In cosmological analyses, precise redshift determination remains pivotal for understanding cosmic evolution. However, with only a fraction of galaxies having spectroscopic redshifts (spec-$z$s), the challenge lies in estimating redshifts for a larger number. To address this, photometry-based redshift (photo-$z$) estimation, employing machine learning algorithms, is a viable solution. Identifying the limitations of previous methods, this study focuses on implementing deep learning (DL) techniques within the Kilo-Degree Survey (KiDS) Bright Galaxy Sample for more accurate photo-$z$ estimations. Comparing our new DL-based model against prior `shallow' neural networks, we showcase improvements in redshift accuracy. Our model gives mean photo-$z$ bias $\langle Δz\rangle= 10^{-3}$ and scatter $\mathrm{SMAD}(Δz)=0.016$, where $Δz = (z_\mathrm{phot}-z_\mathrm{spec})/(1+z_\mathrm{spec})$. This research highlights the promising role of DL in revolutionizing photo-$z$ estimation.
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 $|\l…
The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope. It aims to image 1500 square degrees in four filters (ugri). The core science driver is mapping the large-scale matter distribution in the Universe, using weak lensing shear and photometric redshift measurements. Further science cases include galaxy evolution, Milky Way structure, detection of high-redshift clusters, and finding rare sources such as strong lenses and quasars. Here we present the third public data release (DR3) and several associated data products,…
We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the BPZ code, at least up to zphot<0.9 and r<23.5. At the bright end of r<20, where very complete spectroscopic data overlapping with KiDS are available, the performance of the ML photo-zs clearly surpasses that of BPZ, currently the primary photo-z method for KiDS. Using the Galaxy And Mas…
We present a catalog of quasars and corresponding redshifts in the Kilo-Degree Survey (KiDS) Data Release 4. We trained machine learning (ML) models, using optical ugri and near-infrared ZYJHK_s bands, on objects known from Sloan Digital Sky Survey (SDSS) spectroscopy. We define inference subsets from the 45 million objects of the KiDS photometric data limited to 9-band detections. We show that projections of the high-dimensional feature space can be successfully used to investigate the estimations. The model creation employs two test subsets: randomly selected and the faintest objects, which …
The Kilo-Degree Survey (KiDS) is a multi-band imaging survey designed for cosmological studies from weak lensing and photometric redshifts. It uses the ESO VLT Survey Telescope with its wide-field camera OmegaCAM. KiDS images are taken in four filters similar to the SDSS ugri bands. The best-seeing time is reserved for deep r-band observations that reach a median 5-sigma limiting AB magnitude of 24.9 with a median seeing that is better than 0.7arcsec. Initial KiDS observations have concentrated on the GAMA regions near the celestial equator, where extensive, highly complete redshift catalogues…
The Kilo-Degree Survey (KiDS) is an optical wide-field imaging survey carried out with the VLT Survey Telescope and the OmegaCAM camera. KiDS will image 1500 square degrees in four filters (ugri), and together with its near-infrared counterpart VIKING will produce deep photometry in nine bands. Designed for weak lensing shape and photometric redshift measurements, the core science driver of the survey is mapping the large-scale matter distribution in the Universe back to a redshift of ~0.5. Secondary science cases are manifold, covering topics such as galaxy evolution, Milky Way structure, and…
Photometric observations of 235 extragalactic objects that are potential targets for the Space Interferometry Mission (SIM) are presented. Mean B, V, R, I magnitudes at the 5% level are obtained at 1 - 4 epochs between 2005 and 2007 using the 1-m telescopes at Cerro Tololo Inter-American Observatory and Naval Observatory Flagstaff Station. Of the 134 sources which have V magnitudes in the Veron & Veron-Cetty catalog a difference of over 1.0 mag is found for the observed-catalog magnitudes for about 36% of the common sources, and 10 sources show over 3 mag difference. Our first set of observati…
By the time that the first phase of the Square Kilometre Array is deployed it will be able to perform state of the art Large Scale Structure (LSS) as well as Weak Gravitational Lensing (WGL) measurements of the distribution of matter in the Universe. In this chapter we concentrate on the synergies that result from cross-correlating these different SKA data products as well as external correlation with the weak lensing measurements available from CMB missions. We show that the Dark Energy figures of merit obtained individually from WGL/LSS measurements and their independent combination is signi…
We present a method that accurately propagates residual uncertainties in photometric redshift distributions into the cosmological inference from weak lensing measurements. The redshift distributions of tomographic redshift bins are parameterised using a flexible modified Gaussian mixture model. We fit this model to pre-calibrated redshift distributions and implement an analytic marginalisation over the potentially several hundred redshift nuisance parameters in the weak lensing likelihood, which is demonstrated to accurately recover the cosmological posterior. By iteratively fitting cosmologic…
Exploiting the full statistical power of future cosmic shear surveys will necessitate improvements to the accuracy with which the gravitational lensing signal is measured. We present a framework for calibrating shear with image simulations that demonstrates the importance of including realistic correlations between galaxy morphology, size and more importantly, photometric redshifts. This realism is essential so that selection and shape measurement biases can be calibrated accurately for a tomographic cosmic shear analysis. We emulate Kilo-Degree Survey (KiDS) observations of the COSMOS field u…
We forecast future constraints on local-type primordial non-Gaussianity parameter $f_{\rm NL}$ with a photometric galaxy survey by Euclid, a continuum galaxy survey by Square Kilometre Array (SKA), and their combination. We derive a general expression for the covariance matrix of the power spectrum estimates of multiple tracers to show how the so-called multitracer technique improves constraints on $f_{\rm NL}$. In particular we clarify the role of the overlap fraction of multiple tracers and the division method of the tracers. Our Fisher matrix analysis indicates that stringent constraints of…
National Centre for Nuclear Research（国家核研究中心）；Leiden University（莱顿大学）；Center for Theoretical Physics, Polish Academy of Sciences（波兰科学院理论物理中心）；Jagiellonian University（雅盖隆大学）；Ludwig-Maximilians-Universität（路德维希-马克西米利安大学）；INAF – Osservatorio Astronomico di Capodimonte（意大利国家天体物理研究所–卡波迪蒙特天文台）；Sun Yat-sen University（中山大学）
We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on SDSS DR14 spectroscopic data. We first cleaned the input KiDS data from entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stella…
We use the overlap between multiband photometry of the Kilo-Degree Survey (KiDS) and spectroscopic data based on the Sloan Digital Sky Survey (SDSS) and Galaxy And Mass Assembly (GAMA) to infer the colour-magnitude relation of red-sequence galaxies. We then use this inferred relation to select luminous red galaxies (LRGs) in the redshift range of $0.1<z<0.7$ over the entire KiDS Data Release 3 footprint. We construct two samples of galaxies with different constant comoving densities and different luminosity thresholds. The selected red galaxies have photometric redshifts with typical photo-z e…
We use the photometric redshift method of Chakrabarti & McKee (2008) to infer photometric redshifts of submillimeter galaxies with far-IR (FIR) $\it{Herschel}$ data obtained as part of the PACS Evolutionary Probe (PEP) program. For the sample with spectroscopic redshifts, we demonstrate the validity of this method over a large range of redshifts ($ 4 \ga z \ga 0.3$) and luminosities, finding an average accuracy in $(1+z_{\rm phot})/(1+z_{\rm spec})$ of 10%. Thus, this method is more accurate than other FIR photometric redshift methods. This method is different from typical FIR photometric meth…
We perform photometric analyses for a bright early-type galaxy (ETG) sample with 2949 galaxies ($M_{\rm r}<-22.5$ mag) in the redshift range of 0.05 to 0.15, drawn from the SDSS DR7 with morphological classification from Galaxy Zoo 1. We measure the Petrosian and isophotal magnitudes, as well as the corresponding half-light radius for each galaxy. We find that for brightest galaxies ($M_{\rm r}<-23$ mag), our Petrosian magnitudes, and isophotal magnitudes to 25 ${\rm mag/arcsec^2}$ and 1\% of the sky brightness are on average 0.16 mag, 0.20 mag, and 0.26 mag brighter than the SDSS Petrosian va…
The European Southern Observatory's (ESO) Visible and Infrared Survey Telescope for Astronomy (VISTA) is a 4-m class survey telescope for wide-field near-infrared imaging. VISTA is currently running a suite of six public surveys, which will shortly deliver their first Europe wide public data releases to ESO. The VISTA Kilo-degree Infrared Galaxy Survey (VIKING) forms a natural intermediate between current wide shallow, and deeper more concentrated surveys, by targeting two patches totalling 1500 sq.deg in the northern and southern hemispheres with measured 5-sigma limiting depths of Z ~ 22.4, …
We have obtained deep near-infrared $J$- (1.25 $μ$m), $H$- (1.65$ μ$m) and $K_s$-band (2.15 $μ$m) imaging for a sample of six dwarf galaxies ($M_B\ga-17$ mag) in the Local Volume (LV, $D\la10$ Mpc). The sample consists mainly of early-type dwarf galaxies found in various environments in the LV. Two galaxies (LEDA 166099 and UGCA 200) in the sample are detected in the near-infrared for the first time. The deep near-infrared images allow for a detailed study of the photometric and structural properties of each galaxy. The surface brightness profiles of the galaxies are detected down to the ~$24 …
