arXiv subjects

Harry Desmond

Publications and source records attributed to Harry Desmond.

At least 19 recordsLinked to original sources

arXiv2026

MIGHTEE-HI / LADUMA: Investigating the link between baryons and dynamics with 130 resolved HI-selected galaxies

The baryonic Tully-Fisher relation (bTFR) and the radial acceleration relation (RAR) link the observed dynamics in galaxies to that expected from their baryonic mass distributions. The relations' small intrinsic scatters place strong constraints on galaxy formation models, dark matter properties and theories of modified dynamics, yet detailed measurements beyond the very local Universe remain limited. We use 130 purely HI-selected galaxies with resolved HI kinematics and baryonic mass profiles to measure the bTFR and RAR up to $z\approx0.09$. We measure a tight RAR with an acceleration scale $a_0=(1.50\pm0.05)\times10^{-10},{\rm m,s^{-2}}$ and an intrinsic scatter of $0.096\pm0.006$ dex, consistent with local results. We fit the bTFR in the `inverse' direction, conditioning on $M_{\rm bar}$ to mitigate HI flux-related selection effects, measuring a logarithmic slope of $0.27\pm0.01$ (corresponding to a forward slope of $3.72\pm0.16$), with vertical intrinsic scatter $\sigma_\perp\approx0.05$ dex. Fitting the general $\delta$-family of MOND interpolating functions to the RAR, we infer $\delta=4.10^{+1.4}_{-0.68}$, consistent with the value required by Solar System gravitational constraints and a null Wide Binary Test. We find no significant redshift evolution in the RAR acceleration scale for our pure HI-selected sample. However, the bTFR zero-point shows an apparent evolutionary trend that is strongly dependent on the fit direction: the traditional forward fit yields an $8.7\sigma$ preference for $z$ evolution, while for our fiducial inverse fit, this reduces to $3.4\sigma$, within $\approx2\sigma$ of the RAR evolution constraint. This suggests selection effects bias the forward fit; a careful consideration of such effects will be required in future endeavours to robustly measure the redshift evolution of dynamical scaling relations.

astro-ph.GA↗

arXiv2026

Disentangling modified gravity and galaxy bias with field-level inference

We present a field-level inference framework for testing gravity with the large-scale structure that exploits the full information content of the galaxy distribution. Traditional analyses based on the power spectrum discard non-Gaussian and Fourier phase information, resulting in strong degeneracies between modified gravity (MG) and galaxy bias. Our approach overcomes this limitation by performing a Bayesian likelihood analysis directly on the three-dimensional galaxy number counts field, jointly constraining MG and bias parameters using both amplitudes and phases. As an illustrative application, we analyse mock data in the context of the $f(R)$ theory of gravity and a non-linear galaxy bias model. Non-linear structure formation is modelled using the COmoving Lagrangian Acceleration (COLA) method under different gravity strengths, parameterised by $f_{R0}$. The resulting dark matter fields are then mapped to mock galaxy catalogues via a non-linear bias prescription. We demonstrate that, with fixed and known initial phases, including non-Gaussian and phase information yields tighter constraints on both $f_{R0}$ and the primary bias parameter, $\beta$, relative to the power-spectrum-only analyses. Notably, the field-level approach breaks the degeneracies between MG and galaxy bias inherent to two-point statistics. Through a cosmic web classification into voids, walls, filaments and clusters, we find that under-dense regions are the primary drivers in distinguishing gravity models at the field level. Finally, we establish the robustness of our pipeline against variations in initial conditions, Poisson noise, and galaxy field thresholding, providing a powerful path forward for field-level tests of gravity with next-generation surveys.

astro-ph.CO↗

arXiv2026

The age of the Universe from a large sample of the oldest Galactic stars

We estimate the age of the Universe using the Xiang & Rix sample of 247,103 Milky Way stars with high-resolution spectroscopy from LAMOST DR7 and $Gaia$ eDR3 parallaxes. Stellar ages were estimated using YY isochrones up to 20 Gyr. To remove stars with unusually high and precise ages, we require old stars to be metal-poor and $\alpha$-enriched. We also require consistency between YY ages and those obtained with FLAME based only on $Gaia$ data. Our final sample of 155,600 stars within 5 kpc provides consistent cosmic age estimates using several techniques of increasing rigour. Our main results use an MCMC reconstruction of the latent age distribution, though our iterative reconstruction is very similar. Applying an innovative approach to our MCMC reconstruction and its uncertainties, we find that the oldest star has an age of $A_\star = 13.73^{+0.18}_{-0.15}$ Gyr. Varying the quality cuts can at most reduce this to $A_\star = 13.31^{+0.21}_{-0.18}$ Gyr or raise it to $14.02^{+0.18}_{-0.15}$ Gyr using a much lower or higher age-dependent metallicity ceiling, respectively. Our inferred $A_\star$ is consistent with the 13.6 Gyr expected in CMB-calibrated $\Lambda$CDM, assuming the first long-lived stars formed when the Universe was 0.2 Gyr old. This agreement casts doubt on solutions to the Hubble tension solely through new physics prior to recombination, which generally imply a cosmic age of $12.9 \pm 0.2$ Gyr to match low redshift probes. It is difficult for stellar modelling uncertainties to reconcile such a low age with our result given the low metallicities of the oldest stars in our sample and independent asteroseismic constraints.

astro-ph.CO↗

arXiv2026

Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression

We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions. We compare three well-established random initialization methods as well as initialization with small optimized solutions from exhaustive symbolic regression (ESR) using a GP/SR implementation which is based on the multi-objective evolutionary algorithm NSGA-II. We compare the final Pareto fronts found with each initialization method on twelve synthetic problems of varying complexity and one real-world dataset. We find no significant differences in accuracy or model complexity among the initialization methods. The initial advantage of initialization with ESR disappears after only a few generations. Our results show that, given similar diversity in the initial population, the effect of the initialization method in GP-based symbolic regression on the final Pareto front is negligible.

cs.NE↗

arXiv2026

Forward-modelling the Tolman and distance-duality tests with IllustrisTNG

The Tolman surface-brightness test and the angular-size distance-duality test are two complementary probes of the same underlying relation between luminosity and angular-diameter distance, $D_L = (1+z)^2 D_A$, as holds in any metric theory of gravity where photon number is conserved. Both tests have recently delivered a priori surprising signals: JWST/ASTRODEEP measurements yield a surface brightness scaling with redshift much flatter than the expected value, and ultracompact radio sources also appear to follow a flatter $D_L/D_A$ scaling with redshift. These results have been suggested to support non-expanding cosmologies, however they are also sensitive to astrophysical and instrumental effects. We test whether these results indicate genuine departures from standard cosmology by forward-modelling observed surface-brightness evolution in the IllustrisTNG cosmological hydrodynamical simulation, with an empirical mock-spectroscopic selection trained on ASTRODEEP. We show that the astrophysical evolution relevant for both tests may be effectively parametrised as a single power-law exponent for the luminosity density as a function of redshift, for which the simulation gives $\gamma=2.23\pm0.20$ across realistic aperture conventions. This value is approximately sufficient to explain both the Tolman and distance-duality signals within standard cosmology and galaxy formation physics, with a small discrepancy for the latter suggesting that radio AGN evolve slightly more strongly than bright galaxies.

astro-ph.CO↗

arXiv2026

CMBolic: Symbolic emulators for the Cosmic Microwave Background. I. Lensing

We present the first installment of CMBolic: a suite of symbolic cosmic microwave background (CMB) emulators. In this instance, we emulate the CMB lensing potential power spectrum $C_\ell^{\phi\phi}$ for the widely used extended $\Lambda$CDM model which simultaneously includes massive neutrinos and evolving dark energy modelled using the Chevallier-Polarski-Linder (CPL) parameterization. We achieve comparable precision to existing neural network emulators, with the added benefit of simpler handling as our emulators are analytic functions of the model parameters and multipole $\ell$. On independent validation spectra evaluated in the range $2\leq \ell \leq 5500$, CMBolic achieves mean absolute fractional errors of $0.27\%$ in the $\Lambda$CDM subspace and $0.32\%$ across the full extended parameter space. This emulation error is well below even the most optimistic noise forecasts from CMB Stage 4 experiments. We apply CMBolic to cosmological parameter estimation with Bayesian inference using the lensing-only likelihoods from ACT DR6 and Planck. We show excellent agreement between the posteriors obtained by CMBolic and the Boltzmann code CLASS. This demonstrates the practical use of CMBolic on cosmological parameter estimation, reducing the runtime from 2 weeks to under 3 minutes.

astro-ph.CO↗

arXiv2026

Precision constraints on stellar physics from main sequence detached eclipsing binaries

We present a Bayesian framework to constrain {mass ($M$), metallicity ($Z$), stellar age ($\tau$), and the convective mixing length parameter ($\alpha_{\rm MLT}$)} in main-sequence (MS) detached eclipsing binaries (DEBs). These systems provide precise values of stellar mass and radius, offering stringent tests of stellar evolution models. We combine these with broadband magnitudes in the $B$ and $V$ bands and Gaussian priors on spectroscopic mass and metallicity, and perform Markov Chain Monte Carlo inference using a fast machine-learning surrogate for one-dimensional stellar evolution models computed with Modules for Experiments in Stellar Astrophysics. To make this approach computationally feasible, we implement an active learning strategy that adaptively selects new stellar models to evaluate, concentrating training data in regions of parameter space where the surrogate is most uncertain. Applying this framework to 38 stars in DEB systems, we recover ages more precise than previous isochrone-based determinations and obtain bounds on $\alpha_{\rm MLT}$ for a subset of lower-mass stars ($M \lesssim 1.5 M_\odot$), where convective envelopes provide sensitivity to the mixing length parameter. For several stars, the inferred $\alpha_{\rm MLT}$ values lie below the Solar-calibrated value, supporting previous indications that a universal mixing length parameter may not adequately describe convection across the main sequence. The active learning methods developed here provide a scalable route to Bayesian inference with stellar evolution models, with clear applications to additional stellar physics parameters and other precisely characterized stellar systems.

astro-ph.SR↗

arXiv2026

Constraints on the gravitational potential from DESI DR2 BAO and its implications for the local void scenario

We constrain the difference in gravitational potential between our location and sources at $z \gtrsim 0.3$ using datasets at those redshifts. Our motivation is that the Hubble tension might be caused by a local void, as suggested by galaxy number counts. This would increase the redshift through outflow and gravitational redshift (GR). Only the latter is important at high redshift, where a void contributes a fixed additional GR contribution of $z_0$ due to our location on a potential hill. This $z_0$ model has various subtle effects that were not previously considered, including a hotter CMB and reduced BAO scale $r_{\rm d}$. We test whether $z_0$ can have the previously expected value of 0.84%, which was based on fitting void parameters to galaxy number counts and local $H_0$ measurements. Combining BBN, CMB, BAO, and CC datasets at $z > 0.5$, we find that $z_0 = -0.4^{+0.8}_{-0.9}\%$, which rises to $-0.1 \pm 0.7\%$ when extending our analysis down to $z > 0.29$. Although the results prefer the standard value of $z_0 = 0$, the best-fitting model with $z_0 = 0.84\%$ fits the data almost as well as $\Lambda$CDM, with $\Delta \chi^2 < 2$. We find that $\Lambda$CDM faces a $3.07\sigma$ BAO anomaly in the standard $(H_0 r_{\rm d}, \Omega_{\rm m})$ parameter space, where different regions are preferred by BAO and non-BAO datasets from $z > 0.29$. Fixing $z_0 = 0.84\%$ reduces this to $2.79\sigma$. This suggests that a local void large enough to solve the Hubble tension cannot be ruled out by higher-redshift datasets despite its novel impacts on them.

astro-ph.CO↗

arXiv2026

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions

Symbolic regression with genetic programming (GPSR) may suffer from overfitting and structural bloat, especially when noise is present. In this paper we evaluate description length (DL) and fractional Bayes factor (FBF) criteria as principled, data-efficient alternatives to heuristics for selecting compact expressions that generalise well. We implement DL using a Fisher-information-based parameter encoding and compare it to AIC and BIC across multiple datasets, including noisy synthetic benchmarks and real-world regression problems. We study three search/selection strategies: (i) multi-objective search for accuracy and program length followed by DL/FBF selection; (ii) multi-objective search using DL directly as an objective; and (iii) single-objective optimisation with DL/FBF as the fitness. Across datasets we find that DL/FBF post-selection improves test performance compared to AIC/BIC baseline and that BIC in combination with the same function complexity penalty from DL/FBF produces similar results. In contrast, using DL/FBF directly as a fitness function in single-objective GPSR frequently induces premature convergence to overly simple models. We conclude with practical guidance for using DL/FBF as robust model-selection tools in genetic programming workflows.

cs.NE↗

arXiv2026

The Pulsar Radial Acceleration Relation

The radial acceleration relation (RAR) links observed and baryonic accelerations, and is best established in rotation curves of late-type galaxies. Pulsar timing, which measures line-of-sight (LOS) differential accelerations between the Sun and pulsars, provides a novel probe of this relation, including along directions outside the Galactic disc. By combining these pulsar differential accelerations with the acceleration at the Sun, we test whether current pulsar timing data carry information on a vector generalisation of the RAR, ${g}_{\rm obs}=\nu(|{g}_{\rm bar}|){g}_{\rm bar}$. Comparing the measured SPARC RAR (generalised to 3D) to 26 binary-system pulsars with literature accelerations, we find a reduced $\chi^2$ of 3.58, compared with 10.86 for Newtonian baryonic gravity alone. However, setting all accelerations to that of the Sun gives a reduced $\chi^2$ of 3.75, showing that this vector RAR test is dominated by the Solar acceleration with current data.

astro-ph.GA↗

arXiv2026

Exhaustive Symbolic Integration: Integration by Differentiation and the Landscape of Symbolic Integrability

We introduce Exhaustive Symbolic Integration (ESI), a method that enumerates all symbolic functions up to a given complexity $k$ within a specified operator basis and determines which admit closed-form antiderivatives within the same class. This allows us to compute the "integrability fraction" $\rho(k)$ (the fraction of functions whose derivatives lie within the same class), which we do for five operator bases including combinations of rational functions, powers, exponentials, logarithms and trigonometric functions. We find that $\rho(k)$ declines at high complexity and that the operator basis has a dramatic effect -- in particular, adding the logarithm boosts $\rho(k)$ by a factor of $\sim$3 and produces or exacerbates a clear peak at $k=6$. We also deploy ESI as a novel integration algorithm, identifying three integrals that resist SymPy, Mathematica, RUBI, FriCAS, Maxima and Giac under all tested strategies. When an antiderivative can be found by multiple methods, ESI often returns the simplest form. These results reveal that the landscape of symbolic integrability is shaped primarily by the choice of operators, and that exhaustive enumeration can systematically discover integrable forms -- including novel ones -- that elude computer albegra systems.

cs.SC↗

arXiv2026

The functional form of galaxy and halo luminosity and mass functions

The galaxy luminosity and stellar mass function (LF, SMF), and halo mass function (HMF), are fundamental quantities in astrophysics and crucial inputs to a range of astrophysical and cosmological analyses. They are typically parametrised by fitting functions that have been chosen "by eye" to match observed or simulated data. We apply symbolic regression -- specifically the Exhaustive Symbolic Regression (ESR) algorithm -- to automate the search for optimal LF, SMF and HMF functional forms. ESR scores all functions up to a maximum complexity composed of a user-defined basis set of operators using the description length, an approximation to the Bayesian evidence that balances accuracy with complexity. We find many functions outperforming the Schechter and double Schechter functions for the LF and SMF, and that outperform the Press--Schechter and Warren/Tinker functions for the HMF. By additionally imposing "physicality checks" on functions' extrapolation and integration properties, we identify the optimal, low-complexity functional forms in terms of accuracy, simplicity and behaviour beyond the data range. As well as providing drop-in replacements for literature LF, SMF and HMF fitting functions, and identifying robust behaviour across well-fitting functions, we present a framework with which symbolic regression may be used to automate the discovery of optimal functions for any astrophysical dataset.

astro-ph.GA↗

arXiv2026

Forward-modelling Milky Way Cepheids: selection effects and physical priors in the Gaia-HST calibration

The advent of high-precision Gaia parallaxes for Milky Way Cepheids enables per cent-level calibration of the local distance ladder and the Hubble constant $H_0$. We revisit the Milky Way Cepheid calibration from Gaia EDR3 parallaxes using a fully forward-modelled Bayesian framework that simultaneously infers the period--luminosity relation, the Gaia parallax zero-point offset, and individual stellar distances while explicitly incorporating the disc geometry of the Galaxy through the distance prior and the selection functions specified in two HST SH0ES campaigns. We derive an analytic treatment of the detection probability that accounts for magnitude, parallax, period, and extinction cuts and reduces it to a tractable integral over distance and sky position. Posterior predictive checks show that this generative model matches the observed distributions of parallaxes, magnitudes, and periods. Modelling Galactic structure and survey truncation self-consistently in a Bayesian framework yields period--luminosity parameters that agree with the SH0ES maximum-likelihood values at the ${<}0.5\,\sigma$ level, a consequence of the small intrinsic scatter of the Cepheid period--luminosity relation. Adopting the uniform-in-volume prior recently advocated by H\"og\r{a}s & M\"ortsell (2026), without simultaneously accounting for selection, leads to a ${\sim}\,0.05~\mathrm{mag}$ bias in the period--luminosity zero-point and posterior predictive distributions incompatible with the observed data; this shift is mostly driven by the omission of the selection model, and produces an apparent and unjustified shift in $H_0$ that reflects this mismodelling. A consistent Bayesian treatment of Galactic structure and selection effects reinforces the local distance-ladder determination of $H_0$, and hence the Hubble tension with early-Universe inferences.

astro-ph.GA↗

arXiv2026

Testing cosmic anisotropy with cluster scaling relations

We test claims of large-scale anisotropy in the local expansion rate using cluster scaling relations as distance indicators. Using a Bayesian forward model, we jointly fit the X-ray luminosity--temperature (LT) and thermal Sunyaev-Zel'dovich--temperature (YT) relations, marginalising over the latent cluster distances and modelling selection effects as well as peculiar velocities. The latter are modelled using reconstructions of the local peculiar velocity field where we self-consistently account for possible anisotropic redshift--distance relations via an approximate scheme. This treatment proves crucial to the inferred anisotropy and breaks the degeneracy between anisotropy in scaling relation normalisations and underlying cosmological anisotropy. We apply our method to 312 clusters at $z \lesssim 0.2$, testing dipolar, quadrupolar and general (pixelised) anisotropy models. Bayesian model selection finds no more than weak evidence for any anisotropic model. For dipole models, we obtain upper limits of $\delta H_0 / H_0 < 3.2\%$ and bulk flow magnitude $< 1300\,\mathrm{km\,s^{-1}}$. Our results contrast with previous claims of statistically significant anisotropy from the same data, which we attribute to our principled forward modelling of both redshifts and scaling relation observables through latent distances and our treatment of the impact of anisotropic redshift--distance relations when modelling the local peculiar velocity field. Our work highlights the importance of accurately modelling peculiar velocities when testing isotropy with distance indicators, and motivates the further development of reconstructions that self-consistently treat large-scale deviations from the Hubble flow.

astro-ph.CO↗

arXiv2026

The local void model for the Hubble and BAO tensions

The inconsistency between the locally inferred Hubble constant and the value inferred from the cosmic microwave background assuming the $\Lambda$CDM cosmological model has persisted, turning into an important problem. An emergent underlying trend is that this Hubble tension is driven by data confined to the very low-redshift Universe (typically $z < 0.15$). Most intermediate-redshift measurements remain mutually consistent with $H_0^\mathrm{CMB}$, the $\Lambda$CDM expectation anchored by the CMB. This Perspective examines if a large local void can explain the Hubble tension and its appearance only at low $z$. For an observer residing within a large underdensity, such as the Milky Way inside the claimed KBC void, gravitationally induced outflows and redshift can inflate the locally inferred recession scale $cz'$ despite having $H_0 = H_0^\mathrm{CMB}$. We summarise evidence suggestive of a local underdensity from multi-wavelength galaxy number counts, discuss the dynamical requirements implied by the amplitude of inferred bulk flows, and connect the solution to the emerging low-redshift BAO distance anomaly ($\alpha_{\mathrm{iso}} < 1$). Previously published semi-analytic void models anticipated the observed redshift dependence of BAO deviations and predict a rapid convergence to CMB-consistent expansion for $z \gtrsim 0.2$, aligning with reconstructions of $H_0(z)$ from BAO plus uncalibrated Type Ia supernovae. We conclude by looking to future tests, including improved mapping of the local density and velocity field, fits to galaxy distance catalogues at the field level, kinematic Sunyaev-Zel'dovich constraints on coherent outflows, fast radio bursts, and the long-term prospect of redshift drift measurements as a direct probe of time-varying non-cosmological redshift contributions.

astro-ph.CO↗

arXiv2026

Validating Digital Twins of the Local Universe with the Thermal Sunyaev-Zel'dovich Signal

The thermal Sunyaev-Zel'dovich (tSZ) effect provides a powerful probe of the thermal pressure of ionised gas in galaxy clusters and the cosmic web; constrained simulations reconstruct the mass and velocity fields of the local Universe. We explore how these two may be mutually informative: the tSZ signal provides a benchmark for assessing the fidelity of constrained simulations, and constrained simulations contribute information on the positions, total masses and density profiles of cosmic web structures for use in tSZ studies. We focus on cluster predictions in the Bayesian Origin Reconstruction from Galaxies (BORG) paradigm, introducing CSiBORG-Manticore, a new state-of-the-art suite of digital twins -- data-constrained posterior simulations whose initial conditions are inferred via Bayesian forward modelling. We develop a framework for scoring constrained simulations on their ability to match measured Planck Compton-$y$ maps around clusters, and use it to demonstrate improvement from previous BORG reconstructions. We further validate halo masses against weak-lensing-calibrated X-ray masses from eROSITA. We also show how high-fidelity digital twins offer a practical route to extracting additional information from tSZ data through a novel calibration of the mass-observable relation, and provide a complementary framework to purely statistical analyses of Compton-$y$ maps. This paves the way for integrating the large-scale structure information inherent in constrained simulations into the study of CMB secondary anisotropies.

astro-ph.CO↗

arXiv2026

Revisiting the Great Attractor: The Local Group's streamline trajectory, cosmic velocity and dynamical fate

We revisit the Great Attractor using the Manticore-Local suite of digital twins of the nearby Universe. The Great Attractor concept has been proposed as an answer to three distinct questions: what sources the Local Group velocity in the cosmic microwave background frame, where present-day velocity streamlines converge, and where the Local Group is moving to. Addressing the original motivation of the Great Attractor -- explaining the Local Group cosmic velocity -- we find that mass within $155~h^{-1}\mathrm{Mpc}$ accounts for only ${\sim}72\%$ of that velocity magnitude with ${\sim}38\,\deg$ directional offset. We show that even in the purely linear regime convergence within this volume is not guaranteed, particularly when also accounting for small-scale contributions to the observer velocity; no single structure, including the proposed Great Attractor, would be expected to dominate the velocity budget. Streamline convergence is smoothing-scale-dependent, transitioning from Virgo at small scales through the Hydra--Centaurus region at intermediate scales to Shapley at large scales; at intermediate smoothing the convergence point lies near Abell 3565 with an asymmetric basin of mass $\log( M / (h^{-1} \mathrm{M}_\odot)) = 16.4 \pm 0.1$ that excludes Norma. To address the third question, we evolve the Manticore-Local realisations to scale factor $a = 10$ in a new Beyond-Present-Time simulation suite and identify the asymptotic future location of the Local Group. We find that the dominant motion is towards Virgo, but even it contributes at most one third of the Local Group velocity. Our results demonstrate that the classical Great Attractor is not a dynamically dominant structure but an artefact of the instantaneous velocity field, and that no single attractor is likely to account for the Local Group motion in the cosmic rest frame.

astro-ph.CO↗

arXiv2026

Testing subhalo abundance matching with galaxy kinematics

The rotation velocities of disc galaxies trace dark matter halo structure, providing direct constraints on the galaxy--halo connection. We construct a Bayesian forward model to connect the dark matter halo population predicted by $\Lambda$CDM with an observed sample of disc galaxies (SPARC) through their maximum rotation velocities. Our approach combines a subhalo abundance matching scheme (accounting for assembly bias) with a parameterised halo response to galaxy formation. When assuming no correlation between selection in the SPARC survey and halo properties, reproducing the observed velocities requires strong halo expansion, low abundance matching scatter ($<0.15$ dex at $1\sigma$) and a halo proxy that strongly suppresses the stellar masses in satellite haloes. This is in clear tension with independent clustering constraints. Allowing for SPARC-like galaxies to preferentially populate low $\Vmax$ haloes at fixed virial mass greatly improves the goodness-of-fit and resolves these tensions: the preferred halo response shifts to mild contraction, the abundance matching scatter increases to $\sint = 0.19^{+0.13}_{-0.11}$ dex and the proxy becomes consistent with clustering. However, the inferred selection threshold is extreme, implying that SPARC galaxies occupy the lowest ${\sim}16$ per cent of the $\Vmaxhalo$ distribution at fixed $\Mvir$. Moreover, even with selection, the inferred scatter remains in statistical disagreement with the low-mass clustering constraints, which are most representative of the SPARC galaxies in our sample. Our analysis highlights the advantage of augmenting clustering-based constraints on the galaxy--halo connection with kinematics and suggests a possible tension using current data.

astro-ph.GA↗