Radio Sirens

Radio Sirens: Dataset Description and Reproducibility Guide This dataset accompanies the paper "Radio Sirens: inferring $H_0$ with binary black holes and neutral hydrogen in the era of the Einstein Telescope and the SKA Observatory" and, together with the corresponding GitHub repository, provides all the resources required to reproduce the analyses presented in the study. In the following, we provide a description of the datasets and instructions for reproducing the paper's results. The full analysis pipeline consists of: Constructing density maps from dark matter halo catalogs. Simulating gravitational wave (GW) signals from a BBH population. Computing Fisher-matrix parameter uncertainties with GWFish (Dupletsa et al. 2023) for the BBH population Generating posterior samples with priors from the Fisher-matrix results. Computing GW selection effects using a large BBH injection set. Inferring cosmological parameters with icarogw (Mastrogiovanni et al. 2023). NOTE: All datasets are included in this release. The scripts used for data generation and processing are available in the accompanying GitHub repository. Data generation From dark matter halos to number density contrast maps The dark matter (DM) maps are built starting from the DM halo catalogs derived from the Demnuni set of simulations (Castorina et al. 2015, Carbone et al. 2016, Parimbelli et al. 2021). The simulation provides full sky cone data from redshift 0 to redshift 8, divided into snapshots as in the Millennium simulation (from snapshot 63 corresponding to z=0, till snapshot 1 corresponding to the highest redshift). In our analyses, we stop at snapshot 27 (corresponding to approximately redshift 3). The files in the dark_matter_halos/ folder contain, for each snapshot, the number of DM halos per HEALPix pixel. Each .txt file consists of a single column, where each row corresponds to the halo count in a given pixel. The number of rows equals the number of pixels for nside=128 (196,608 pixels). Files are named according to count_map_snapshot_<snapshot_number>_zmin<zmin>_zmax<zmax>_nside128.txt where: <snapshot_number> is the simulation snapshot number (27–63), <zmin> is the minimum redshift of the snapshot, <zmax> is the maximum redshift of the snapshot. The halo count maps are converted into HI density contrast maps. We provide the file hi_data/map4icaro_lmax48_zmin0.005_zmax3.025_nside16.hdf5 which is the exact HI map used in all analyses presented in the paper. The script used to generate this file is: prepare_los_file_for_icarogw.ipynb The resulting dataset stores $(1+\delta(z,\mathrm{pix}_i))$ for each redshift bin (rows) and sky pixel (columns), with $\delta$ the number density contrast. To avoid numerical issues associated with taking logarithms within icarogw (Mastrogiovanni et al. 2023), entries with exact zero values are replaced by a small positive value (1e-5). Binary black hole (BBH) population The simulated BBH populations are stored in: gw_data/bbh_events/ The release contains the following catalogs: Clustered GW sets: bbh_1e4_events_for_gwfish_rs1.hdf5 bbh_1e4_events_for_gwfish_rs10.hdf5 bbh_1e4_events_for_gwfish_rs42.hdf5 bbh_1e4_events_for_gwfish_rs110.hdf5 Isotropic GW sets: bbh_1e4_events_for_gwfish_rs1_NoHI.hdf5 bbh_1e4_events_for_gwfish_rs10_NoHI.hdf5 These datasets are generated using: create_gw_events.ipynb Injection set for selection effects A large injection set used to model GW selection effects is provided in: gw_data/bbh_1e7_injections_for_gwfish_5_300_Msol.hdf5 The injections are generated using: create_gw_injections.ipynb Only the signal-to-noise ratio (SNR) is computed for this injection set with GWFish (Dupletsa et al. 2023) using: run_gwfish_for_injections.py The resulting SNR file is: gw_data/bbh_1e7_injections_for_gwfish_SNR_results_5_300_Msol.hdf5 Data analysis Fisher matrix samples of BBH observations The clustered and isotropic GW catalogs are first analyzed with GWFish (Dupletsa et al. 2023) and subsequently post-processed with priors as described in Dupletsa et al. 2025. The Fisher-matrix analysis is performed using: run_gwfish_for_events.py Inputs gw_data/bbh_events/*.hdf5 gw_data/my_detectors.yaml gw_data/psds/ET_10_full_cryo_psd.txt Outputs gw_data/gwfish_results_raw/ This folder contains: Fisher matrices, covariance matrices, 1-$\sigma$ parameter uncertainties. The naming convention follows that of the corresponding injected GW catalogs. Posterior samples (PEs) generation The Fisher results are post-processed by sampling from a truncated multivariate Gaussian distribution with priors as described in Dupletsa et al. 2025. The post-processing is performed using: get_samples_from_gwfish.py The resulting posterior samples are stored in: gw_data/gwfish_results_resampled/ A note on additional GWFish input files GWFish requires detector specifications, which are provided in: gw_data/ Specifically: my_detectors.yaml contains the detector configuration, while psds/ET_10_full_cryo_psd.txt contains the detector sensitivity curve. For more information on running GWFish, see the official tutorials: General GWFish tutorial Priors post-processing tutorial $H_0$ inference and results The cosmological parameter estimation is performed using: run_icarogw_for_cosmoPEs.py Inputs hi_data/map4icaro_lmax48_zmin0.005_zmax3.025_nside16.hdf5 gw_data/gwfish_results_resampled/ gw_data/bbh_1e7_injections_for_gwfish_SNR_results_5_300_Msol.hdf5 Outputs results/ Each subdirectory contains: label_result.json containing posterior samples for the cosmological parameters, and label_corner.png containing the corresponding corner plot. For more details on icarogw, see the official repository and tutorials. Workflow summary The relationship between the provided datasets and the scripts used to generate and process them is summarized below. Radio-Sirens Pipeline: Maps generation (dark_matter_halos/) prepare_los_file_for_icarogw.ipynb hi_data/map4icaro*.hdf5 GW events branch create_gw_events.ipynb BBH event catalogs run_gwfish_for_events.py gwfish_results_raw get_samples_from_gwfish.py gwfish_results_resampled GW selection effects branch create_gw_injections.ipynb injection catalog run_gwfish_for_injections.py SNR results Cosmology inference run_icarogw_for_cosmoPEs.py results/ posteriors of cosmological parameters