arXiv subjects

H. Hildebrandt

Publications and source records attributed to H. Hildebrandt.

At least 37 records · Page 2Linked to original sources

arXiv2024

The PAU Survey: Photometric Calibration of Narrow Band Images

The Physics of the Accelerating Universe (PAU) camera is an optical narrow band and broad band imaging instrument mounted at the prime focus of the William Herschel Telescope. We describe the image calibration procedure of the PAU Survey data. We rely on an external photometric catalogue to calibrate our narrow band data using stars that have been observed by both datasets. We fit stellar templates to the stellar broad band photometry of the Sloan Digital Sky Survey and synthesise narrow band photometry that we compare to the PAUS narrow band data to determine their calibration. Consequently, the PAUS data are in the AB system as inherited from its reference calibrator. We do several tests to check the performance of the calibration. We find it self-consistent when comparing repeated observations of the same objects, with a good overall accuracy to the AB system which we estimate to be at the 2\% precision level and no significant trends as a function of narrow band filter or wavelength. Repeated observations allow us to build a spatial map of the illumination pattern of the system. We also check the wavelength dependence of the calibration comparing to stellar spectra. We find that using only blue stars reduces the effects of variations in the stellar template fitting to broad-band colours, improving the overall precision of the calibration to around 1\% and its wavelength uniformity. The photometric redshift performance obtained with the PAUS data attests to the validity of our calibration to reach the PAUS science goals.

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arXiv2024

The PAU Survey: a new constraint on galaxy formation models using the observed colour redshift relation

We use the GALFORM semi-analytical galaxy formation model implemented in the Planck Millennium N-body simulation to build a mock galaxy catalogue on an observer's past lightcone. The mass resolution of this N-body simulation is almost an order of magnitude better than in previous simulations used for this purpose, allowing us to probe fainter galaxies and hence build a more complete mock catalogue at low redshifts. The high time cadence of the simulation outputs allows us to make improved calculations of galaxy properties and positions in the mock. We test the predictions of the mock against the Physics of the Accelerating Universe Survey, a narrow band imaging survey with highly accurate and precise photometric redshifts, which probes the galaxy population over a lookback time of 8 billion years. We compare the model against the observed number counts, redshift distribution and evolution of the observed colours and find good agreement; these statistics avoid the need for model-dependent processing of the observations. The model produces red and blue populations that have similar median colours to the observations. However, the bimodality of galaxy colours in the model is stronger than in the observations. This bimodality is reduced on including a simple model for errors in the GALFORM photometry. We examine how the model predictions for the observed galaxy colours change when perturbing key model parameters. This exercise shows that the median colours and relative abundance of red and blue galaxies provide constraints on the strength of the feedback driven by supernovae used in the model.

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arXiv2023

The PAU Survey: Photometric redshift estimation in deep wide fields

We present photometric redshifts (photo-$z$) for the deep wide fields of the Physics of the Accelerating Universe Survey (PAUS), covering an area of $\sim$50 deg$^{2}$, for $\sim$1.8 million objects up to $i_{\textrm{AB}}<23$. The PAUS deep wide fields overlap with the W1 and W3 fields from CFHTLenS and the G09 field from KiDS/GAMA. Photo-$z$ are estimated using the 40 narrow bands (NB) of PAUS and the broad bands (BB) of CFHTLenS and KiDS. We compute the redshifts with the SED template-fitting code BCNZ, with a modification in the calibration technique of the zero-point between the observed and the modelled fluxes, that removes any dependence on spectroscopic redshift samples. We enhance the redshift accuracy by introducing an additional photo-$z$ estimate ($z_{\textrm{b}}$), obtained through the combination of the BCNZ and the BB-only photo-$z$. Comparing with spectroscopic redshifts estimates ($z_{\textrm{s}}$), we obtain a $σ_{68} \simeq 0.019$ for all galaxies with $i_{\textrm{AB}}<23$ and a typical bias $|z_{\textrm{b}}-z_{\textrm{s}}|$ smaller than 0.01. For $z_{\textrm{b}} \sim (0.10-0.75)$ we find $σ_{68} \simeq (0.003-0.02)$, this is a factor of $10-2$ higher accuracy than the corresponding BB-only results. We obtain similar performance when we split the samples into red (passive) and blue (active) galaxies. We validate the redshift probability $p(z)$ obtained by BCNZ and compare its performance with that of $z_{\textrm{b}}$. These photo-$z$ catalogues will facilitate important science cases, such as the study of galaxy clustering and intrinsic alignment at high redshifts ($z \lesssim 1$) and faint magnitudes.

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arXiv2023

DES Y3 + KiDS-1000: Consistent cosmology combining cosmic shear surveys

We present a joint cosmic shear analysis of the Dark Energy Survey (DES Y3) and the Kilo-Degree Survey (KiDS-1000) in a collaborative effort between the two survey teams. We find consistent cosmological parameter constraints between DES Y3 and KiDS-1000 which, when combined in a joint-survey analysis, constrain the parameter $S_8 = σ_8 \sqrt{Ω_{\rm m}/0.3}$ with a mean value of $0.790^{+0.018}_{-0.014}$. The mean marginal is lower than the maximum a posteriori estimate, $S_8=0.801$, owing to skewness in the marginal distribution and projection effects in the multi-dimensional parameter space. Our results are consistent with $S_8$ constraints from observations of the cosmic microwave background by Planck, with agreement at the $1.7σ$ level. We use a Hybrid analysis pipeline, defined from a mock survey study quantifying the impact of the different analysis choices originally adopted by each survey team. We review intrinsic alignment models, baryon feedback mitigation strategies, priors, samplers and models of the non-linear matter power spectrum.

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arXiv2023

The PAU Survey: Narrow-band image photometry

PAUCam is an innovative optical narrow-band imager mounted at the William Herschel Telescope built for the Physics of the Accelerating Universe Survey (PAUS). Its set of 40 filters results in images that are complex to calibrate, with specific instrumental signatures that cannot be processed with traditional data reduction techniques. In this paper we present two pipelines developed by the PAUS data management team with the objective of producing science-ready catalogues from the uncalibrated raw images. The Nightly pipeline takes care of all image processing, with bespoke algorithms for photometric calibration and scatter-light correction. The Multi-Epoch and Multi-Band Analysis (MEMBA) pipeline performs forced photometry over a reference catalogue to optimize the photometric redshift performance. We verify against spectroscopic observations that the current approach delivers an inter-band photometric calibration of 0.8% across the 40 narrow-band set. The large volume of data produced every night and the rapid survey strategy feedback constraints require operating both pipelines in the Port d'Informació Cientifica data centre with intense parallelization. While alternative algorithms for further improvements in photo-z performance are under investigation, the image calibration and photometry presented in this work already enable state-of-the-art photometric redshifts down to iAB=23.0.

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arXiv2023

The PAU Survey: Close galaxy pairs identification and analysis

Galaxy pairs constitute the initial building blocks of galaxy evolution, which is driven through merger events and interactions. Thus, the analysis of these systems can be valuable in understanding galaxy evolution and studying structure formation. In this work, we present a new publicly available catalogue of close galaxy pairs identified using photometric redshifts provided by the Physics of the Accelerating Universe Survey (PAUS). To efficiently detect them we take advantage of the high-precision photo$-z$ ($σ_{68} < 0.02$) and apply an identification algorithm previously tested using simulated data. This algorithm considers the projected distance between the galaxies ($r_p < 50$ kpc), the projected velocity difference ($ΔV < 3500$ km/s) and an isolation criterion to obtain the pair sample. We applied this technique to the total sample of galaxies provided by PAUS and to a subset with high-quality redshift estimates. Finally, the most relevant result we achieved was determining the mean mass for several subsets of galaxy pairs selected according to their total luminosity, colour and redshift, using galaxy-galaxy lensing estimates. For pairs selected from the total sample of PAUS with a mean $r-$band luminosity $10^{10.6} h^{-2} L_\odot$, we obtain a mean mass of $M_{200} = 10^{12.2} h^{-1} M_\odot$, compatible with the mass-luminosity ratio derived for elliptical galaxies. We also study the mass-to-light ratio $M/L$ as a function of the luminosity $L$ and find a lower $M/L$ (or steeper slope with $L$) for pairs than the one extrapolated from the measurements in groups and galaxy clusters.

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arXiv2023

Euclid: Calibrating photometric redshifts with spectroscopic cross-correlations

Cosmological constraints from key probes of the Euclid imaging survey rely critically on the accurate determination of the true redshift distributions, $n(z)$, of tomographic redshift bins. We determine whether the mean redshift, $ $, of ten Euclid tomographic redshift bins can be calibrated to the Euclid target uncertainties of $σ( )<0.002\,(1+z)$ via cross-correlation, with spectroscopic samples akin to those from the Baryon Oscillation Spectroscopic Survey (BOSS), Dark Energy Spectroscopic Instrument (DESI), and Euclid's NISP spectroscopic survey. We construct mock Euclid and spectroscopic galaxy samples from the Flagship simulation and measure small-scale clustering redshifts up to redshift $z<1.8$ with an algorithm that performs well on current galaxy survey data. The clustering measurements are then fitted to two $n(z)$ models: one is the true $n(z)$ with a free mean; the other a Gaussian Process modified to be restricted to non-negative values. We show that $ $ is measured in each tomographic redshift bin to an accuracy of order 0.01 or better. By measuring the clustering redshifts on subsets of the full Flagship area, we construct scaling relations that allow us to extrapolate the method performance to larger sky areas than are currently available in the mock. For the full expected Euclid, BOSS, and DESI overlap region of approximately 6000 deg$^{2}$, the uncertainties attainable by clustering redshifts exceeds the Euclid requirement by at least a factor of three for both $n(z)$ models considered, although systematic biases limit the accuracy. Clustering redshifts are an extremely effective method for redshift calibration for Euclid if the sources of systematic biases can be determined and removed, or calibrated-out with sufficiently realistic simulations. We outline possible future work, in particular an extension to higher redshifts with quasar reference samples.

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arXiv2023

The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how the synergies between narrow-band photometric data and large imaging surveys can be exploited to improve broadband photometric redshifts. We used a multi-task learning (MTL) network to improve broadband photo-z estimates by simultaneously predicting the broadband photo-z and the narrow-band photometry from the broadband photometry. The narrow-band photometry is only required in the training field, which also enables better photo-z predictions for the galaxies without narrow-band photometry in the wide field. This technique was tested with data from the Physics of the Accelerating Universe Survey (PAUS) in the COSMOS field. We find that the method predicts photo-zs that are 13% more precise down to magnitude i_{AB} < 23; the outlier rate is also 40% lower when compared to the baseline network. Furthermore, MTL reduces the photo-z bias for high-redshift galaxies, improving the redshift distributions for tomographic bins with z>1. Applying this technique to deeper samples is crucial for future surveys such as \Euclid or LSST. For simulated data, training on a sample with i_{AB} <23, the method reduces the photo-z scatter by 16% for all galaxies with i_{AB}<25. We also studied the effects of extending the training sample with photometric galaxies using PAUS high-precision photo-zs, which reduces the photo-z scatter by 20% in the COSMOS field.

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arXiv2022

Potential scientific synergies in weak lensing studies between the CSST and Euclid space probes

Aims. With the next generation of large surveys coming to the stage of observational cosmology soon, it is important to explore their potential synergies and to maximise their scientific outcomes. In this study, we aim to investigate the complementarity of the two upcoming space missions Euclid and the China Space Station Telescope (CSST), focusing on weak lensing (WL) cosmology. In particular, we analyse the photometric redshifts (photo-zs) and the galaxy blending effects. For Euclid, WL measurements suffer from chromatic PSF effects. For this, CSST can provide valuable information for Euclid to obtain more accurate PSF, and to calibrate the color and color-gradient biases for WL measurements. Methods. We create image simulations for different surveys, and quantify the photo-z performance. For blending analyses, we employ high-resolution HST/CANDELS data to mock Euclid, CSST, and an LSST-like survey. We analyse the blending fraction for different cases, and the blending effects on galaxy photometry. Furthermore, we demonstrate that CSST can provide a large enough number of high SNR multi-band galaxy images to calibrate the color-gradient biases for Euclid. Results. The sky coverage of Euclid lies entirely within the CSST footprint. The combination of Euclid with CSST data can be done more uniformly than with the various ground-based data. Our studies show that by combining Euclid and CSST, we can reach a photo-z precision of $σ_{\rm NMAD} \approx 0.04$, and an outlier fraction of $η\approx 2.4\%$. Because of the similarly high resolutions, the data combination of Euclid and CSST can be relatively straightforward for photometry. To include ground-based data, however, sophisticated deblending utilizing priors from high-resolution space data is demanded. The color-gradient biases for Euclid can be well calibrated to the level of 0.1% using galaxies from CSST deep survey.

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arXiv2022

Consistent lensing and clustering in a low-$S_8$ Universe with BOSS, DES Year 3, HSC Year 1 and KiDS-1000

We evaluate the consistency between lensing and clustering probes of large-scale structure based on measurements of projected galaxy clustering from BOSS combined with overlapping galaxy-galaxy lensing from three surveys: DES Y3, HSC Y1, and KiDS-1000. An intra-lensing-survey study finds good agreement between these lensing data. We model the observations using the Dark Emulator and fit the data at two fixed cosmologies: Planck, with $S_8=0.83$, and a Lensing cosmology with $S_8=0.76$. For a joint analysis limited to scales with $R>5.25h^{-1}$Mpc, we find that both cosmologies provide an acceptable fit to the data. Full utilisation of the small-scale clustering and lensing measurements is hindered by uncertainty in the impact of baryon feedback and assembly bias, which we account for with a reasoned theoretical error budget. We incorporate a systematic scaling parameter for each redshift bin, $A$, that decouples the lensing and clustering to capture any inconsistency. When a wide range of scales ($0.15<R<60h^{-1}$Mpc) are incorporated, we find different results for the consistency of clustering and lensing between the two cosmologies. Limiting the analysis to the bins for which the impact of the selection of the lens sample is expected to be minimal, for the low-$S_8$ Lensing cosmology, the measurements are consistent with $A$=1; $A=0.91\pm0.04$ using DES+KiDS and $A=0.97\pm0.06$ using HSC. For the Planck cosmology case, we find a discrepancy: $A=0.79\pm0.03$ using DES+KiDS and $A=0.84\pm0.05$ using HSC. We demonstrate that a kSZ-based estimate for baryonic effects alleviates some of the discrepancy in the Planck cosmology. This analysis demonstrates the statistical power of these small-scale measurements, but also indicates that caution is still warranted given current uncertainties in modelling baryonic effects, assembly bias, and selection effects in the foreground sample.

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arXiv2022

KiDS & Euclid: Cosmological implications of a pseudo angular power spectrum analysis of KiDS-1000 cosmic shear tomography

We present a tomographic weak lensing analysis of the Kilo Degree Survey Data Release 4 (KiDS-1000), using a new pseudo angular power spectrum estimator (pseudo-$C_{\ell}$) under development for the ESA Euclid mission. Over 21 million galaxies with shape information are divided into five tomographic redshift bins, ranging from 0.1 to 1.2 in photometric redshift. We measured pseudo-$C_{\ell}$ using eight bands in the multipole range $76<\ell<1500$ for auto- and cross-power spectra between the tomographic bins. A series of tests were carried out to check for systematic contamination from a variety of observational sources including stellar number density, variations in survey depth, and point spread function properties. While some marginal correlations with these systematic tracers were observed, there is no evidence of bias in the cosmological inference. B-mode power spectra are consistent with zero signal, with no significant residual contamination from E/B-mode leakage. We performed a Bayesian analysis of the pseudo-$C_{\ell}$ estimates by forward modelling the effects of the mask. Assuming a spatially flat $Λ$CDM cosmology, we constrained the structure growth parameter $S_8 = σ_8(Ω_{\rm m}/0.3)^{1/2} = 0.754_{-0.029}^{+0.027}$. When combining cosmic shear from KiDS-1000 with baryon acoustic oscillation and redshift space distortion data from recent Sloan Digital Sky Survey (SDSS) measurements of luminous red galaxies, as well as the Lyman-$α$ forest and its cross-correlation with quasars, we tightened these constraints to $S_8 = 0.771^{+0.006}_{-0.032}$. These results are in very good agreement with previous KiDS-1000 and SDSS analyses and confirm a $\sim 3σ$ tension with early-Universe constraints from cosmic microwave background experiments.

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arXiv2022

KiDS-1000: cosmic shear with enhanced redshift calibration

We present a cosmic shear analysis with an improved redshift calibration for the fourth data release of the Kilo-Degree Survey (KiDS-1000) using self-organising maps (SOMs). Compared to the previous analysis of the KiDS-1000 data, we expand the redshift calibration sample to more than twice its size, now consisting of data of 17 spectroscopic redshift campaigns, and significantly extending the fraction of KiDS galaxies we are able to calibrate with our SOM redshift methodology. We then enhance the calibration sample with precision photometric redshifts from COSMOS2015 and the Physics of the Accelerated Universe Survey (PAUS), allowing us to fill gaps in the spectroscopic coverage of the KiDS data. Finally we perform a Complete Orthogonal Sets of E/B-Integrals (COSEBIs) cosmic shear analysis of the newly calibrated KiDS sample. We find $S_8 = 0.748_{-0.025}^{+0.021}$, which is in good agreement with previous KiDS studies and increases the tension with measurements of the cosmic microwave background to 3.4σ. We repeat the redshift calibration with different subsets of the full calibration sample and obtain, in all cases, agreement within at most 0.5σ in $S_8$ compared to our fiducial analysis. Including additional photometric redshifts allows us to calibrate an additional 6 % of the source galaxy sample. Even though further systematic testing with simulated data is necessary to quantify the impact of redshift outliers, precision photometric redshifts can be beneficial at high redshifts and to mitigate selection effects commonly found in spectroscopically selected calibration samples.

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arXiv2021

High-quality strong lens candidates in the final Kilo Degree survey footprint

We present 97 new high-quality strong lensing candidates found in the final $\sim 350\,\rm deg^2$, that completed the full $\sim 1350\,\rm deg^2$ area of the Kilo-Degree Survey (KiDS). Together with our previous findings, the final list of high-quality candidates from KiDS sums up to 268 systems. The new sample is assembled using a new Convolutional Neural Network (CNN) classifier applied to $r$-band (best seeing) and $g,~r,~i$ color-composited images separately. This optimizes the complementarity of the morphology and color information on the identification of strong lensing candidates. We apply the new classifiers to a sample of luminous red galaxies (LRGs) and a sample of bright galaxies (BGs) and select candidates that received a high probability to be a lens from the CNN ($P_{\rm CNN}$). In particular, setting $P_{\rm CNN}>0.8$ for the LRGs, the $1$-band CNN predicts 1213 candidates, while the $3$-band classifier yields 1299 candidates, with only $\sim$30\% overlap. For the BGs, in order to minimize the false positives, we adopt a more conservative threshold, $P_{\rm CNN} >0.9$, for both CNN classifiers. This results in 3740 newly selected objects. The candidates from the two samples are visually inspected by 7 co-authors to finally select 97 "high-quality" lens candidates which received mean scores larger than 6 (on a scale from 0 to 10). We finally discuss the effect of the seeing on the accuracy of CNN classification and possible avenues to increase the efficiency of multi-band classifiers, in preparation of next-generation surveys from ground and space.

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arXiv2021

Lensing Without Borders. I. A Blind Comparison of the Amplitude of Galaxy-Galaxy Lensing Between Independent Imaging Surveys

Lensing Without Borders is a cross-survey collaboration created to assess the consistency of galaxy-galaxy lensing signals ($ΔΣ$) across different data-sets and to carry out end-to-end tests of systematic errors. We perform a blind comparison of the amplitude of $ΔΣ$ using lens samples from BOSS and six independent lensing surveys. We find good agreement between empirically estimated and reported systematic errors which agree to better than 2.3$σ$ in four lens bins and three radial ranges. For lenses with $z_{\rm L}>0.43$ and considering statistical errors, we detect a 3-4$σ$ correlation between lensing amplitude and survey depth. This correlation could arise from the increasing impact at higher redshift of unrecognised galaxy blends on shear calibration and imperfections in photometric redshift calibration. At $z_{\rm L}>0.54$ amplitudes may additionally correlate with foreground stellar density. The amplitude of these trends is within survey-defined systematic error budgets which are designed to include known shear and redshift calibration uncertainty. Using a fully empirical and conservative method, we do not find evidence for large unknown systematics. Systematic errors greater than 15% (25%) ruled out in three lens bins at 68% (95%) confidence at $z<0.54$. Differences with respect to predictions based on clustering are observed to be at the 20-30% level. Our results therefore suggest that lensing systematics alone are unlikely to fully explain the "lensing is low" effect at $z<0.54$. This analysis demonstrates the power of cross-survey comparisons and provides a promising path for identifying and reducing systematics in future lensing analyses.

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arXiv2021

Bright galaxy sample in the Kilo-Degree Survey Data Release 4: selection, photometric redshifts, and physical properties

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 $|\langle δz \rangle| \sim 5 \times 10^{-4}$ and low scatter (scaled mean absolute deviation of $\sim 0.018(1+z)$), both practically independent of the $r$-band magnitude and photo-z at $0.05 < z_\mathrm{phot} < 0.5$. 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, use them as lenses and measure 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 ($M_\star>5\times 10^{11} M_\odot$), the red galaxies occupy dark matter halos that are much more massive than those occupied by blue galaxies with the same stellar mass. The data presented here are publicly released via the KiDS webpage at http://kids.strw.leidenuniv.nl/DR4/brightsample.php.

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arXiv2021

Self-calibration and robust propagation of photometric redshift distribution uncertainties in weak gravitational lensing

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 cosmological and nuisance parameters arising from the redshift distribution model, we perform a self-calibration of the redshift distributions via the tomographic cosmic shear measurements. Our method is applied to the third data release of the Kilo-Degree Survey combined with the VISTA Kilo-Degree Infrared Galaxy Survey (KV450). We find constraints on cosmological parameters that are in very good agreement with the fiducial KV450 cosmic shear analysis and investigate the effects of the more flexible model on the self-calibrated redshift distributions. We observe posterior shifts of the medians of the five tomographic redshift distributions of up to $Δz \approx 0.02$, which are however degenerate with an observed decrease of the amplitude of intrinsic galaxy alignments by about $10\%$.

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arXiv2021

Testing KiDS cross-correlation redshifts with simulations

Measuring cosmic shear in wide-field imaging surveys requires accurate knowledge of the redshift distribution of all sources. The clustering-redshift technique exploits the angular cross-correlation of a target galaxy sample with unknown redshifts and a reference sample with known redshifts, and is an attractive alternative to colour-based methods of redshift calibration. We test the performance of such clustering redshift measurements using mock catalogues that resemble the Kilo-Degree Survey (KiDS). These mocks are created from the MICE simulation and closely mimic the properties of the KiDS source sample and the overlapping spectroscopic reference samples. We quantify the performance of the clustering redshifts by comparing the cross-correlation results with the true redshift distributions in each of the five KiDS photometric redshift bins. Such a comparison to an informative model is necessary due to the incompleteness of the reference samples at high redshifts. Clustering mean redshifts are unbiased at $|Δz|<0.006$ under these conditions. The redshift evolution of the galaxy bias can be reliably mitigated at this level of precision using auto-correlation measurements and self-consistency relations, and will not become a dominant source of systematic error until the arrival of Stage-IV cosmic shear surveys. Using redshift distributions from a direct colour-based estimate instead of the true redshift distributions as a model for comparison with the clustering redshifts increases the biases in the mean to up to $|Δz|\sim0.04$. This indicates that the interpretation of clustering redshifts in real-world applications will require more sophisticated (parameterised) models of the redshift distribution in the future. If such better models are available, the clustering-redshift technique promises to be a highly complementary alternative to other methods of redshift calibration.

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arXiv2021

Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4

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 allows to 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 model for classification, while ANN performs the best for combined classification and redshift. The inference results are tested using number counts, Gaia parallaxes, and other quasar catalogs. Based on these tests, we derived the minimum classification probability 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 modeled with Gaussian uncertainties. The redshift error (mean and scatter) equals 0.01 +/- 0.1 in the safe subset and -0.0004 +/- 0.2 in the extrapolation, in a redshift range of 0.14 < z < 3.63. Our success of the extrapolation challenges the way that models are optimized and applied at the faint data end. The catalog is ready for cosmology and active galactic nucleus (AGN) studies.

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