AI Transforming Our Understanding Of The Universe

The alerts provide information on a new astronomical object, such as its likelihood of being real, its type, the galaxy it belongs to and how its brightness has changed over time. With this data, astronomers are able to select the best candidates for follow-up research.

However, even with the efforts of the software teams and brokers, there is still too much transient data for any research team to sift through. The final stage of data processing from the Rubin telescope will involve scientists using machine learning and AI techniques to identify the best data.

These techniques may be for identifying real cosmic objects among the terabytes of false alerts received, or for classifying the ones most interesting to scientists.

Astronomy is increasingly code-heavy and focused on in-house development. Given the huge amounts of data generated with every night of telescope observations, it is, unsurprisingly, one of the first sciences to turn to machine learning as a solution.

LSST's Informatics and Statistics Science Collaboration (ISSC), for example, is a group of over 150 data scientists who work on developing tools for astronomy, focusing on the survey's data science goals.

Astronomy has led the charge in regard to big data, with funding provided by companies such as Amazon and Microsoft for a number of major projects. Indeed, the namesake of the 8.4-metre Simonyi Survey Telescope at the Rubin observatory, Charles Simonyi, is known for software development in the early days of Microsoft, as well as his philanthropic work.

The volume of data produced by the observatory will not only produce opportunities for scientists, software developers and tech workers, but also for volunteers with an interest in astronomy via citizen science projects.

LSST's partnership with the citizen science platform Zooniverse will ask volunteers to look through data and provide additional context to what they're shown – identifying interesting objects, discarding garbage data and classifying various types of phenomena.

What does the Rubin observatory tell us about modern astronomy? The 20th century saw a greater push for international collaboration in exploring the skies. The increased sophistication of the resulting observatories means that more and more astronomers are working in the service of enabling science, rather than making discoveries themselves.

The huge amounts of data generated by the survey, and the huge number of personnel required to analyse it, is not novel to Rubin. Other contemporary surveys such as Euclid and the Ligo-Virgo-Kagra collaboration, as well as the next decade's even larger Square Kilometer Array, each consist of thousands of collaborators worldwide leveraging huge amounts of data.