Context. Galaxy mergers and interactions are an integral part of our basic understanding of how galaxies grow and evolve over time. However, the effect that galaxy mergers have on star-formation ...rates (SFRs) is contested, with observations of galaxy mergers showing reduced, enhanced, and highly enhanced star formation. Aims. We aim to determine the effect of galaxy mergers on the SFR of galaxies using statistically large samples of galaxies, totalling over 200 000, which is over a large redshift range from 0.0 to 4.0. Methods. We trained and used convolutional neural networks to create binary merger identifications (merger or non-merger) in the SDSS, KiDS, and CANDELS imaging surveys. We then compared the SFR, with the galaxy main sequence subtracted, of the merging and non-merging galaxies to determine what effect, if any, a galaxy merger has on SFR. Results. We find that the SFR of merging galaxies are not significantly different from the SFR of non-merging systems. The changes in the average SFR seen in the star-forming population when a galaxy is merging are small, of the order of a factor of 1.2. However, the higher the SFR is above the galaxy main sequence, the higher the fraction is for galaxy mergers. Conclusions. Galaxy mergers have little effect on the SFR of the majority of merging galaxies compared to the non-merging galaxies. The typical change in SFR is less than 0.1 dex in either direction. Larger changes in SFR can be seen but are less common. The increase in merger fraction as the distance above the galaxy main sequence increases demonstrates that galaxy mergers can induce starbursts.
We present an analysis of the mid-infrared Wide-field Infrared Survey Explorer (WISE) sources seen within the equatorial GAMA G12 field, located in the North Galactic Cap. Our motivation is to study ...and characterize the behavior of WISE source populations in anticipation of the deep multiwavelength surveys that will define the next decade, with the principal science goal of mapping the 3D large-scale structures and determining the global physical attributes of the host galaxies. In combination with cosmological redshifts, we identify galaxies from their WISE W1 (3.4 m) resolved emission, and we also perform a star-galaxy separation using apparent magnitude, colors, and statistical modeling of star counts. The resulting galaxy catalog has 590,000 sources in 60 deg2, reaching a W1 5 depth of 31 Jy. At the faint end, where redshifts are not available, we employ a luminosity function analysis to show that approximately 27% of all WISE extragalactic sources to a limit of 17.5 mag (31 Jy) are at high redshift, . The spatial distribution is investigated using two-point correlation functions and a 3D source density characterization at 5 Mpc and 20 Mpc scales. For angular distributions, we find that brighter and more massive sources are strongly clustered relative to fainter sources with lower mass; likewise, based on WISE colors, spheroidal galaxies have the strongest clustering, while late-type disk galaxies have the lowest clustering amplitudes. In three dimensions, we find a number of distinct groupings, often bridged by filaments and superstructures. Using special visualization tools, we map these structures, exploring how clustering may play a role with stellar mass and galaxy type.
Abstract
We present the-wizz, an open source and user-friendly software for estimating the redshift distributions of photometric galaxies with unknown redshifts by spatially cross-correlating them ...against a reference sample with known redshifts. The main benefit of the-wizz is in separating the angular pair finding and correlation estimation from the computation of the output clustering redshifts allowing anyone to create a clustering redshift for their sample without the intervention of an ‘expert’. It allows the end user of a given survey to select any subsample of photometric galaxies with unknown redshifts, match this sample's catalogue indices into a value-added data file and produce a clustering redshift estimation for this sample in a fraction of the time it would take to run all the angular correlations needed to produce a clustering redshift. We show results with this software using photometric data from the Kilo-Degree Survey (KiDS) and spectroscopic redshifts from the Galaxy and Mass Assembly survey and the Sloan Digital Sky Survey. The results we present for KiDS are consistent with the redshift distributions used in a recent cosmic shear analysis from the survey. We also present results using a hybrid machine learning–clustering redshift analysis that enables the estimation of clustering redshifts for individual galaxies. the-wizz can be downloaded at http://github.com/morriscb/The-wiZZ/.
ABSTRACT
We present a new sample of galaxy-scale strong gravitational lens candidates, selected from 904 deg2 of Data Release 4 of the Kilo-Degree Survey, i.e. the ‘Lenses in the Kilo-Degree Survey’ ...(LinKS) sample. We apply two convolutional neural networks (ConvNets) to ${\sim }88\,000$ colour–magnitude-selected luminous red galaxies yielding a list of 3500 strong lens candidates. This list is further downselected via human inspection. The resulting LinKS sample is composed of 1983 rank-ordered targets classified as ‘potential lens candidates’ by at least one inspector. Of these, a high-grade subsample of 89 targets is identified with potential strong lenses by all inspectors. Additionally, we present a collection of another 200 strong lens candidates discovered serendipitously from various previous ConvNet runs. A straightforward application of our procedure to future Euclid or Large Synoptic Survey Telescope data can select a sample of ∼3000 lens candidates with less than 10 per cent expected false positives and requiring minimal human intervention.
Context. The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope, specifically designed for measuring weak gravitational ...lensing by galaxies and large-scale structure. When completed it will consist of 1350 square degrees imaged in four filters (ugri). Aims. Here we present the fourth public data release which more than doubles the area of sky covered by data release 3. We also include aperture-matched ZYJHKs photometry from our partner VIKING survey on the VISTA telescope in the photometry catalogue. We illustrate the data quality and describe the catalogue content. Methods. Two dedicated pipelines are used for the production of the optical data. The ASTRO-WISE information system is used for the production of co-added images in the four survey bands, while a separate reduction of the r-band images using the THELI pipeline is used to provide a source catalogue suitable for the core weak lensing science case. All data have been re-reduced for this data release using the latest versions of the pipelines. The VIKING photometry is obtained as forced photometry on the THELI sources, using a re-reduction of the VIKING data that starts from the VISTA pawprints. Modifications to the pipelines with respect to earlier releases are described in detail. The photometry is calibrated to the Gaia DR2 G band using stellar locus regression. Results. In this data release a total of 1006 square-degree survey tiles with stacked ugri images are made available, accompanied by weight maps, masks, and single-band source lists. We also provide a multi-band catalogue based on r-band detections, including homogenized photometry and photometric redshifts, for the whole dataset. Mean limiting magnitudes (5σ in a 2″ aperture) and the tile-to-tile rms scatter are 24.23 ± 0.12, 25.12 ± 0.14, 25.02 ± 0.13, 23.68 ± 0.27 in ugri, respectively, and the mean r-band seeing is 0.″70.
We present redshift distribution estimates of galaxies selected from the fourth data release of the Kilo-Degree Survey over an area of ∼1000 deg
2
(KiDS-1000). These redshift distributions represent ...one of the crucial ingredients for weak gravitational lensing measurements with the KiDS-1000 data. The primary estimate is based on deep spectroscopic reference catalogues that are re-weighted with the help of a self-organising map (SOM) to closely resemble the KiDS-1000 sources, split into five tomographic redshift bins in the photometric redshift range 0.1 <
z
B
≤ 1.2. Sources are selected such that they only occupy that volume of nine-dimensional magnitude-space that is also covered by the reference samples (‘gold’ selection). Residual biases in the mean redshifts determined from this calibration are estimated from mock catalogues to be ≲0.01 for all five bins with uncertainties of ∼0.01. This primary SOM estimate of the KiDS-1000 redshift distributions is complemented with an independent clustering redshift approach. After validation of the clustering-
z
on the same mock catalogues and a careful assessment of systematic errors, we find no significant bias of the SOM redshift distributions with respect to the clustering-
z
measurements. The SOM redshift distributions re-calibrated by the clustering-
z
represent an alternative calibration of the redshift distributions with only slightly larger uncertainties in the mean redshifts of ∼0.01 − 0.02 to be used in KiDS-1000 cosmological weak lensing analyses. As this includes the SOM uncertainty, clustering-
z
are shown to be fully competitive on KiDS-1000 data.
We present the methodology for a joint cosmological analysis of weak gravitational lensing from the fourth data release of the ESO Kilo-Degree Survey (KiDS-1000) and galaxy clustering from the ...partially overlapping Baryon Oscillation Spectroscopic Survey (BOSS) and the 2-degree Field Lensing Survey (2dFLenS). Cross-correlations between BOSS and 2dFLenS galaxy positions and source galaxy ellipticities have been incorporated into the analysis, necessitating the development of a hybrid model of non-linear scales that blends perturbative and non-perturbative approaches, and an assessment of signal contributions by astrophysical effects. All weak lensing signals were measured consistently via Fourier-space statistics that are insensitive to the survey mask and display low levels of mode mixing. The calibration of photometric redshift distributions and multiplicative gravitational shear bias has been updated, and a more complete tally of residual calibration uncertainties was propagated into the likelihood. A dedicated suite of more than 20 000 mocks was used to assess the performance of covariance models and to quantify the impact of survey geometry and spatial variations of survey depth on signals and their errors. The sampling distributions for the likelihood and the
χ
2
goodness-of-fit statistic have been validated, with proposed changes for calculating the effective number of degrees of freedom. The prior volume was explicitly mapped, and a more conservative, wide top-hat prior on the key structure growth parameter
S
8
=
σ
8
(Ω
m
/0.3)
1/2
was introduced. The prevalent custom of reporting
S
8
weak lensing constraints via point estimates derived from its marginal posterior is highlighted to be easily misinterpreted as yielding systematically low values of
S
8
, and an alternative estimator and associated credible interval are proposed. Known systematic effects pertaining to weak lensing modelling and inference are shown to bias
S
8
by no more than 0.1 standard deviations, with the caveat that no conclusive validation data exist for models of intrinsic galaxy alignments. Compared to the previous KiDS analyses,
S
8
constraints are expected to improve by 20% for weak lensing alone and by 29% for the joint analysis.
We present a catalog of quasars with their corresponding redshifts derived from the photometric Kilo-Degree Survey (KiDS) Data Release 4. We achieved it by training machine learning (ML) models, ...using optical
u
g
ri
and near-infrared
Z
Y
J
H
K
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, based on a feature space built from magnitudes and their combinations. We show that projections of the high-dimensional feature space on two dimensions can be successfully used, instead of the standard color-color plots, to investigate the photometric estimations, compare them with spectroscopic data, and efficiently support the process of building a catalog. The model selection and fine-tuning employs two subsets of objects: those randomly selected and the faintest ones, which allowed us to properly fit the bias versus variance trade-off. We tested three ML models: random forest (RF), XGBoost (XGB), and artificial neural network (ANN). We find that XGB is the most robust and straightforward model for classification, while ANN performs the best for combined classification and redshift. The ANN inference results are tested using number counts,
Gaia
parallaxes, and other quasar catalogs that are external to the training set. Based on these tests, we derived the minimum classification probability for quasar candidates which provides the best purity versus completeness trade-off:
p
(QSO
cand
) > 0.9 for
r
< 22 and
p
(QSO
cand
) > 0.98 for 22 <
r
< 23.5. We find 158 000 quasar candidates in the safe inference subset (
r
< 22) and an additional 185 000 candidates in the reliable extrapolation regime (22 <
r
< 23.5). Test-data purity equals 97% and completeness is 94%; the latter drops by 3% in the extrapolation to data fainter by one magnitude than the training set. The photometric redshifts were derived with ANN and modeled with Gaussian uncertainties. The test-data redshift error (mean and scatter) equals 0.009 ± 0.12 in the safe subset and −0.0004 ± 0.19 in the extrapolation, averaged over a redshift range of 0.14 <
z
< 3.63 (first and 99th percentiles). Our success of the extrapolation challenges the way that models are optimized and applied at the faint data end. The resulting catalog is ready for cosmology and active galactic nucleus (AGN) studies.
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