Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.35317

LISA bright sirens as a tiebreaker among cosmologies in tension

M. F. A. Medeiros·F. C. Carvalho·L. L. Sales
View original source ↗Read source document ↗Export metadata ↓

Abstract

Gravitational waves from massive black hole binaries detected by LISA will deliver luminosity distances without any astrophysical calibration, offering a probe of the expansion history independent of the cosmic distance ladder. Rather than asking how tightly LISA will constrain a single fiducial cosmology, we ask which of several mutually discrepant present-day cosmologies a future LISA dataset would render most self-consistent. We build mock catalogues of LISA bright sirens under three fiducial cosmologies drawn from datasets that participate in the current $H_0$ discrepancy (Planck 2018, Union3 and Pantheon+SH0ES), with a self-consistent error budget from instrumental noise, weak lensing and host peculiar velocities. We compare the posterior from the simulated LISA data alone with that of a real-data combination of Type Ia supernovae, DESI baryon acoustic oscillations, cosmic chronometers and the compressed Planck 2018 CMB, and quantify their internal tension in the $(H_0,Ω_m)$ plane, crossing each fiducial with three SN Ia compilations. The fiducial drives the tension, from $2.08σ$ to well beyond $5σ$, while the supernova compilation changes it by at most $1.2σ$. The discriminating power is strongly asymmetric: a Pantheon+SH0ES universe is inconsistent with every background we test, at a significance far in excess of $5σ$ and still at $3$-$5σ$ for the $10$-$20$ events expected from recent population models, whereas the Planck 2018 and Union3 fiducials, which differ by only $0.24$ km/s/Mpc in $H_0$, cannot be separated by any catalogue size. LISA bright sirens are therefore a sharp arbiter of the Cepheid-calibrated scenario and, on this error budget, silent between the other two.

Explore related subjects

astro-ph.CO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M. F. A. Medeiros, F. C. Carvalho, L. L. Sales. 2026-09-28. LISA bright sirens as a tiebreaker among cosmologies in tension. https://arxiv.org/abs/2609.35317

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Everything Everywhere All At Once: A Hierarchical Framework for Mapping Anisotropies in the Local Universe

We present a Bayesian hierarchical forward model for mapping the local luminosity-density field from heterogeneous redshift surveys. By dividing the survey volume into angular cells and redshift shells, we model the observed galaxy distribution directly in apparent-magnitude and redshift space. This allows us to infer cell-level luminosity functions and densities while preserving each survey's unique selection function. Simultaneously, the ensemble of cells constrains the cosmic-mean luminosity function, its redshift evolution, and the hyperparameters describing cell-to-cell variation. We apply the framework to 2MRS, 6dFGS, and GAMA over $0.005<z<0.065$, combining wide sky coverage with deeper and fainter galaxy samples. The model successfully recovers global $J$-band luminosity-function parameters and a mean luminosity density consistent with previous low-redshift measurements. The inferred luminosity-density scatter decreases with volume, consistent with the cosmic-variance amplitude expected in a $Λ$CDM universe. Within the volume we probe, we find no evidence for a large coherent underdensity. Instead, the local Universe is broadly consistent with smooth evolution and cosmic variance. The nearest shell at $z \simeq 0.01$ is the clearest exception, lying $\simeq 0.12$~dex ($\simeq 25\%$) below the smooth global model, a $-2.6σ$ underdensity with a one-sided significance of $\simeq 99.5\%$. This framework offers a scalable architecture for mapping cosmic density fields with next-generation wide and deep surveys.

astro-ph.CO↗
arXiv2026

Euclid Quick Data Release (Q1). The first Euclid view of Planck galaxy protocluster candidates at cosmic noon

[ABRIGED ABSTRACT] A large catalogue of candidate galaxy protoclusters with high star-formation rates was produced by the Planck collaboration. We search, in the first data release (Q1) of the Euclid survey, for the visible and infrared counterparts of the Planck galaxy protocluster candidates expected to be above $z > 1.5$. Eight of them are in Euclid Q1. Our goal is to investigate the optical nature of these overdensities previously detected in the submillimetre wavelength range. We search for overdensities using the DETECTIFz algorithm, an overdensity finder based on Delaunay tessellation that uses photometric redshift probability distributions through Monte Carlo simulations. Focusing our search on the eight high-star forming Planck protocluster candidates, we find that two of them have one Euclid counterpart, and six have between two and four Euclid counterparts, which amounts to a total of 20 Euclid counterparts. These Euclid counterparts lie at photometric redshifts $1.4<z_{\rm ph} < 2.7$ and 12 of them also have partial Herschel coverage. All detections have also been confirmed by at least one other independent protocluster detection algorithm. We study the colours, derived stellar masses and star-formation rates (SFRs) of the detected member galaxies of those protocluster candidate counterparts. We also estimate the total stellar masses, SFRs, and the halo mass lower limits for all Euclid protocluster candidates. We find that in the dark matter halo mass ($M_{\rm h}$) / redshift plane, these Planck and Euclid overdense regions lie in the region $12.6 <\log_{10} (M_{\rm h}/M_\odot)< 13.4$, $1.4<z< 2.7$. This means that the halos of our objects are expected to have experienced a transition between cold flows in hot media to accretion of hot material.

astro-ph.CO↗
arXiv2026

CNN+FoF: application of deep learning to the identification of dark matter haloes

We present a deep-learning-based approach for identifying dark matter haloes in cosmological N-body simulations. Our framework consists of a volumetric Convolutional Neural Network to classify individual simulation particles as either halo or non-halo members, followed by a highly optimised and parallelised Friends-of-Friends clustering algorithm that groups the classified halo members into distinct haloes. The training data comprise simulations generated using GADGET-4, with labels obtained with the ROCKSTAR halo finder. Our models incorporate two main halo mass definitions, $M_{200\mathrm{b}}$ and $M_{\text{vir}}$, with similar performance. For haloes defined by the ROCKSTAR $M_{200\mathrm{b}}$ criterion, the classification network demonstrated stable performance across multiple simulation resolutions. For the highest resolution, it achieved over $98\%$ across all primary performance metrics when identifying halo particles. Furthermore, the FoF algorithm yielded halo catalogues with a purity generally exceeding $99\%$ and a stable completeness of $94\%$ for masses above $5\times10^{11} \, M_\odot$. Our pipeline recovered the centre-of-mass positions, velocities and halo masses with high fidelity, yielding a halo mass function consistent to within $5\%$ of the reference while faithfully reconstructing the internal density profiles. The primary objective of this study is to offer a faster and scalable alternative to conventional halo finders, achieving a speed-up of approximately one order of magnitude relative to ROCKSTAR. While traditional N-body simulations remain dominated by the time required to solve gravitational dynamics, this post-processing acceleration becomes essential when coupled with fast field-level emulators, offering a promising pathway towards efficient, end-to-end forward modelling in modern simulation-based inference.

astro-ph.CO↗