5 papers
NAPTIME: A Neural-Process Framework for Rubin Alert Classification
Nicholas Earl, Siddharth Chaini, K. Decker French +3
The Vera C. Rubin Observatory Legacy Survey of Space and Time will produce a high-volume stream of irregularly sampled multiband alerts for which spectroscopic confirmation will be…
Toward decision-aware AI for LSST-scale time-domain astronomy
C. R. Bom, A. Mahabal, F. Bianco +27
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will generate approximately (10^7) alerts per night, pushing time-domain astronomy beyond pipelines that trea…
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
Siddharth Chaini, Federica B. Bianco, Ashish Mahabal
Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves. On the eve of the Vera C. Rubin…
Searching for Ultracool Dwarfs in Early LSST Data Products
Easton J. Honaker, John E. Gizis, Christian Aganze +8
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) promises to drastically accelerate the discovery of ultracool dwarfs (UCDs) over the course of its 10-year su…
In Search of the Unknown Unknowns: A Multi-Metric Distance Ensemble for Out of Distribution Anomaly Detection in Astronomical Surveys
Siddharth Chaini, Federica B. Bianco, Ashish Mahabal
Distance-based methods involve the computation of distance values between features and are a well-established paradigm in machine learning. In anomaly detection, anomalies are iden…