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.
A fast deep-learning approach to probing primordial black hole populations in gravitational wave events
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abstract
Primordial black holes (PBHs), envisioned as a compelling dark matter candidate and a window onto early-Universe physics, may contribute to some of the gravitational-wave (GW) signals detected by the LIGO-Virgo-KAGRA network. Traditional hierarchical Bayesian analysis, which relies on precise GW-event posterior estimates to extract information on potential PBH populations from GW events, becomes computationally demanding for catalogs with a large number of events. Here, we present a fast deep-learning framework, leveraging Transformer and normalizing flows, that maps GW-event posterior samples to joint posterior distributions over the hyperparameters of the PBH population. Our approach yields credible intervals with acceptable accuracy while delivering an order-of-magnitude speedup. These results highlight the potential of deep learning for fast and accurate PBH population studies, and its applicability to next-generation GW detectors when combined with appropriate event-level inference models.
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Demonstrates direct comparison of observable compact-binary populations from GW data to astrophysical models, with unbiased inference shown possible and applied to O3 data.
Simulations show a 40-50 solar-mass black-hole cutoff is not guaranteed to be confidently recovered from GWTC-4-like catalogs, spurious detections are unlikely, and O4 data would reduce cutoff-mass uncertainty by at least 20 percent while yielding only a lower bound on the carbon-alpha reaction rate
MCMC comparison of projected PBH correlation functions with z≈6 quasar angular auto-correlation data yields posterior constraints f_PBH∼10^{-3}, m_PBH∼10^{12}M_⊙ for Poisson models and ξ_eff≃2.1, r_cl≃76 Mpc for clustered models.
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
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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Comparing astrophysical models to gravitational-wave data in the observable space
Demonstrates direct comparison of observable compact-binary populations from GW data to astrophysical models, with unbiased inference shown possible and applied to O3 data.
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Measurement prospects for the pair-instability mass cutoff with gravitational waves
Simulations show a 40-50 solar-mass black-hole cutoff is not guaranteed to be confidently recovered from GWTC-4-like catalogs, spurious detections are unlikely, and O4 data would reduce cutoff-mass uncertainty by at least 20 percent while yielding only a lower bound on the carbon-alpha reaction rate
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Constraining supermassive primordial black hole clustering with the angular auto-correlation of $z\simeq 6$ quasars
MCMC comparison of projected PBH correlation functions with z≈6 quasar angular auto-correlation data yields posterior constraints f_PBH∼10^{-3}, m_PBH∼10^{12}M_⊙ for Poisson models and ξ_eff≃2.1, r_cl≃76 Mpc for clustered models.
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Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.