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.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Normalizing flows replace binned histograms for estimating multi-detector signal parameters in PyCBC, slashing storage by three orders of magnitude with under 0.05% sensitivity loss and up to 6.55% gains in specific cases.
A single MoE-based foundation model with transformer backbone unifies simulation, PID, and noise filtering for the GlueX DIRC detector and matches or exceeds traditional geometrical and prior deep-learning methods across kinematics.
AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.
citing papers explorer
-
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.