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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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
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
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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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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.