Pushing spectral siren cosmology into the third-generation era: A blinded mock data challenge

Gravitational wave (GW) spectral sirens offer a promising method for measuring cosmological parameters using GW data only --- without relying on external redshift information such as electromagnetic counterparts or galaxy catalogs --- by exploiting distributional features in the population of GW sources. The advent of third-generation detectors such as the Einstein Telescope (ET) will provide catalogs three orders of magnitude larger than current ones, raising questions about the scalability and robustness of existing inference pipelines. We present a blinded mock data challenge that tests three public pipelines with distinct numerical implementations, namely (10^4) binary black hole mergers that can be observed in 1 year. We assessed their computational performance, validated their agreement in a blinded setting, and forecast cosmological constraints. We find that, thanks to GPU acceleration, these pipelines can process the events expected from ET within a manageable timeframe. All pipelines recover consistent cosmological and population parameters. Assuming a flat Lambda cold dark matter (ΛCDM) model, we measure H(z) at z . We also identified the events that contribute mostly to constraining cosmological parameters, and show that low-distance sources near population features drive the constraining power on all cosmological parameters, while higher-distance events primarily constrain Ω_̊m m,0. Our results establish a validated, performance-tested framework for spectral siren cosmology in the era of third-generation GW observatories. ̧himera, and on simulated ET observations containing the best mathcal O with 2.4% precision, and achieve a mean precision on H(z) of 2.8% across 0.7<z< 1.8 with a catalog of ∼ 12,000 high S/N events. This corresponds to joint constraints of ∼ 10% on H_0 and ∼ 26% on Ω_ m ,0