arXiv subjects

Angus H. Wright

Publications and source records attributed to Angus H. Wright.

At least 19 recordsLinked to original sources

arXiv2026

KiDS-Legacy: The consistency test of the large-scale structure with Bernardeau-Nishimichi-Taruya transform

We perform the first $k$-cut cosmic shear analysis of the KiDS-Legacy survey. This method uses the Bernardeau-Nishimichi-Taruya (BNT) transform to construct weak-lensing kernels that are more localised than conventional ones, and remove information from selected physical scales while retaining the constraining power of the targeted range. Removing the scale of $k \geq 0.33~\mathrm{Mpc}^{-1}$ from the KiDS-Legacy pseudo-$C_\ell$ data vector, and using a covariance matrix whose Gaussian component is computed from the theoretical data vector, we find $S_8 = 0.798 \pm 0.045$. This agrees with both the fiducial KiDS-Legacy bandpower result and our no-$k$-cut pseudo-$C_\ell$ posterior to within $0.1\sigma$, indicating no significant bias from nonlinear astrophysical feedback at the precision of KiDS-Legacy. We also study the case in which the Gaussian covariance is computed from the observed data vector. In this setup, the same scale cut of $k < 0.33~\mathrm{Mpc}^{-1}$ gives a much lower $S_8=0.717_{-0.046}^{+0.047}$. Further $k$-cut tests reveal a mild scale-dependent trend, with larger physical scales preferring lower $S_8$ values and a maximum low- versus high-$k$ deviation of $1.80\sigma$. Mock tests show that this behaviour is not produced by the covariance prescription or data vector alone, but may arise from their interplay. These results show that BNT $k$-cuts provide both a mitigation strategy for nonlinear systematics and a diagnostic of weak-lensing inference pipelines.

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arXiv2026

cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era

cloelib is a Python library developed to compute cosmological observables within the Cosmology Likelihood for Observables in Euclid (CLOE) ecosystem (cloe-org). As cosmology enters a precision era driven by galaxy survey missions such as Euclid, there is a growing need for flexible, efficient, and differentiable software capable of supporting next-generation inference pipelines. cloelib addresses these demands through a modular architecture that interfaces seamlessly with established Boltzmann solvers whilst incorporating JAX-based automatic differentiation to enable gradient-based methods. The library defines consistent protocols for background evolution, perturbations, and non-linear structure formation, and supports a wide range of observables, including photometric and spectroscopic large-scale structure probes, as well as cross-correlations with the Cosmic Microwave Background and galaxy clusters. In its finalised form, cloelib is intended to serve as the reference theory computation infrastructure for Euclid's first cosmological release, bridging traditional numerical cosmology with modern optimisation techniques and emerging machine learning approaches to inference.

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arXiv2026

pop-cosmos: Forward modeling KiDS-1000 redshift distributions using realistic galaxy populations

The accuracy of the cosmological constraints from Stage~IV galaxy surveys will be limited by how well the galaxy redshift distributions can be inferred. We have addressed this challenging problem for the Kilo-Degree Survey (KiDS) cosmic shear sample by developing a forward-modeling framework with two main ingredients: (1) the \texttt{pop-cosmos} generative model for the evolving galaxy population, calibrated on \textit{Spitzer} IRAC $\textit{Ch.\,1}<26$ galaxies from COSMOS2020; and (2) a data model for noise and selection, machine-learned from the SURFS-based KiDS-Legacy-Like Simulations (SKiLLS). Applying KiDS tomographic binning to our synthetic photometric data, we infer redshift distributions in each of five bins directly from the population and data models, bypassing the need for spectroscopic reweighting. Keeping the data model fixed, we compare results using two different galaxy population models: \texttt{pop-cosmos}; and \texttt{shark}, the semi-analytic galaxy formation model used in SKiLLS. In the first ($0.1<z<0.3$) and last ($0.9<z<1.2$) tomographic bins we find systematic differences in the mean redshifts of $\Delta z\sim0.05$-$0.1$, comparable to the reported uncertainties from spectroscopic reweighting methods. This work paves the way for accurate redshift distribution calibration for Stage~IV surveys directly through forward modeling, thus providing an independent cross-check on spectroscopic-based calibrations which avoids their selection biases and incompleteness. We will use the \texttt{pop-cosmos} redshift distributions in an upcoming full KiDS cosmology reanalysis.

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arXiv2026

pop-cosmos: Redshifts and physical properties of KiDS-1000 galaxies

Principled Bayesian inference of galaxy properties has not previously been performed for wide-area weak lensing surveys with millions of sources. We address this gap by applying the pop-cosmos generative model to perform spectral energy distribution (SED) fitting for 4 million KiDS-1000 galaxies. Calibrated on deep COSMOS2020 photometric data, pop-cosmos specifies a physically-motivated prior over the galaxy population up to $z \simeq 6$ in stellar population synthesis (SPS) parameter space. Using the Speculator SPS emulator with GPU-accelerated MCMC sampling, we perform full posterior inference at 8.2 GPU seconds per galaxy, obtaining joint constraints on galaxy redshifts and physical properties. We validate photometric redshifts against $\sim\!185,\!000$ KiDS galaxies cross-matched to DESI DR1 spectroscopic samples, achieving low bias ($2\times10^{-3}$), scatter ($\sigma_{\mathrm{MAD}}=0.03$), and outlier fraction (3.2%) for the Bright Galaxy Survey, with comparable performance (bias $3\times10^{-2}$, $\sigma_{\mathrm{MAD}}=0.05$, 1.0% outliers) for luminous red galaxies (LRGs). Within the LRG sample, we identify massive, dusty, star-forming contaminants at $z \simeq 0.4$ satisfying standard colour selections for quenched populations. We infer trends in stellar mass, star formation, metallicity, and dust across five tomographic redshift bins consistent with established scaling relations. Using specific star formation rate constraints, we identify $\sim$7% of KiDS-1000 galaxies as quenched, versus 37% implied by conservative colour cuts. This enables the construction of weak lensing samples defined by physical properties while mitigating intrinsic alignment systematics and preserving statistical power. Our analysis validates pop-cosmos out-of-sample, establishing it as a scalable approach for galaxy evolution and cosmological analyses with photometric surveys.

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arXiv2025

The Power of DESI for Photometric Redshift Calibration: A Case Study with KiDS-1000

Accurate redshift estimates are a critical requirement for weak lensing surveys and one of the main uncertainties in constraints on dark energy and large-scale cosmic structure. In this paper, we study the potential to calibrate photometric redshift (photo-z) distributions for gravitational lensing using the Dark Energy Spectroscopic Instrument (DESI). Since beginning its science operations in 2021, DESI has collected more than 50 million redshifts, adding about one million monthly. In addition to its large-scale structure samples, DESI has also acquired over 256k high-quality spectroscopic redshifts (spec-zs) in the COSMOS and XMM and VVDS fields. This is already a factor of 3 larger than previous spec-z calibration compilations in these two regions. Here, we explore calibrating photo-zs for the subset of KiDS-1000 galaxies that fall into joint self-organizing map (SOM) cells overlapping the DESI COSMOS footprint using the DESI COSMOS observations. Estimating the redshift distribution in KiDS-1000 with the new DESI data, we find broad consistency with previously published results while also detecting differences in the mean redshift in some tomographic bins with an average shifts of Delta Mean(z) = -0.028 in the mean and Delta Median(z) = +0.011 in the median across tomographic bins. However, we also find that incompleteness per SOM cell, i.e., groups of galaxies with similar colors and magnitudes, can modify n(z) distributions. Finally, we comment on the fact that larger photometric catalogs, aligned with the DESI COSMOS and DESI XMM and VVDS footprints, would be needed to fully exploit the DESI dataset and would extend the coverage to nearly eight times the area of existing 9-band photometry.

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arXiv2025

KiDS-Legacy: Constraints on Horndeski gravity from weak lensing combined with galaxy clustering and cosmic microwave background anisotropies

We present constraints on modified gravity from a cosmic shear analysis of the final data release of the Kilo-Degree Survey (KiDS-Legacy) in combination with DESI measurements of baryon acoustic oscillations, eBOSS observations of redshift space distortions, and cosmic microwave background anisotropies from Planck. We study the Horndeski class of modified gravity models in an effective field theory framework employing a parameterisation that satisfies stability conditions by construction and, for the first time, present a cosmological analysis in this inherently stable parameter basis. Cosmic shear constrains the Horndeski parameter space significantly, matching or surpassing the CMB contribution. Adopting the de-mixed kinetic term of the scalar field perturbation, $D_{\rm kin}$, and the deviation of the Planck mass from its fiducial value, $\Delta M_*^2\equiv M_*^2-1$, as model parameters, we constrain their present values to be $\Delta \hat{M}_*^2=0.32^{+0.07}_{-0.21}$ and $\hat{D}_{\rm kin} = 3.74^{+0.69}_{-1.92}$, which deviate from general relativity at $1.5\sigma$ and $1.9\sigma$, respectively. We derive constraints on the structure growth parameter $S_8=0.813^{+0.008}_{-0.011}$, which is compatible with the $\Lambda$CDM constraint at $0.54\sigma$. We obtain the deviation of the effective Newtonian coupling from the GR value as $\Delta \mu_{\infty,{\rm eff}}=0.066\pm0.023$, corresponding to a $2.9\sigma$ significance. Although modified gravity provides a slightly better fit to the data, a model comparison shows only a weak preference for modified gravity at the $1.4\sigma$ level. When adopting a dynamical dark energy model of the background cosmology, the inferred modified gravity parameter constraints are stable with respect to a $\Lambda$CDM background, while a mild preference at $1.57\sigma$ for dynamical dark energy remains.

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arXiv2025

KiDS-Legacy: Constraining dark energy, neutrino mass, and curvature

We constrained minimally extended cosmological models with the cosmic shear analysis of the final data release from the Kilo-Degree Survey (KiDS-Legacy) in combination with external probes. Due to the consistency of the KiDS-Legacy analysis with the cosmic microwave background (CMB), we could combine these datasets reliably for the first time. Additionally, we used CMB lensing, galaxy redshift-space distortions, and baryon acoustic oscillations. We assessed, in turn, the effects of spatial curvature, varying neutrino masses, and an evolving dark energy component on cosmological constraints from KiDS-Legacy alone and from KiDS-Legacy combined with external probes. We find KiDS-Legacy to be consistent with the fiducial flat $\Lambda$-cold dark matter ($\Lambda$CDM) analysis with $c^2 \sum m_\nu\leq 1.5\,$eV, $w_0 = -1.0\pm 0.7$, and $w_a = -1.3^{+1.9}_{-2.0}$ while $\Omega_K = 0.08^{+0.16}_{-0.17}$ (1$\sigma$ bounds) with an almost equal goodness of fit. The $w_0w_a$CDM model is not a significant improvement over $\Lambda$CDM when cosmic shear and CMB lensing are combined, yielding a Bayes factor $B = 0.07$. If all probes are combined, however, $B$ increases to 2.73, corresponding to a $2.6\sigma$ suspiciousness tension. The constraint on $S_8 = \sigma_8\sqrt{\Omega_\mathrm{m}/0.3}$ is robust to opening up the parameter space for cosmic shear. Adding all external datasets to KiDS-Legacy, we find $S_8 = 0.816 \pm 0.006$ in $\Lambda$CDM and $S_8 = 0.837 \pm 0.008$ in $w_0 w_a$CDM for all probes combined.

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arXiv2025

KiDS-1000 cosmic shear reanalysis using MetaCalibration

A number of cosmic shear studies have reported results that are in mild tension with the Planck cosmic microwave measurement. To explore if this can be caused by biases in the shear estimation, we revisit the analysis of data from the Kilo-Degree Survey (KiDS) using an alternative shape measurement pipeline that is more robust to uncertainties in the calibration. To this end, we present an implementation of MetaCalibration, and compare its performance to that of lensfit, which has been used in previous analyses of these data. We find that the multiplicative bias is reduced, especially for the most distant redshifts, as derived from multi-band image simulations designed to match the KiDS data (SURFS-based KiDS-Legacy-Like Simulations: SKiLLS). For all tomographic bins we obtain a multiplicative bias $|m|<0.017$, with negligible additive bias. Importantly, the calibration has a negligible sensitivity to key galaxy properties. The resulting robust shear estimates were used to obtain cosmological parameter constraints. We find that the parameter $S_8\equiv \sigma_8 \sqrt{\Omega_\mathrm{m}/0.3} =0.789_{-0.024}^{+0.020}$ is consistent with the previous KiDS-1000 lensfit constraint of $S_8=0.776^{+0.029 +0.002}_{-0.027-0.003}$ (statistical + systematic errors). Thanks to the higher effective source density, the constraining power is improved by about 28%. The difference in $S_8$ with the Planck value remains at a similar level, 1.8$\sigma$, implying that it is not caused by the shear measurements.

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arXiv2025

Using Deep Learning Methods to Detect for Ultra-diffuse Galaxies in KiDS

Ultra-diffuse Galaxies (UDGs) are a subset of Low Surface Brightness Galaxies (LSBGs), showing mean effective surface brightness fainter than $24\ \rm mag\ \rm arcsec^{-2}$ and a diffuse morphology, with effective radii larger than 1.5 kpc. Due to their elusiveness, traditional methods are challenging to be used over large sky areas. Here we present a catalog of ultra-diffuse galaxy (UDG) candidates identified in the full 1350 deg$^2$ area of the Kilo-Degree Survey (KiDS) using deep learning. In particular, we use a previously developed network for the detection of low surface brightness systems in the Sloan Digital Sky Survey \citep[LSBGnet,][]{su2024lsbgnet} and optimised for UDG detection. We train this new UDG detection network for KiDS (UDGnet-K), with an iterative approach, starting from a small-scale training sample. After training and validation, the UGDnet-K has been able to identify $\sim3300$ UDG candidates, among which, after visual inspection, we have selected 545 high-quality ones. The catalog contains independent re-discovery of previously confirmed UDGs in local groups and clusters (e.g NGC 5846 and Fornax), and new discovered candidates in about 15 local systems, for a total of 67 {\it bona fide} associations. Besides the value of the catalog {\it per se} for future studies of UDG properties, this work shows the effectiveness of an iterative approach to training deep learning tools in presence of poor training samples, due to the paucity of confirmed UDG examples, which we expect to replicate for upcoming all-sky surveys like Rubin Observatory, Euclid and the China Space Station Telescope.

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arXiv2025

Matter power spectrum reconstruction with KiDS-Legacy: Improved internal $\Lambda$CDM consistency and preference for strong baryonic feedback

Direct measurements of the matter power spectrum, $P_\mathrm{m}(k,z)$, provide a powerful tool to investigate observed tensions between models of structure growth while also testing the internal consistency of cosmological probes. We analyse cosmic shear data from the final data release of the Kilo-Degree Survey (KiDS), presenting a deprojected $P_\mathrm{m}(k,z)$, measured in up to three redshift bins. Compared to analyses using previous KiDS releases, we find improved internal consistency in the $z\lesssim0.7$ regime. At large scales, $k\lesssim0.1\,h\,\rm Mpc^{-1}$, our power spectrum reconstruction aligns with $\Lambda$CDM predictions with a density fluctuation amplitude $\sigma_8=0.81$. Furthermore, at small scales, $k=3$-$20\,h\,\rm Mpc^{-1}$, the average matter power spectrum is suppressed by $30\%\pm10\%\,{\rm (stat.)}\pm4\%\,{\rm (sys.)}$ with $2.8\sigma$ significance relative to a dark-matter-only model, consistent with expectations of strong baryonic feedback.

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arXiv2025

Measurement of circumgalactic extinction in the Kilo-Degree Survey's Data Release 4

Galaxies reside in extended dark matter halos, but their baryonic content, especially dust, remains less well understood. We aim to investigate the amount and distribution of dust in galactic halos by measuring shifts in the observed magnitudes of background galaxies. Dust absorbs and scatters shorter-wavelength light, reddening background sources. We quantify this effect using multi-band magnitude shifts in the Kilo-Degree Survey (KiDS) Data Release 4, distinguishing it from achromatic magnification. Our pipeline is validated using MICE2 mock catalogues, and we explore potential systematics arising from galaxy selection, in contrast to previous quasar-based studies. Simulations show that input halo and dust masses can be recovered independently and jointly using galaxy-galaxy lensing (GGL) and magnitude shifts. A minimum redshift separation of $\Delta z_{\mathrm B}\sim0.3$ and brightness cuts are required to minimise overlap effects and the effects of photometric noise, respectively. Applying our pipeline to KiDS data, we first confirm the achromaticity of magnitude shifts in five near-infrared filters ($ZYJHK_{\rm s}$). In optical bands ($ugri$), joint analysis constrains halo mass and dust mass to $0.2$ dex and $0.3$ dex, respectively, yielding $M_{\rm dust}=7.24^{+0.52}_{-0.64} \times 10^7\,M_\odot$ and $\log(M_{\rm halo}^{\rm DM}/M_\odot)=12.33^{+0.08}_{-0.04}$. These results agree with previous studies and represent the first successful measurement of magnitude shifts and halo extinction with KiDS, confirming that galaxies reside in extended baryonic halos enriched by gas and dust from feedback processes.

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arXiv2025

The fifth data release of the Kilo Degree Survey: Multi-epoch optical/NIR imaging covering wide and legacy-calibration fields

We present the final data release of the Kilo-Degree Survey (KiDS-DR5), a public European Southern Observatory (ESO) wide-field imaging survey optimised for weak gravitational lensing studies. We combined matched-depth multi-wavelength observations from the VLT Survey Telescope and the VISTA Kilo-degree INfrared Galaxy (VIKING) survey to create a nine-band optical-to-near-infrared survey spanning $1347$ deg$^2$. The median $r$-band $5σ$ limiting magnitude is 24.8 with median seeing $0.7^{\prime\prime}$. The main survey footprint includes $4$ deg$^2$ of overlap with existing deep spectroscopic surveys. We complemented these data in DR5 with a targeted campaign to secure an additional $23$ deg$^2$ of KiDS- and VIKING-like imaging over a range of additional deep spectroscopic survey fields. From these fields, we extracted a catalogue of $126\,085$ sources with both spectroscopic and photometric redshift information, which enables the robust calibration of photometric redshifts across the full survey footprint. In comparison to previous releases, DR5 represents a $34\%$ areal extension and includes an $i$-band re-observation of the full footprint, thereby increasing the effective $i$-band depth by $0.4$ magnitudes and enabling multi-epoch science. Our processed nine-band imaging, single- and multi-band catalogues with masks, and homogenised photometry and photometric redshifts can be accessed through the ESO Archive Science Portal.

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arXiv2025

KiDS-Legacy: Consistency of cosmic shear measurements and joint cosmological constraints with external probes

We present a cosmic shear consistency analysis of the final data release from the Kilo-Degree Survey (KiDS-Legacy). By adopting three tiers of consistency metrics, we compare cosmological constraints between subsets of the KiDS-Legacy dataset split by redshift, angular scale, galaxy colour and spatial region. We also review a range of two-point cosmic shear statistics. With the data passing all our consistency metric tests, we demonstrate that KiDS-Legacy is the most internally consistent KiDS catalogue to date. In a joint cosmological analysis of KiDS-Legacy and DES Y3 cosmic shear, combined with data from the Pantheon+ Type Ia supernovae compilation and baryon acoustic oscillations from DESI Y1, we find constraints consistent with Planck measurements of the cosmic microwave background with $S_8\equiv \sigma_8\sqrt{\Omega_{\rm m}/0.3} = 0.814^{+0.011}_{-0.012}$ and $\sigma_8 = 0.802^{+0.022}_{-0.018}$.

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arXiv2025

KiDS-Legacy: Cosmological constraints from cosmic shear with the complete Kilo-Degree Survey

We present cosmic shear constraints from the completed Kilo-Degree Survey (KiDS), where the cosmological parameter $S_8\equiv\sigma_8\sqrt{\Omega_{\rm m}/0.3} = 0.815^{+0.016}_{-0.021}$, is found to be in agreement ($0.73\sigma$) with results from the Planck Legacy cosmic microwave background experiment. The final KiDS footprint spans $1347$ square degrees of deep nine-band imaging across the optical and near-infrared, along with an extra $23$ square degrees of KiDS-like calibration observations of deep spectroscopic surveys. Improvements in our redshift distribution estimation methodology, combined with our enhanced calibration data and multi-band image simulations, allow us to extend our lensed sample out to a photometric redshift of $z_{\rm B}\leq2.0$. Compared to previous KiDS analyses, the increased survey area and redshift depth results in a $\sim32\%$ improvement in constraining power in terms of $\Sigma_8\equiv\sigma_8\left(\Omega_{\rm m}/0.3\right)^\alpha = 0.821^{+0.014}_{-0.016}$, where $\alpha = 0.58$ has been optimised to match the revised degeneracy direction of $\sigma_8$ and $\Omega_{\rm m}$. We adopt a new physically motivated intrinsic alignment model that depends jointly on the galaxy sample's halo mass and spectral type distributions, and that is informed by previous direct alignment measurements. We also marginalise over our uncertainty on the impact of baryon feedback on the non-linear matter power spectrum. Comparing to previous KiDS analyses, we conclude that the increase seen in $S_8$ primarily results from our improved redshift distribution estimation and calibration, as well as new survey area and improved image reduction. Our companion paper St\"olzner et al. (submitted) presents a full suite of internal and external consistency tests, finding the KiDS-Legacy data set to be the most internally robust sample produced by KiDS to date.

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arXiv2025

KiDS-Legacy: Redshift distributions and their calibration

We present the redshift calibration methodology and bias estimates for the cosmic shear analysis of the fifth and final data release (DR5) of the Kilo-Degree Survey (KiDS). KiDS-DR5 includes a greatly expanded compilation of calibrating spectra, drawn from $27$ square degrees of dedicated optical and near-IR imaging taken over deep spectroscopic fields. The redshift distribution calibration leverages a range of new methods and updated simulations to produce the most precise $N(z)$ bias estimates used by KiDS to date. Improvements to our colour-based redshift distribution measurement method (SOM) mean that we are able to use many more sources per tomographic bin for our cosmological analyses, and better estimate the representation of our source sample given the available spec-$z$. We validate our colour-based redshift distribution estimates with spectroscopic cross-correlations (CC). We find that improvements to our cross-correlation redshift distribution measurement methods mean that redshift distribution biases estimated between the SOM and CC methods are fully consistent on simulations, and the data calibration is consistent to better than $2\sigma$ in all tomographic bins.

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arXiv2025

KiDS-SBI: Simulation-based inference analysis of KiDS-1000 cosmic shear

We present a simulation-based inference (SBI) cosmological analysis of cosmic shear two-point statistics from the fourth weak gravitational lensing data release of the ESO Kilo-Degree Survey (KiDS-1000). KiDS-SBI efficiently performs non-Limber projection of the matter power spectrum via Levin's method, and constructs log-normal random matter fields on the curved sky for arbitrary cosmologies, including effective prescriptions for intrinsic alignments and baryonic feedback. The forward model samples realistic galaxy positions and shapes based on the observational characteristics, incorporating shear measurement and redshift calibration uncertainties, as well as angular anisotropies due to variations in depth and point-spread function. To enable direct comparison with standard inference, we limit our analysis to pseudo-angular power spectra. The SBI is based on sequential neural likelihood estimation to infer the posterior distribution of spatially-flat $Λ$CDM cosmological parameters from 18,000 realisations. We infer a mean marginal of the growth of structure parameter $S_{8} \equiv σ_8 (Ω_\mathrm{m} / 0.3)^{0.5} = 0.731\pm 0.033$ ($68 \%$). We present a measure of goodness-of-fit for SBI and determine that the forward model fits the data well with a probability-to-exceed of $0.42$. For fixed cosmology, the learnt likelihood is approximately Gaussian, while constraints widen compared to a Gaussian likelihood analysis due to cosmology dependence in the covariance. Neglecting variable depth and anisotropies in the point spread function in the model can cause $S_{8}$ to be overestimated by ${\sim}5\%$. Our results are in agreement with previous analysis of KiDS-1000 and reinforce a $2.9 σ$ tension with constraints from cosmic microwave background measurements. This work highlights the importance of forward-modelling systematic effects in upcoming galaxy surveys.

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arXiv2025

KiDS-1000: Weak lensing and intrinsic alignment around luminous red galaxies

We study the properties of luminous red galaxies (LRGs) selected from the fourth data release of the Kilo Degree Survey (KiDS-1000) via galaxy-galaxy lensing of the background galaxies from KiDS-1000. We used a halo model formalism to interpret our measurements and obtain estimates of the halo masses as well as the satellite fractions of the LRGs, resulting in halo masses of $2.7 \times 10^{12} h^{-1} {\rm M}_{\odot}<M_{\rm h}< 2.6 \times 10^{13} h^{-1} {\rm M}_{\odot}$. We studied the strength of intrinsic alignments (IA) using the position-shape correlations as a function of LRG luminosity, where we used a double power law to describe the relation between luminosity and halo mass to allow for a comparison with previous works. Here, we directly linked the observed IA of the (central) galaxy to the mass of the hosting halo, which is expected to be a fundamental quantity in establishing the alignment. We find that the dependence of the IA amplitude on halo mass is described well by a single power law, with an amplitude of $A = 5.74\pm{0.32}$ and slope of $β_M = 0.44\pm{0.04}$, in the range of $1.9 \times 10^{12}h^{-1} {\rm M}_{\odot}<M_{\rm h}<3.7 \times 10^{14} h^{-1} {\rm M}_{\odot}$. We also find that both red and blue galaxies from the source sample associated with the LRGs are randomly oriented, with respect to the LRGs, although our detection significance is limited by the uncertainty in our photometric redshifts.

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arXiv2025

KiDS-Legacy: Angular galaxy clustering from deep surveys with complex selection effects

Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the high number density and extensive depth of the data. However, the complicated selection effects in wide and deep surveys can potentially cause significant bias in the angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure the 2PCF from the newly published KiDS-Legacy sample. Given an $r$-band $5σ$ magnitude limit of $24.8$ and survey footprint of $1347$ deg$^2$, it achieves an excellent combination of sky coverage and depth for such a measurement. We find that complex selection effects, primarily induced by varying seeing, introduce over-estimation of the 2PCF by approximately an order of magnitude. To correct for such effects, we apply a machine learning-based method to recover an organised random (OR) that presents the same selection pattern as the galaxy sample. The basic idea is to find the selection-induced clustering of galaxies using a combination of self-organising maps (SOMs) and hierarchical clustering (HC). This unsupervised machine learning method is able to recover complicated selection effects without specifying their functional forms. We validate this SOM+HC method on mock deep galaxy samples with realistic systematics and selections derived from the KiDS-Legacy catalogue. Using mock data, we demonstrate that the OR delivers unbiased 2PCF cosmological parameter constraints, removing the $27σ$ offset in the galaxy bias parameter that is recovered when adopting uniform randoms. Blinded measurements on the real KiDS-Legacy data show that the corrected 2PCF is robust to the SOM+HC configuration near the optimal set-up suggested by the mock tests.

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