arXiv subjects

C. -A. Lin

Publications and source records attributed to C. -A. Lin.

3 recordsLinked to original sources

arXiv2021

KiDS-1000 catalogue: Redshift distributions and their calibration

We present redshift distribution estimates of galaxies selected from the fourth data release of the Kilo-Degree Survey over an area of $\sim1000$ 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 $0.1<z_\mathrm{B}\le1.2$. 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 $\lesssim0.01$ for all five bins with uncertainties of $\sim 0.01$. 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 $\sim 0.01-0.02$ 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.

astro-ph.CO↗

arXiv2020

KiDS-1000 Methodology: Modelling and inference for joint weak gravitational lensing and spectroscopic galaxy clustering analysis

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 BOSS and 2dFLenS surveys. Cross-correlations between galaxy positions and ellipticities have been incorporated into the analysis, necessitating a hybrid model of non-linear scales that blends perturbative and non-perturbative approaches, and an assessment of contributions by astrophysical effects. All weak lensing signals are 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 is propagated into the likelihood. A dedicated suite of more than 20000 mocks is 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 to the number of degrees of freedom. Standard weak lensing point estimates on $S_8=σ_8\,(Ω_{\rm m}/0.3)^{1/2}$ derived from its marginal posterior are easily misinterpreted to be biased low, and an alternative estimator and associated credible interval have been 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. [abridged]

astro-ph.CO↗

arXiv2017

On the realistic validation of photometric redshifts, or why Teddy will never be Happy

Two of the main problems encountered in the development and accurate validation of photometric redshift (photo-z) techniques are the lack of spectroscopic coverage in feature space (e.g. colours and magnitudes) and the mismatch between photometric error distributions associated with the spectroscopic and photometric samples. Although these issues are well known, there is currently no standard benchmark allowing a quantitative analysis of their impact on the final photo-z estimation. In this work, we present two galaxy catalogues, Teddy and Happy, built to enable a more demanding and realistic test of photo-z methods. Using photometry from the Sloan Digital Sky Survey and spectroscopy from a collection of sources, we constructed datasets which mimic the biases between the underlying probability distribution of the real spectroscopic and photometric sample. We demonstrate the potential of these catalogues by submitting them to the scrutiny of different photo-z methods, including machine learning (ML) and template fitting approaches. Beyond the expected bad results from most ML algorithms for cases with missing coverage in feature space, we were able to recognize the superiority of global models in the same situation and the general failure across all types of methods when incomplete coverage is convoluted with the presence of photometric errors - a data situation which photo-z methods were not trained to deal with up to now and which must be addressed by future large scale surveys. Our catalogues represent the first controlled environment allowing a straightforward implementation of such tests. The data are publicly available within the COINtoolbox (https://github.com/COINtoolbox/photoz_catalogues).

astro-ph.GA↗