We are living in the era of “big data” in astronomy — a time when telescopes don’t just capture stars, they collect staggering volumes of data every night. This transformation is driven by advancements in observational technology, which now allow scientists to monitor more of the universe with greater precision than ever before.

One of the most ambitious projects in this space is the Legacy Survey of Space and Time (LSST) — a 10-year initiative that aims to study everything from the birth of galaxies to the mysterious force known as dark energy.

A Telescope That Sees the Whole Sky

“The LSST is expected to observe billions of objects — basically, it will scan the entire southern sky,” says Grant Merz, an astronomy graduate student at the University of Illinois Urbana-Champaign. “It’s an unprecedented amount of optical imaging data.”

And he’s not exaggerating. The LSST will generate more than 20 terabytes of data every single night — roughly equivalent to streaming 6,000 high-definition movies. This is a goldmine for scientific discovery, but also a logistical nightmare.

The Human Problem: Too Much Data, Too Little Time

As exciting as this explosion of data is, the sheer volume poses a huge challenge.

Imagine trying to find a needle in a cosmic haystack. “It’s like running a daycare where you’re used to looking after one kid, and suddenly there are 100,” says Amanda Wasserman, another U of I astronomy graduate student. “You’d need a robot to help.”

And that’s where artificial intelligence comes in.

Meet the AI That Picks the Stars Worth Studying

To manage the overwhelming data influx, astronomers are turning to AI tools that can sort, prioritize, and highlight important information. Wasserman works on an AI system called RESSPECT — short for Recommendation System for Spectroscopic Follow-up.

RESSPECT helps identify which supernovae are worth further investigation. By highlighting the most promising candidates for spectroscopy (a technique that reveals the elements within a supernova), it allows researchers to spend their time where it matters most.

Spotify for the Stars: Finding Anomalies with LAISS

Another exciting tool in the astronomer’s AI toolkit is LAISS — Lightcurve Anomaly Identification and Similarity Search. Inspired by the algorithm used by Spotify to recommend music, LAISS compares lightcurve data (which tracks changes in brightness of celestial objects) to find patterns and outliers.

But unlike Spotify, astronomers don’t just want more of the same. They’re also looking for anomalies — the cosmic oddities that break the pattern and could lead to entirely new discoveries.

“Even just deciding whether something is a supernova or not can take hours of manual analysis,” Wasserman explains. “AI tools like LAISS can dramatically speed up that process.”

AI Doesn’t Just Save Time — It Frees the Mind

By delegating the time-consuming task of data sifting to AI, astronomers can focus more on high-level analysis and interpretation.

“It’s not just about saving time,” says Wasserman. “It’s about shifting our energy from filtering data to asking deeper questions about the universe.”

Watching the Carbon Cost of Cosmic Curiosity

Of course, using AI at this scale does raise environmental concerns. Machine learning models consume a lot of energy, and as AI becomes more central to research, its carbon footprint is coming under scrutiny.

Merz notes that astronomers are being conscientious about this. “In many scientific papers, researchers include carbon usage estimates. As a community, we’re aware of the environmental impact and are actively working to reduce it.”

Astronomy is no longer about stargazing through a telescope for hours on end. It’s about teaching machines to help us navigate oceans of data, unlocking secrets of the universe faster than ever before.

With the LSST and AI working in tandem, a new era of cosmic discovery is here — and this time, the stars are talking back.