Searcharxiv⌕ Search

arXiv · 2610.14781

TDCOSMO 2026: A 3% measurement of the Hubble constant from strong lensing and stellar kinematics

Abstract

We present cosmological constraints from 11 strongly lensed quasars with measured time delays (hereafter, the TDCOSMO-2026 sample). In addition to expanding the sample, we improve many aspects of our blind analysis with respect to our 2025 analysis. Among other improvements, spatially resolved kinematics are available for nine systems, including six with JWST-NIRSpec, compared to one in 2025; for three lens models, JWST-NIRCam images now replace HST; axisymmetric dynamical modeling is applied directly to the spatially resolved data, instead of an axisymmetric correction factor applied to spherical models; and the line-of-sight convergence is now based on the Euclid Flagship simulations instead of the Millennium Simulation. Our baseline and primary result is based on the TDCOSMO-2026 sample, in combination with a prior on $Ω_{\rm m}$ from the DES-Dovekie supernova sample. In the flat $Λ$ cold dark matter ($Λ$CDM) cosmology, we find $H_0=71.1^{+2.4}_{-2.3}$ km.s$^{-1}$.Mpc$^{-1}$, a 3.3% uncertainty that accounts for the mass-sheet degeneracy. The SLACS and SL2S samples are in excellent agreement with the TDCOSMO-2026 sample, improving the precision on $H_0$ in flat $Λ$CDM to 3.0%. Using the DESI-DR2 baryon acoustic oscillations (BAO) likelihood instead of DES-Dovekie yields fully consistent results. We also present constraints in the open $Λ$CDM, $w$CDM, $w_0w_a$CDM, and $w_ϕ$CDM cosmologies; the TDCOSMO $H_0$ inference is robust and consistent across all of them. The combination of the TDCOSMO+SLACS+SL2S likelihood with those from DESI-DR2 and DES-Dovekie disfavors $w=-1$ at the $2.3$-$2.5σ$ level in all three dark energy extensions that we tested. We find, e.g., $w_0=-0.88^{+0.04}_{-0.05}$ in $w_ϕ$CDM and $w_0 = -0.85^{+0.07}_{-0.06}$ in $w_0w_a$CDM, while $w_a = -0.48^{+0.46}_{-0.44}$ remains consistent with zero.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

TDCOSMO Collaboration, Simon Birrer, Michele Cappellari, Frédéric Courbin, Christopher D. Fassnacht, Pierre Fleury, Joshua A. Frieman, Aymeric Galan, Xiang-Yu Huang, Daniel P. Johnson, Sanjana Khanolkar, Shawn Knabel, Hrvoje Krizic, Martin Millon, Takahiro Morishita, Veronica Motta, Pritom Mozumdar, Eric Paic, Hadrien Paugnat, Allan G. Schweinfurth, Anowar J. Shajib, William Sheu, Dominique Sluse, Massimo Stiavelli, Sherry H. Suyu, Tommaso Treu, Felipe Urcelay, Devon M. Williams, Kenneth C. Wong. 2026-10-10. TDCOSMO 2026: A 3% measurement of the Hubble constant from strong lensing and stellar kinematics. https://arxiv.org/abs/2610.14781

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

KEEP EXPLORING

Related papers

Dark Energy Survey Year 6 Results: improved mitigation of spatially varying observational systematics with masking for the MagLim++ lens sample

As photometric surveys reach unprecedented statistical precision, systematic uncertainties increasingly dominate large-scale structure probes relying on galaxy number density. Defining the final survey footprint is critical, as it excludes regions affected by artefacts or suboptimal observing conditions. For galaxy clustering, spatially varying observational systematics, such as seeing, are a leading source of bias. Template maps of contaminants are used to derive spatially dependent corrections, but extreme values may fall outside the applicability range of mitigation methods, compromising correction reliability. The complexity and accuracy of systematics modelling depend on footprint conservativeness, with aggressive masking enabling simpler, robust mitigation. We present a unified approach to define the DES Y6 JOINT footprint, integrating observational systematics templates and artefact indicators that degrade mitigation performance. This removes extreme values from an initial SEED footprint, leading to the final JOINT footprint. By evaluating the DES Y6 lens sample MagLim++ on this footprint, we enhance the Iterative Systematics Decontamination (ISD) method, detecting non-linear systematic contamination and improving correction accuracy. While the mask's impact on clustering is less significant than systematics decontamination, it remains non-negligible, comparable to statistical uncertainties in certain w(theta) scales and redshift bins. Supporting coherent analyses of galaxy clustering and cosmic shear, the final footprint spans 4,031.04 deg2, setting the basis for DES Y6 1x2pt, 2x2pt, and 3x2pt analyses. This work highlights how targeted masking strategies optimise the balance between statistical power and systematic control in Stage-III and -IV surveys.

astro-ph.CO↗

Probing scalar-tensor-induced gravitational waves in the nHz band: NANOGrav and SKA

Scalar-induced gravitational waves (SIGWs) have recently attracted considerable interest, both as a possible explanation for the nanohertz signal reported by the Pulsar Timing Array (PTA) collaboration and for their connection with primordial black hole (PBH) physics. In addition to SIGWs, scalar-tensor-induced gravitational waves (STGWs) have emerged as a promising cosmological source of the stochastic gravitational wave background (SGWB). In this paper, we compute the STGWs generated during a generic matter-dominated (MD) era, as well as during an early matter-dominated (eMD) epoch followed by a sudden transition to the standard radiation-dominated (RD) stage, working in the Poisson gauge. We find that, in a purely MD age, the corresponding energy density rapidly dilutes, whereas in the presence of an eMD phase it remains non-vanishing due to the short duration of the eMD period. We then investigate whether the STGW signal could provide a dominant contribution to the \texttt{NANOGrav 15-year} dataset and we forecast the prospects for its detection with future observations by the Square Kilometre Array (SKA). In particular, we consider STGWs generated during both eMD and RD eras, including their linear-order contributions. We further inject the scalar-induced contribution, both as an additional component and as the only signal, and compare the spectra against the same datasets. Our results show that, although the SIGWs still appear to provide the dominant contribution and the posteriors are tightly constrained by the requirement of avoiding PBH overproduction, the GWs induced by scalar-tensor mixing constitute a viable target for future, more sensitive nano-Hz detectors of the SGWB.

astro-ph.CO↗

Cosmo-PINN: A Physics-Informed Neural Network for Cosmological Reconstruction

We introduce Cosmo-PINN, a Physics-Informed Neural Network for reconstruction of the cosmological theory. In this work we demonstrate the application of the Cosmo-PINN in the reconstruction of the dark energy equation of state parameter $w_{DE}\left( z\right) $ directly from late-time cosmological observations. This framework overcomes the main limitation shared by Gaussian Process and Artificial Neural Network reconstruction approaches, where the recovered solution is driven by the data and it is not necessarily true that it is physically consistent, by embedding the cosmological constraints directly into the loss function as constraints, ensuring that the reconstructed quantities satisfy the physical laws at every point during the training. For the training of the network, we employed background data, and specifically the Baryon Acoustic Oscillation from DESI DR2, the Cosmic Chronometers and three different Supernova compilations, while we simultaneously introduce the cosmological parameters $% H_{0},~Ω_{m0}$ and $r_{\mathrm{drag}}$ as trained parameters. The reconstruction shows that the trained $w_{DE}\left( z\right) $ crosses the phantom divide around $z=0.5$ in agreement with the value obtained by the Chevallier-Polarski-Linder model. In the quintessence scenario, the dark energy equation of state parameter is forced to the $Λ$CDM limit in the past, that is $w_{DE}\left( z\right) \rightarrow -1 $. Finally, we demonstrate the significance of imposing the physical constraints within the loss function by comparing the Cosmo-PINN reconstruction against a purely data-driven neural network with the same architecture.

astro-ph.CO↗