Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
T., Mishra-Sharma, S., & Villar, V
2 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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BOOM is a new high-throughput alert broker using Rust, MongoDB, Valkey and Kafka that matches prior ZTF features at ~7x speed and is extended as Babamul for LSST's 20 million nightly alerts.
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Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
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BOOM and Babamul: a real-time, multi-survey, optical alert broker system operating at scale
BOOM is a new high-throughput alert broker using Rust, MongoDB, Valkey and Kafka that matches prior ZTF features at ~7x speed and is extended as Babamul for LSST's 20 million nightly alerts.