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Loading contentHow astronomy keeps up with the flood of survey data — the machine-learning methods that classify and discover at scale, the applications where they meet the sky, the brokers that triage the alert stream in real time, and the data engineering that keeps it honest. Built on real methods, brokers, and benchmark datasets; nothing is fabricated.
Assigning objects to discrete categories from their measured features — star or galaxy, supernova type, real detection or artefact. It is the workhorse of survey astronomy, sorting the millions of objects a modern survey finds into kinds a human never could review one by one.
Classifying the flood of alerts that a survey like Rubin issues — millions each night when something on the sky changes — quickly enough to catch the fleeting events worth following up. It is the problem the community alert brokers exist to solve.
A community alert broker, led from Chile, that ingests a survey's alert stream and classifies its transients and variable objects in real time with machine learning — first for the Zwicky Transient Facility and, in the era ahead, for Rubin.
Standard, shared datasets on which different methods are compared on equal footing — like the Galaxy Zoo morphology labels or the PLAsTiCC transient-classification challenge. They let the field measure real progress rather than incomparable claims.