In this data release from the ongoing LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) we present 120-168 MHz images covering 27% of the northern sky. Our coverage is split into two regions centred at approximately 12h45m +44 • 30 and 1h00m +28 • 00 and spanning 4178 and 1457 square degrees respectively. The images were derived from 3,451 hrs (7.6 PB) of LOFAR High Band Antenna data which were corrected for the direction-independent instrumental properties as well as direction-dependent ionospheric distortions during extensive, but fully automated, data processing. A catalogue of 4,396,228 radio sources is derived from our total intensity (Stokes I) maps, where the majority of these have never been detected at radio wavelengths before. At 6 resolution, our full bandwidth Stokes I continuum maps with a central frequency of 144 MHz have: a median rms sensitivity of 83 µJy/beam; a flux density scale accuracy of approximately 10%; an astrometric accuracy of 0.2 ; and we estimate the point-source completeness to be 90% at a peak brightness of 0.8 mJy/beam. By creating three 16 MHz bandwidth images across the band we are able to measure the in-band spectral index of many sources, albeit with an error on the derived spectral index of > ±0.2 which is a consequence of our flux-density scale accuracy and small fractional bandwidth. Our circular polarisation (Stokes V) 20 resolution 120-168 MHz continuum images have a median rms sensitivity of 95 µJy/beam, and we estimate a Stokes I to Stokes V leakage of 0.056%. Our linear polarisation (Stokes Q and Stokes U) image cubes consist of 480 × 97.6 kHz wide planes and have a median rms sensitivity per plane of 10.8 mJy/beam at 4 and 2.2 mJy/beam at 20 ; we estimate the Stokes I to Stokes Q/U leakage to be approximately 0.2%. Here we characterise and publicly release our Stokes I, Q, U and V images in addition to the calibrated uv-data to facilitate the thorough scientific exploitation of this unique dataset.
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 Sloan Digital Sky Survey (SDSS) DR14 spectroscopic data. We first cleaned the input KiDS data of 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 stellarity index. We used the t-SNE algorithm to map the multidimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r < 22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS. This gives 3.4 million objects in the final inference sample, from which the random forest identified 190,000 quasar candidates. Accuracy of 97% (percentage of correctly classified objects), purity of 91% (percentage of true quasars within the objects classified as such), and completeness of 87% (detection ratio of all true quasars), as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint. The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from the Wide-field Infrared Survey Explorer (WISE). An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of p QSO > 0.8 is optimal for purity, whereas p QSO > 0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey. We publicly release the resulting catalog at http://kids.strw.leidenuniv.nl/DR3/quasarcatalog.php, and the code at https://github.com/snakoneczny/kids-quasars.
We present a bright galaxy sample with accurate and precise photometric redshifts (photo-zs), selected using ugriZYJHKs photometry from the Kilo-Degree Survey (KiDS) Data Release 4. The highly pure and complete dataset is flux-limited at r < 20 mag, covers ∼1000 deg2, and contains about 1 million galaxies after artifact masking. We exploit the overlap with Galaxy And Mass Assembly spectroscopy as calibration to determine photo-zs with the supervised machine learning neural network algorithm implemented in the ANNz2 software. The photo-zs have a mean error of |⟨δz⟩|∼5 × 10−4 and low scatter (scaled mean absolute deviation of ∼0.018(1 + z)); they are both practically independent of the r-band magnitude and photo-z at 0.05 < zphot < 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, used them as lenses, and measured 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⋆ > 5 × 1011 M⊙), the red galaxies occupy dark matter halos that are much more massive than those occupied by blue galaxies with the same stellar mass.
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 ugri and near-infrared ZYJHKs 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(QSOcand) > 0.9 for r < 22 and p(QSOcand) > 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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