Dingo-Pop uses a transformer to perform amortized, end-to-end population inference from GW strain data in seconds, bypassing per-event Monte Carlo sampling.
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Spectral-siren H0 constraints from GWTC-4.0 binary black holes remain robust when the mass spectrum is permitted to evolve with redshift at current detector sensitivity.
DALI extends the Fisher Matrix to higher orders and approximates MCMC posteriors for GW events at 55 times lower cost, with singlet-DALI offering better accuracy and the GWDALI code providing automatic differentiation and modern waveforms.
Gaussian Process Regression on mock GW siren catalogues reconstructs comoving distance and derivatives, showing that derivative diagnostics at specific redshifts best separate cosmological models while background data alone does not.
citing papers explorer
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End-to-End Population Inference from Gravitational-Wave Strain using Transformers
Dingo-Pop uses a transformer to perform amortized, end-to-end population inference from GW strain data in seconds, bypassing per-event Monte Carlo sampling.
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Gravitational-wave constraints on $H_0$ are robust to (putative) redshift evolution in the binary black hole mass spectrum at current sensitivity
Spectral-siren H0 constraints from GWTC-4.0 binary black holes remain robust when the mass spectrum is permitted to evolve with redshift at current detector sensitivity.
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On the use of the Derivative Approximation for Likelihoods for Gravitational Wave Inference
DALI extends the Fisher Matrix to higher orders and approximates MCMC posteriors for GW events at 55 times lower cost, with singlet-DALI offering better accuracy and the GWDALI code providing automatic differentiation and modern waveforms.
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Gaussian Process Reconstruction of Cosmological Parameters with Gravitational Wave Sirens using Machine Learning
Gaussian Process Regression on mock GW siren catalogues reconstructs comoving distance and derivatives, showing that derivative diagnostics at specific redshifts best separate cosmological models while background data alone does not.