Meet the AAS Keynote Speakers: Dr Aaron Meisner

In this series of posts, we sit down with a few keynote speakers of the 245th AAS meeting to learn more about them and their research. You can see a full schedule of their talks here and read our other interviews here!


A star is born when a cloud of gas collapses and heats up enough for hydrogen fusion to ignite at the core, in which two hydrogen atoms fuse to form a helium atom and release energy. However, some gas clouds aren’t massive enough to reach this temperature, resulting in a ball of gas that will gradually cool down over its lifetime, known as a brown dwarf. Since brown dwarfs aren’t powered by fusion, they’re extremely dim, making them challenging to find, even within our own neighbourhood of the Milky Way. By mining wide area infrared sky surveys, Dr. Aaron Meisner is able to observe these elusive sub-stellar objects and answer key questions around star and planet formation. 

As an undergraduate at Stanford University, Dr. Meisner says that “what really drew [him] to astrophysics was the fact that it was very data rich science”. During his PhD at Harvard University, he worked on producing maps of interstellar dust, and although the topic of his research has since changed, a focus on data analysis has remained central to his research. He is currently staff astronomer at the National Science Foundation’s National Optical-Infrared Astronomy Research Laboratory (NOIRLab). At AAS 245 he will deliver a plenary lecture on the frontiers in the study of substellar objects, including “T-type sub dwarfs, the first Y-types of dwarf and probably the first hyper velocity low mass star”, as well as the “nexus forming between big data… and citizen science”.

Due to their low masses, brown dwarfs actually “overlap in mass and temperature with giant exoplanets”, offering astronomers like Dr. Meisner a unique opportunity to treat brown dwarfs as free-floating planets and study gas giants without “a bright host star contaminating all of their observations”. As a result, it’s much easier to study certain characteristics of gas giants, such as their water content, in brown dwarfs than in exoplanets. Brown dwarfs also offer significant insights into the process by which clouds of gas collapse to form stars, and will be crucial in determining whether there is a “low mass cutoff to the star formation process” and how many sub-stellar objects exist, relative to main-sequence stars.

The nature of brown dwarfs makes them challenging to observe and study. There is a “fundamental degeneracy between their mass, their age and their temperature because they’re cooling gradually over billions of years. So if you see a brown dwarf at a given temperature, it could be very young and very low mass, or it could be very old and many dozens of Jupiter masses. And so getting the fundamental parameters of brown dwarfs is a really difficult problem”, explained Dr. Meisner. To circumvent this problem, astronomers use a method known as benchmarking, which uses “a brown dwarf that’s a companion to some other object” with a known age. Assuming the two objects were formed at the same time, you can infer the age of the brown dwarf and break the degeneracy. 

Currently brown dwarfs can be found within the nearest 20 parsecs (around 65 light years). The next hundred parsecs may be illuminated with “upcoming next generation surveys like Rubin-LSST, Euclid, and Roman”. Previously, Dr. Meisner co-founded Backyard Worlds: Planet 9, a citizen science project that produced 10 million classifications. But citizen science projects will have to “scale up by…another order of magnitude…to meet the level of data deluge that’s going to come”. To meet this need, we need to “get artificial intelligence and participatory science to be synergistic in a way that’s not competitive and preserves the human element of discovery”.

While recruiting more volunteers to citizen science projects is definitely an important aspect of preparing for the era of big data, Dr. Meisner also wants to find ways to increase the impact that a single volunteer’s work. This might involve making “volunteers’ time more efficient by running a machine learning pre-selection and only showing select patches of the sky” or using volunteer classifications as “the basis for a training set to be used to improve the machine learning”. While these kinds of projects can be a great way to “give people new data literacy skills”, Dr. Meisner also noted the importance of being careful when communicating how astronomers use AI: “when we’re doing projects like Backyard Worlds, we try to avoid portraying it as a competition against the bots” and “as if we only need to get the classification data, and that’s going to allow us to train something that’s then going to replace all of the humans.”

To learn more about brown dwarfs, leveraging big data, and the next generation of citizen science projects be sure to attend Dr. Aaron Meisner’s Plenary Lecture: Revealing the Solar Neighborhood’s Diversity and the Milky Way’s Substellar Halo at 3:40 pm EST on Wednesday January 15th at #AAS245!


Edited by: Megan Masterson

Featured Image Credit: AAS

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