Scalable Bayesian data curation for next-generation radio experiments
Next-generation radio telescopes produce data volumes that preclude manual quality assessment, yet data curation remains essential for science. We present a general, fully automatic Bayesian anomaly-detection method for radio science experiments in which data curation is performed inside the inference: a latent anomaly indicator is marginalised in the likelihood rather than converted into an external pre-flag. Implemented in JAX with GPU-accelerated inference, the pipeline assigns probabilistic data-curation scores without prior knowledge and requires no thresholds, manual inspection, or subjective decisions. We demonstrate the method on the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH), applying it to 4655 observations (one year of REACH data). The pipeline assigns scores across time and frequency, enabling identification of the optimal observations to carry forward into scientific inference while reducing the risk that contaminated data bias the result. In doing so, it simultaneously recovers weather-driven systematics, instrument-component drifts, and narrow-band radio-frequency interference, while revealing complex dependencies between data quality and environmental or instrumental state that would be difficult to uncover by manual curation alone. This turns data curation from an external manual bottleneck into autonomous, inference-level infrastructure for the Square Kilometre Array era.