AI in Telescope and Astronomical Image Analysis Quiz

Answers and explanations appear as you take the quiz.

1. What does 'real-bogus' classification in astronomy refer to?
   • Distinguishing genuine astronomical sources from artifacts like cosmic rays and satellite trails
   • Deciding whether a research paper is fraudulent
   • Telling real stars from planets
   • Checking if a telescope is calibrated
2. At the June 30, 2026 start of the Rubin survey, what nightly data volume did the Observatory report collecting?
   • A few gigabytes
   • Roughly 10 terabytes
   • About one petabyte
   • A few megabytes
3. How does Rubin Observatory detect many changes in the sky?
   • By measuring the radio spectrum of every star
   • By waiting for human reports
   • By comparing new images with earlier images
   • By changing the telescope mirror coating
4. What does a photometric redshift estimate?
   • The mass of a black hole
   • The brightness of the Moon
   • A telescope's focus error
   • A galaxy's distance from its broadband colors
5. Why is training data for transient detection typically imbalanced?
   • Genuine transients are rare compared to artifacts and static sources
   • Telescopes only work at night
   • Galaxies all look identical
   • There is too little storage
6. What is a strong gravitational lens that AI helps detect?
   • A defective camera lens
   • A foreground mass bending light from a background object
   • A type of supernova
   • A reflective telescope coating
7. What is the role of an astronomical alert broker in the Rubin ecosystem?
   • Operate the telescope mirrors
   • Replace all follow-up observations
   • Ingest alerts, add context, and help filter or prioritize them for research
   • Generate synthetic galaxies for training only
8. What nightly alert count did Rubin report during early LSST operations?
   • About 70 alerts
   • Around seven billion per minute
   • Exactly 100 alerts
   • Up to about seven million alerts