Eric Douglas Published

WVU Professor On Search For Supernovas

two men dressed in casual clothing look at a computer screen
Timothy Faerber, a WVU graduate student, and Professor Loren Anderson study supernova remnants to further understand the properties and dynamics of the galaxy.
WVU Photo/Nathaniel Godwin

Their lifecycle is in millions of years, but stars in the Milky Way grow, produce heat and light and then they die. Some burn out in a spectacular supernova.

Loren Anderson, a professor at the Eberly College of Arts and Sciences at West Virginia University, is studying the remnants of those explosions to better “understand the properties and dynamics of our galaxy.” 

News Director Eric Douglas, an admitted science and astronomy geek himself, sat down with Anderson to learn more. 

This interview has been lightly edited for clarity. 

Douglas: What is a supernova? 

Anderson: So stars create energy, the process we know as fusion, where hydrogen atoms are combined into helium atoms, that’s the primary way. However, that process uses up the hydrogen in the stars. And eventually the stars run out. And at the end of their lifetimes, they kind of go on a frantic search for new ways to generate energy. But eventually those methods run out. And what happens is, during fusion, they kind of blow up, you know, the pressure from the generation of energy makes the star that’s present size. And then without that pressure, they collapse inwards and that collapse creates a rebound that leads to a supernova. 

Loren Anderson, professor, astronomy, WVU Eberly College of Arts and Sciences.

Credit/West Virginia University

Douglas: So they literally explode, implode, and then explode bigger again.

Anderson: Without that initial explosion, what you said is correct. It’s generating energy, it’s stable. It’s producing the heat and the light that we need, but it’s going to run out and skip some of the very fast evolutionary steps, then it goes. Our sun will go through a different evolutionary path, however, so it’s only the most massive stars that do that explosion.

Douglas: An interesting thing I saw in the description of your research is that we know of about 300 or 400 of these supernovas that have happened. But, statistically, there should be about 1000 of them. 

Anderson: Those numbers are only for our own galaxy — within the Milky Way. The supernova remnants, which after the explosion, there’s still some embers glowing, and we call those glowing embers, supernova remnants, but they only last a pretty short amount of time. 

A supernova will go off and then it will relatively quickly become undetectable. That’s what leads to the relatively low numbers. They’re kind of hard to find. The fact that there’s many multiples more that should be discoverable, comes from studies of other galaxies, and comes from studying the population of stars that are in our galaxy that should explode to produce these things.

Douglas: Roughly speaking, we know there are X number of stars in our galaxy, compared to a similar-sized galaxy. 

Anderson: That’s right. And just to be 100 percent clear, that’s not my research, that number comes from other people. That’s one method that is the strongest evidence for the number, but many people propose different methods and all of them arrive at the same answer that we’ve only found a fraction of what’s out there.

Douglas: Why is that important?

Anderson: It’s important just for understanding the type of galaxy that we live in. And so by mapping out all of these, we can learn about the massive star history of our galaxy over the last tens of thousands of years. We can put our galaxy in context of other galaxies in the universe. And also, there are a lot of interesting physics, none of which I do, but there’s a lot of interesting physics, of studying individual supernova remnants and understanding how that explosion progresses in time, and its interaction with the environment, all of that sort of stuff. Each one that we find is a new little laboratory for study.

Douglas: What’s the process for finding these remnant supernovas?

Anderson: It is surprisingly low tech. Essentially what I do is I bring up an image of a field of the galaxy, so a little part of the sky. And the data that I’m using for that are from the MEERkat telescope in South Africa, which is a 64 telescope array where all 64 telescopes work together to observe a patch of sky. It’s exceedingly powerful. We look at a little patch of the sky. And on that patch of the sky, we identify all the objects that are known to exist. And then I look for things that have a characteristic morphology of a supernova remnant, that are not known to exist yet.

No fancy algorithms. It’s just me and a computer monitor. 

The lower two of these shell-like features are supernova remnants, with SNR G1.0-0.1 on the left and SNR G0.9+0.1 on the right. The uppermost shell is the Sagittarius D HII region, a site of recent star formation. SNR G0.9+0.1 has a pulsar wind nebula at its center, showing a tangled complex of radio emission. Polar outflows from this nebula appear to be distorting the shell of the supernova, particularly towards the north.

Credit/South African Radio Astronomy Observatory (SARAO)

Douglas: Just to be clear, MEERkat is a radio telescope. How does it translate into a visual format? I’m not sure how that works.

Anderson:  Oh, right. So your standard radio telescope, like the Green Bank Telescope, takes an observation of one location at a time. If you want to make an image, like the pretty images from Hubble or JWST (James Webb Space Telescope), you have to move the telescope to make an observation at each pixel. But an array of telescopes like Meerkat or like the Very Large Array in New Mexico, you can get something just like what Hubble gets. What I’m working with are complete images of little patches of the sky.

Douglas: How long does it take to go through one of these files? 

Anderson: The full search, this particular data set that I was working with, covers more than 100 square degrees. And the full search took me probably on the order of four months, fairly dedicated work. That’s not to say that going through one time would not take much time, but there’s a lot of checks afterwards to make sure that something wasn’t discovered yet. 

And what I try to do is do the search, and then wait a little while and then do the search again to make sure that what I’m finding is actually able to be repeated. That’s one thing that is not a strength of the eye method in that it’s very dependent on my brain, which is not the same as your brain or anyone else’s. And so what I find could be different from what another researcher finds. Repeatability is important in science and so I do what I can to maximize repeatability.

Douglas: How many galaxies are there? Do we know a rough estimate of how many galaxies there are?

Anderson: We have very rough estimates and it’s certainly more than 100 billion. 

Douglas: One hundred billion galaxies? How many stars like ours are in the Milky Way? 

Anderson: It’s all kind of nonsense. These are numbers that we don’t deal with in our daily lives. So the Milky Way has about 200 billion stars. 

Douglas: Wow!

Anderson: So that’s two times 10 to the 11th. So a 2, followed by 11 zeroes. And I said, there’s at least 100 billion other galaxies. The Milky Way is a little bigger than average. I’d still say conservatively, there’s probably a 1 followed by 22 zeros other stars out there.

Douglas: Yeah, those aren’t numbers that we deal with?

Anderson: No, it doesn’t make a whole lot of sense. I can repeat them to you because I do them academically, but there’s not a lot of context behind that.

It’s something like there’s more stars in the universe than grains of sand on all the beaches in the earth, something like that. You could look up the exact quote but it’s a phenomenal number. 

Douglas:. What haven’t we talked about?

Anderson: Well, there’s two other parts. So the second half of the research, we’re going to be doing some machine learning. I have a collaborator in South Africa, and she’s going to take the objects that I’ve identified, and use that as – what’s called a training set, basically train the computer to look for similar objects. 

It turns out that humans are really, really good at pattern recognition. And this is something that computers are not as good at as they are at other aspects of artificial intelligence. So she has an algorithm that she would like to try this machine learning on. And so we’ll use what I find as a training set to try to classify these objects, and hopefully find new ones. That part of the research is the most exploratory.

Douglas: And you’re also doing some STEM training too. 

Anderson: We have this outreach program called SPOT. And in SPOT, the program ambassadors — so college undergraduates give science lectures to the public and to high school students, as a way of training the ambassadors in public speaking and in science communication, and also in engaging the next generation of scientists. 

My other collaborator on this project, Catherine Williamson, will be developing a new SPOT module focused on supernova remnants. This will be a talk that the ambassadors can give to their audiences on supernova remnants.