365daysDate: August 2, 2009

Title: Stardust@home: the Search for Interstellar Dust


Podcaster: Dr. Andrew Westphal and Anna Butterworth

Organization: Center for Science Education at UC Berkeley’s Space Sciences Laboratory

Description: The Stardust spacecraft returned the first samples of extra-solar interstellar dust (“star dust”) ever collected as well as dust samples from comet Wild 2 – the first ever samples from a solid Solar System body beyond the Moon. While the comet dust samples numbered in the thousands, the number of interstellar dust particles expected to be found in Stardust’s specially created aerogel collector are estimated to number a few dozen at best, and searching for them has proved exceedingly difficult. In this podcast, Stardust@home Director Dr. Andrew Westphal will discuss how you can get involved in the search and the team’s progress to-date.

Bio: Dr. Andrew Westphal works at UC Berkeley’s Space Sciences Laboratory (SSL) where he pioneered the development of aerogel for the capture and identification of interstellar dust in low earth orbit. He is now considered a leader in the development of laboratory techniques for the extraction and analysis of captured interstellar dust, principally in support of the Stardust mission. Dr. Westphal also teaches Physics for premedical students at UC Berkeley and has extensive experience working with other student groups, including elementary students and a traveling student summer camp at SSL.

Today’s sponsor: This episode of “365 Days of Astronomy” is sponsored by Rossiter & Associates (, a process improvement consulting firm specializing in energy efficiency for oil refining, petrochemicals and chemical operations. Rossiter & Associates – Energy Efficiency by Design.



AB: Hello, my name is Anna Butterworth. I work on the Stardust@Home project at the University of California at Berkeley and I’m here today talking with the Stardust@Home P.I., Andrew Westphal. Hello Andrew.

AW: Good Morning

AB: Now, for those who don’t know, what is Stardust@Home and how did it start?

AW: So, Stardust@Home is a part of the Stardust project. Stardust, in fact, was a mission, a NASA mission, that returned the very first samples of solid extraterrestrial material from beyond the Moon. It brought back two completely unprecedented samples. It brought back the first samples of material from a comet, that is, material that was collected at a comet and brought back to Earth, and it also brought back the very first samples of contemporary interstellar dust in a completely different collector.

AB: They were back-to-back, weren’t they?

AW: That’s right. There were two collectors that were bolted back-to-back, the collectors were made of a weird material called aerogel, for the most part. There are also some aluminum foil collectors, but they’re a smaller fraction of the total area of each collector. These aerogel collectors were used to capture dust from the comet and from the stream of interstellar dust that’s coming into the Solar System. The challenge here is that you have to capture dust particles that are going at very, very high speeds and aerogel – it turns out to be the most, the best material for capturing that kind of dust at that kind of speed.

AB: What’s it like, aerogel? What’s material is it?

AW: Well, it’s a chemically, it’s very simple. It’s just silicon dioxide, just like glass. But, it’s a very unusual material because it’s got an extremely low density. It’s only a few times as dense as air. When you hold it in your hand it looks like a ghost. It also turns out to have very unusual properties mechanically, so it’s very, very challenging to work with.

We’re now working on the interstellar dust collection from Stardust and there are many facets to this project. But the very first one is simply to find the interstellar dust particles themselves. That’s the prerequisite for doing anything on these samples.

AB: How big might the particles be?

AW: The particles are incredibly small. They’re typically less than a micron; that is a millionth of a meter in diameter. They are extremely tiny. They’re very difficult to see. So, that was really the challenge, is to find these very, very tiny particles in this collector which on the scale of the dust particles is vast, that is, it’s about a tenth of a square meter in area, but the dust particles are a millionth of a meter or smaller in diameter.

The challenge was to search efficiently over this whole collector to find the tracks made by these tiny particles when they come into the aerogel. The difficulty, really, was that we didn’t know how to even approach this problem when we first started the project. We knew that we would be using some sort of automated microscope to collect digital images of the entire collector. But the challenge after that was to identify, to recognize these tracks. And we assumed, at first, that we would use some sort of computer based algorithm, but we quickly realized that we had no idea how to write an algorithm that would reliably identify tracks.

AB: I thought you need to know the answer before you can ask the question.

AW: Yeah, that’s exactly right. And so what we did know was that people coming into our lab and looking through a microscope at aerogel and at tracks of particles in aerogel, could very easily recognize them even if they had never seen aerogel or tracks before. So, this led to the idea that we might be able to ask for help to search this huge area. And, just to give you a sense of scale, the number of images that you have to search; the number of fields of view, I should say, that you have to search is of order a million and so that’s a huge amount of searching that you have to do — far beyond our capabilities in our small research group.

AB: So, each field of view is what, that’s the view that the microscope would see?

AW: Exactly. That’s the view that the microscope would see and so if you were doing this by hand, you would be peering through a microscope, and going from one field of view of the microscope to the next. And, if you had one person working on this, actually, even if you had a small team of people working on this, it would take something like a hundred years to search the entire collector! So, this clearly is not very realistic. So, this is why we took the really unusual approach that we did which is to ask for help from amateur scientists all over the world. And this is the project that is now known as Stardust@Home.

The way it works is that we have a web-based microscope. It‘s called a virtual microscope, which actually turns out works even better than a real microscope because you don’t have to lean over a microscope and peer through eye pieces. You can just use it right on the desktop of your computer and it allows you to search very efficiently for these tiny particles. So we have now more than 27,000 people who have volunteered to help us. They have collectively done more than 60,000,000 searches of this collector.

AB: How long has this been going?

AW: Well, we started in the Fall of 2006. That was a few months after the Stardust Mission came back with its samples. We now have a number of candidates that we’re busily analyzing, extracting, and analyzing, and it turns out that the Stardust@Home part of this whole effort is by far the fastest of any of the aspects of the project. The other steps — after one has identified a candidate and wants to find out whether it’s really an interstellar dust particle or not — these subsequent steps are far slower.

First, you have to extract the particle or the candidate from the collector, and then you have to analyze it. Because these particles are so small, it requires enormously sophisticated instruments. In fact, the best instruments for the job – practically the only instruments for the job– are x-ray microscopes at Synchrotrons. These are gigantic instruments the size of shopping malls. There are a few of them all over the world and these have been employed now to analyze the candidates that we‘ve extracted.

We’re still in the process of analyzing the data and this is a slow and it feels glacial, this process. The reason is we have to be careful, first of all, whenever we are handling the collector. But also, it just takes a huge amount of effort to analyze these things. So, we really appreciate the patience of the Stardust@Home “Dusters” who have been waiting for word that we’ve really found the first interstellar dust particle. It hasn’t happened yet.

AB: So, the Dusters are really good, but how do you know that?

AW: That’s a great question. The Dusters have done a huge amount of work, but that’s really not sufficient. You have to know that they’re good, they have to be good, and you have to know that they’re good. And the way that we know that, is that we include in the data stream that everybody gets, a series of what we call calibration movies. These are fields of view that have either the image of a track in it already which we have placed in the image, or it’s one we have already examined and that we’ve concluded that it’s not got a track in it. So, we can measure the efficiency at which people can find tracks and we can also measure the false positive rate. What is amazing is that the Dusters who are working on Stardust@Home are incredibly good at what they do. They are far better, certainly the most experienced ones, are far better than we are because they’ve done so much work and they’re very experienced now at looking for these tracks. So, it’s we think really a wonderful example of Citizen Science in action.

AB: I think I will add that one of those tracks that the Dusters found we looked at in an x-ray microscope and it turned out to be fifty times thinner than a human hair.

AW: That’s right, that’s right.

AB: That’s how good they are.

AW: That’s right. Yes, that’s right. Some of the tracks that have been found, some of which we found, turned out not to be interstellar for sure. But they’re real tracks. They’re particles that came into the aerogel and they’re incredibly tiny. It’s amazing that anybody can find them, but the Stardust@Home Dusters did.

AB: So, this is a valid methodology then, you’d say?

AW: I think in two ways. We’re using these calibration images, we’ve shown that the Stardust@Home approach is a good way to search for features that you think you understand and can provide images of as examples. But, also, it’s a wonderful tool for discovery because some of the tracks that the Dusters have found turned out not to look anything like our calibration images, but they found them anyway. And that’s a wonderful advantage of working with people rather than computers. Computers are so literal minded that they would probably not have found these because we wouldn’t have trained them to. Whereas humans are much more open minded about what they are looking for.

AB: Well, it’s a fabulous project and we’ve got still a long way to go. Okay, thanks very much Andrew.

AW: Thank you, Anna.


End of podcast:

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