top of page
Search

How is Machine Learning Shaping Modern Astronomy?

Machine learning, or ML, is revolutionizing the field of astronomy, allowing scientists to analyze vast amounts of data and make new discoveries at an unprecedented rate. With the increase of telescopes and other instruments that can collect data about the universe, there is a need for efficient and effective ways to process this information. The graph below shows drastic increases in astronomical data intake in recent years from the most popular instruments.

ML algorithms can help by automatically identifying patterns and trends in the data, allowing astronomers to focus on the most promising and intensive areas of study.

One of the key ways that machine learning is being used in astronomy is to classify objects in the universe. For example, algorithms can be trained to identify different types of stars, galaxies, comets, or asteroids based on their characteristics. This helps astronomers to understand the properties and evolution of these objects as well as to search for rare or unusual objects that might not be easily recognizable by humans. The image below shows how an astronomer classifies different galaxies in order to build a classifier.

Another important application of machine learning in astronomy is the detection and analysis of exoplanets. These are planets that orbit stars other than the Sun, and they can be difficult to detect due to their small size and faintness. ML algorithms can help to identify exoplanets by analyzing data from telescopes and other instruments, looking for small variations in the light emitted by a star that could indicate the presence of a planet. The process of identifying an exoplanet seems simple.For instance, when a dip in brightness of a star is noticed then a planet has passed in front of it, right? In actual

lity, dips in a star’s brightness may be caused by a multitude of reasons and it would take an astronomer manually looking at each star’s time-series to identify if the change is in fact caused by a planet. An example of

this can be seen in the image below. The exoplanet passing in front of its star has a slightly different pattern than a sunspot for instance, but an astronomer would be able to recognize the difference.

However, with ML astronomers can train datasets to classify exoplanet dips based on known exoplanet detections which eliminates the need to then go through each time-series manually.

Machine Learning is not one-size-fits-all for astronomy. For instance, in the search for exoplanets an astronomer could train a model using a time series of a star’s brightness known as light-curves as talked about previously or images of exoplanets around stars that have been directly observed. In the case of direct imaging, it is mostly used for dimmer stars that are close to Earth, have exoplanets in a large orbit around them, and the planets do not cross in front of (transit) their star. Telescopes can capture longer exposures since the stars are dim, which in turn brings out the faint exoplanets surrounding them in images as shown in the image below on the left. On the other hand, light-curves are used to detect exoplanets that are far away from Earth, have short orbits, and transit their star. The light-curve shows periodic dimming of a star indicating a transit as seen in the image below on the right.


The same can be said for supernovae and solar flares. Both are measured either by direct imagingor light-curves. The same ideas for exoplanet detection as mentioned above apply but the content varies slightly. Direct imaging supernovae (SN) and solar flares apply to SN that are far away but extremely brightor dimmer SN that are closer to Earth. The image below is a great example of an extremely bright supernova that was directly imaged in a galaxy roughly 350 million light years away. In contrast, light curves can be applied to any SN within a certain change-in-brightness threshold as determined by the scientific equipment. Naturally, the light-curves would show a single positive change in brightness as opposed to a periodic negative change in brightness shown in exoplanet transit curves. All in all, applications for ML are widely varied for any celestial object and the best path is not straightforward

The question then becomes which training method is the right balance between accurate, efficient, and light weight. Let’s say we’re looking at training a model to recognize solar flares. Given the importance of being able to forecast when a solar flare will happen and how intense it will be, the model designed will want to prioritize accuracy over anything else. Even if that means compromising model run-time or CPU/GPU usage required. On the other hand, for something like identifying galaxies, a highly efficient model would be used since it would have to parse through hundreds of thousands of images.

Overall, machine learning is an essential tool for modern astronomy, enabling scientists to make new discoveries and advance our understanding of the universe. As data collection technologies continue to improve, the role of ML in astronomy will only become more important, and we can look forward to many exciting new discoveries in the future.