in redshift accuracy for galaxies brighter than $H_{F140W}<24$ with an unprecedented and representative high-redshift galaxy sample. We find an average scatter of $0.0197\pm0.0003(1+z)$ in the Skelton et al. (2014) photometric redshifts. Photometric redshift accuracy decreases with magnitude and redshift, but does not vary monotonically with color or stellar mass. The 1-$σ$ scatter lies between $0.01-0.03$(1+z) for galaxies of all masses and colors…
We use multi-band optical and near-infrared photometric observations of galaxies in the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) to predict photometric redshifts using artificial neural networks. The multi-band observations span over 0.39 microns to 8.0 microns for a sample of about 1000 galaxies in the GOODS-S field for which robust size measurements are available from Hubble Space Telescope Wide Field Camera 3 observations. We use Self Organizing Maps (SOMs) to map the multi dimensional photometric and galaxy size observations while taking advantage of existin…
For more that seventy years, the measurements of fluxes of galaxies at different wavelengths and derived colours have been used to estimate their corresponding cosmological distances. From the fields of galaxy and AGN evolution to precision cosmology, the number of scientific projects relying on such distance measurements, called photometric redshifts, have exploded. The benefits of photometric redshifts is that all sources detected in photometric images can have some distance estimates relatively cheaply. The major drawback is that these cheap estimates have a low precision when compared with…
We created a catalog of photometric redshift of ~3,000,000 SDSS galaxies annotated by their broad morphology. The photometric redshift was optimized by testing and comparing several pattern recognition algorithms and variable selection strategies, trained and tested on a subset of the galaxies in the catalog that had spectra. The galaxies in the catalog have i magnitude brighter than 18 and Petrosian radius greater than 5.5''. The majority of these objects are not included in previous SDSS photometric redshift catalogs such as the photoz table of SDSS DR12. Analysis of the catalog shows that t…
Broadband photometry offers a time and cost effective method to reconstruct the continuum emission of celestial objects. Thus, photometric redshift estimation has supported the scientific exploitation of extragalactic multiwavelength surveys for more than twenty years. In the era of precision cosmology, with the upcoming Euclid and LSST surveys, very tight constraints are put on the expected performance of photometric redshift estimation using broadband photometry, thus new methods have to be developed in order to reach the required performance. We present a novel automatic method of optimizin…
We present the methodology and data behind the photometric redshift database of the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We adopt a hybrid technique, empirically estimating the redshift via local regression on a spectroscopic training set, then fitting a spectrum template to obtain K-corrections and absolute magnitudes. The SDSS spectroscopic catalog was augmented with data from other, publicly available spectroscopic surveys to mitigate target selection effects. The training set is comprised of $1,976,978$ galaxies, and extends up to redshift $z\approx 0.8$, with a useful cov…
We propose a new method to estimate the photometric redshift of galaxies by using the full galaxy image in each measured band. This method draws from the latest techniques and advances in machine learning, in particular Deep Neural Networks. We pass the entire multi-band galaxy image into the machine learning architecture to obtain a redshift estimate that is competitive with the best existing standard machine learning techniques. The standard techniques estimate redshifts using post-processed features, such as magnitudes and colours, which are extracted from the galaxy images and are deemed t…
We present a new scheme, $\it{galtag}$, for refining the photometric redshift measurements of faint galaxies by probabilistically tagging them to observed galaxy groups constructed from a brighter, magnitude-limited spectroscopy survey. First, this method is tested on the DESI light-cone data constructed on the GALFORM galaxy formation model to tests its validity. We then apply it to the photometric observations of galaxies in the Kilo-Degree Imaging Survey (KiDS) over a 1 deg$^2$ region centred at 15$^\mathrm{h}$. This region contains Galaxy and Mass Assembly (GAMA) deep spectroscopic observa…
Large direct-imaging surveys usually use a template-fitting technique to estimate photometric redshifts for galaxies, which are then applied to derive important galaxy properties such as luminosities and stellar masses. These estimates can be noisy and suffer from systematic biases because of the possible mis-selection of templates and the propagation of the photometric redshift uncertainty. We introduce an algorithm, the Direct Empirical Photometric method (DEmP), which can be used to directly estimate these quantities using training sets, bypassing photometric redshift determination. DEmP al…
We investigate the effect of strong emission line galaxies on the performance of empirical photometric redshift estimation methods. In order to artificially control the contribution of photometric error and emission lines to total flux, we develop a PCA-based stochastic mock catalogue generation technique that allows for generating infinite signal-to-noise ratio model spectra with realistic emission lines on top of theoretical stellar continua. Instead of running the computationally expensive stellar population synthesis and nebular emission codes, our algorithm generates realistic spectra wit…
Joint probabilistic inference of galaxy redshifts and rest-frame spectra from photometric fluxes with latent diffusion
Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology（高能物理研究所，巴塞罗那科学与技术学院）；Port d’Informació Científica (PIC)（科学信息港）
We present a study of photometric redshift performance for galaxies and active galactic nuclei detected in deep radio continuum surveys. Using two multi-wavelength datasets, over the NOAO Deep Wide Field Survey Boötes and COSMOS fields, we assess photometric redshift (photo-z) performance for a sample of $\sim 4,500$ radio continuum sources with spectroscopic redshifts relative to those of $\sim 63,000$ non radio-detected sources in the same fields. We investigate the performance of three photometric redshift template sets as a function of redshift, radio luminosity and infrared/X-ray properti…
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 individua…
Search for quasar pairs with Gaia astrometric data. II. Photometric redshift prediction with machine learning for the MGQPC catalogue
Institute for Frontier in Astronomy and Astrophysics, Beijing Normal University（北京师范大学前沿天文与天体物理研究院）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Physics and Astronomy, Beijing Normal University（北京师范大学物理与天文学院）
Performance of Photometric Template Fitting for Ultra-High Redshift Galaxies
Cosmic Dawn Center (DAWN)（宇宙黎明中心）；Niels Bohr Institute, University of Copenhagen（哥本哈根大学尼尔斯·玻尔研究所）；Department of Physics and Astronomy, University of Missouri（密苏里大学物理与天文学系）；DARK Cosmology Center, University of Copenhagen（哥本哈根大学暗宇宙学中心）；California Institute of Technology（加州理工学院）
This research focuses on identifying high redshift galaxies from LABOCA(LArge APEX BOlometer CAmera) and SPIRE(The Spectral and Photometric Imaging Receiver) maps towards proto-cluster candidates initially selected from the SPT (South pole telescope) survey. Based on the Multi-Gaussian fitting algorithm, we cross-match all significant LABOCA sources at SPIRE wavelengths based on their coordinates and signal to noise ratio to derive their photometry at 250, 350, 500 and 870 $μm$. We use this information to calculate a photometric redshift for SPT sources towards cluster fields. The code was dev…
We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415…
We present catalogs of calibrated photometry and spectroscopic redshifts in the Extended Groth Strip, intended for studies of photometric redshifts (photo-z's). The data includes ugriz photometry from CFHTLS and Y-band photometry from the Subaru Suprime camera, as well as spectroscopic redshifts from the DEEP2, DEEP3 and 3D-HST surveys. These catalogs incorporate corrections to produce effectively matched-aperture photometry across all bands, based upon object size information available in the catalog and Moffat profile point spread function fits. We test this catalog with a simple machine lea…
Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between the spectroscopic and photometric redshifts is close to zero, the distribution of these differences remains wide and distinctly non-Gaussian. As per our previous empirical estimate of photometric redshifts, we find that the predictions can be significantly improved by adding colours from other wavebands, namely the near-infrared and ultraviolet. Self-testin…
