Astronomers have catalogued billions of stars and hundreds of billions of galaxies. We have found thousands of planets orbiting stars. We can also map where these galaxies sit and trace how the universe changes over time.
The universe also contains objects that are too faint, too far away, or too crowded to study easily. Simply taking a picture of a galaxy tells us only parts of its story.
When we look at breathtaking pictures of the universe, we rarely see the hidden limits of the machine that built them. Even the best telescopes have mechanical blind spots that can quietly miss key galaxies. If we ignore these missed targets, they can affect our understanding of how galaxies and other cosmic objects evolve over time.
Our new paper, published in the Astronomical Journal, presents a computer tool to show how clever instrument design can solve these hardware limitations. It models how tiny telescope robots work so we can catch missed targets to map the universe accurately.
Thousands of tiny robots
Modern astronomy is not just about individual stars and galaxies.
Instead, scientists design massive surveys to scan large areas of the sky multiple times. The bigger these surveys are, the easier it is to learn how stars form, how galaxies grow, and how the universe changes over time.
Modern cosmic survey telescopes use thousands of tiny robots to position optical fibres to collect light from distant stars and galaxies. Future telescopes plan to use over 20,000 or more of these at once.
Each robot’s optical fibre directs the light from a distant star or galaxy to a tool called a multi-object spectrograph. This splits the light to show astronomers what the galaxy is made of and how far away it is.
The tiny robots can do a great job, but they have physical limits. They can bump into each other. They can’t get too close to each other without crashing. In crowded parts of the sky where galaxies sit close together in dense clusters, the robots can run out of room.
As a result, the system skips some galaxies to avoid crashes. These missing galaxies can create blind spots. If we use these biased data our maps can give us misleading answers about the universe.
Finding the blind spots
To keep the robots safe, engineers set strict rules such as the space between robot bases (called pitch), how far each robot can move around its base (called patrol radius), and the safety gap two robots must keep so that they do not touch (called exclusion radius).
To understand how these constraints introduce blind spots, our new study introduces a proof-of-concept computer tool which acts as a testing ground before a telescope turns on.
Our software can test many combinations of robot systems and astronomical targets. It acts like a flight simulator for telescopes. It mimics the process of moving a robot to position its optical fibre to many targets over repeated visits to the sky.
This tracks how well the system assigns fibres to targets, called allocation efficiency. It also tracks how many total galaxies get observed over time, called survey completeness.
In our study, we ran tests using specifications from real telescopes. Our results showed three key rules.
First, setting the smallest pitch, or using the highest fibre density, gives the best completeness and efficiency.
Second, when the pitch is fixed, increasing how far a robot can reach is the next best way to improve fibre assignment.
Third, making the safety gap smaller also helps stop missed targets, but it has a smaller effect than changing the reach of the robot.
Our tool bridges the gap between science goals and machine limits. By testing these rules early, astronomers can identify and address technology limits in designing new instruments. Engineers can use our code to test new ideas for future instruments before spending millions of dollars to build them.
There is still much of the universe that we haven’t mapped yet. Future maps of the universe will depend on more than just giant telescope mirrors. They will also need smart computer tools that improve how much telescopes can actually see.
Sometimes, mapping the whole universe starts with getting thousands of tiny robots to move in exactly the right way.
- is an Honorary Postdoctoral Associate, Macquarie University
- This article first appeared in The Conversation
