Dark matter and ghost particles—industrial data helps uncover the secrets of the universe

REBECCA AHRENS

In the beginning, the universe was as tiny as tiny could be. Planets didn’t exist yet. Stars didn’t exist yet. Time and space didn’t even exist yet. And then, as many of you know, there was a BANG. 

Scientists think that out of that tiny bit of hyper-condensed stuff came exploding the building blocks of everything we can see around us: stars, planets, asteroids, meteors, life, non-life, all the luminous matter—everything we think of when we think of the universe. But, the story goes, something else came out of that big bang too. Something dark. Something undetectable. Some utterly mysterious force, or particle, or substance that seems to be holding the fabric of the cosmos together.  

Everything we can see—either with our naked eyes or with the help of instruments like telescopes—all that stuff only accounts for about 15% of the mass we expect there to be in the universe. The widely accepted theory is that the rest is what physicists call “dark matter.” This matter is called dark because it doesn’t seem to interact with the electromagnetic spectrum: it doesn’t emit or absorb light, we can’t detect it using radio waves, or x-rays, or any other frequency wave on the spectrum. But if that’s true, how do we know dark matter even exists?

I’m Rebecca Ahrens. And you’re listening to stories from Our Industrial Life.

Each episode, we bring you tales of how industrial data and technology impact our everyday lives, keep our world running and shape the future of society and the planet.

This week on the show, we’re going deep underground, into the deepest clean lab in the world, where scientists at SNOLAB have set up super sensitive experiments in hopes that they will be the first to detect a faint glimmer, a subtle sign that dark matter really is out there, quietly holding our universe together. 

STEVEN BACK

So, there's no other place like this on Earth. There's no other place that's as deep and as clean as we are. 

REBBECCA

This is Steven Back. He’s the Technical Services Manager at SNOLAB. Basically, Steven’s team is responsible for looking at all the industrial data coming off the equipment at SNOLAB. 

STEVEN

My team consists of a team of instrumentation techs, millwrights, electricians, machiners, plumbers. And make sure that this facility keeps running. We build new infrastructure in the facility, and we maintain and operate systems.

REBECCA

It’s a pretty important and tricky job because SNOLAB has some fairly unusual operating conditions.  

Can you just describe what that looks like for you, like, when you enter the lab every day? 

STEVEN

So we start on the surface. Like everyone else, I drive in to work.

REBECCA

SNOLAB, by the way, is located in Ontario, Canada. 

STEVEN

Two kilometers underground, in a active nickel mine. So I put on my mining gear, put on my hard hat, put on my safety glasses, my safety equipment. And then I go down to this cage that goes down two kilometers—6,800 feet down—at 2,200 feet a minute. And it goes pitch black and you go down quite quickly to this environment where you're thrust into this mining world where there's 20% more pressure on you.

REBECCA

Once you get to the SNOLAB level of this shaft, you start to make your way down these long mining tunnels. 

STEVEN

We walk a kilometer and a half into the lab. And you're walking through the same mining environment that all the miners go every single day.

REBECCA

Then, after about a mile of walking through this dark mining world…

STEVEN

You're met with these double doors. And that's the entrance of SNOLAB. And that's when things start getting a little weird. Once you enter SNOLAB, we ask you to remove all your coveralls— remove everything—and take a shower.

And then we wear these, like, special blue coveralls in the lab. We wear hairnets. 

The mining dust has uranium and thorium in it. And so these are radioactive particles that, when they decay down, they become lead. And anything radioactive would impact these world-leading experiments.

We're an ISO six cleanroom. So that means that just any amount of dust, any amount of dirt coming in the facility, can be very detrimental to these fundamental science experiments that are happening.

We have to be very super cautious that everything that goes in the lab is clean and doesn't bring any sort of dust from anywhere in the world. So every single part that we bring in—and every experiment has thousands and thousands and thousands of parts—we meticulously clean every single one of those parts.

We also have an extensive testing program that every material we bring down is not radioactive. So we have to make sure that whatever bolts we're bringing down, materials we're bringing down, that it doesn't have a high radioactive signature to it. As well as that nothing, like, sheds. So, like, in our lab, wood is not really allowed.

REBECCA

Pencils aren’t even allowed

STEVEN

Because all that stuff kind of sheds material and that prevents us from being a clean lab. 

Neutrino detection

REBECCA

SNOLAB conducts several different kinds of experiments, including looking at the effects of increased pressure on organisms living underground. But the main reason SNOLAB needs to be the deepest clean lab in the world is because of experiments like PICO, DEAP, HALO, and SNO+.  These, along with a few others, are set up to look for extremely subtle signs of neutrinos and dark matter.

So far, no one has been able to detect dark matter. And even though we have about 100 trillion neutrinos whizzing through our bodies at every second, it’s incredibly difficult and rare to detect them. 

Can you explain what some of these experiments are, what they're trying to do? And why you need to conduct them so far underground, and in such an immaculately clean environment? 

STEVEN

So the analogy that I use is: imagine you're in an orchestra and you have, like, these violinists. And you have these flutes and your trombones. And it's, like, beautiful harmony around you.

And this is—I compare it’s what it’s like being on the surface. And so the music around you, this harmony around you, is all the thousands of millions of particles that are coming through you at every single second. So particles coming from the sun, coming from the universe, distant galaxies—they're all kind of coming through us. We don't notice them.

And what the scientists here are trying to do is they’re trying to listen for pin drop. And you can't hear the pin drop in this sea of music because there's just so much. And he pin drop is dark matter. Or the pin drop is neutrino double beta decay.

And so, in order to listen for this pin drop, we go two kilometers underground. Because this two kilometers of rock is a pretty good way of shielding out these millions of particles that are going through us. So where I'm sitting right now—so I’m actually right now two kilometers underground—there's only a couple particles that are actually able to make it through this two kilometers of Canadian Shield rock so that the scientists can really look at the particles that they're interested in.

And so the particles that we're interested in is dark matter and neutrinos.

REBECCA

SNOLAB started as an experiment designed to study neutrinos and has since expanded into other types of experiments, including those set up to look for dark matter. 

STEVEN

We originated in the early 90s as the SNO experiment, the Sudbury Neutrino Observatory, which was this single experiment focused—looking at—the different flavors of neutrinos coming from the universe. That experiment won Dr. Art MacDonald the Nobel Prize in Physics in 2015. 

So neutrinos are these subatomic particles. The sun spits out millions, probably billions, trillions at us, and they decay in a certain way. 

REBECCA

As is the case with dark matter, scientists had theorized the existence of neutrinos for a while before they found them—which they did in 1956 by creating them in a nuclear reactor. Then, in 1965, physicists working in a gold mine in South Africa finally detected neutrinos coming from cosmic radiation.

Like dark matter, neutrinos don’t interact with the electromagnetic force. They don’t emit or absorb light and we can’t detect them with normal telescopes. Sometimes they’re even referred to as “ghost particles” because they zip through walls and floors and mountains and our bodies effortlessly, with apparently no trace. In fact, despite the trillions of neutrinos moving through our bodies, there’s only a roughly 25% chance that one will actually interact with your body over the course of your life. 

Experiments like the original SNO and newer SNO+ experiment are set up so that if and when a neutrino does interact with something, scientists will see it as a small flash of light. These experiments are what are known as…

STEVEN

Scintillating experiments that try and create an environment where dark matter might interact with something and create light.

So my favorite experiment, because I've worked on it a lot, is the SNO+ experiment. And the SNO+ experiment is the successor to the Nobel-winning experiment. And it’s this 12-meter diameter acrylic vessel that is within a cavern. Remember, we're two kilometers underground.

And this acrylic vessel is filled with linear alkylbenzene, with this mineral oil. And what's kind of neat is that this mineral oil is normally used to make laundry detergent. But we can use it to make world-leading science.

And so a large portion of my time at SNOLAB was: we took 800 tonnes of this, shipped it down by 200 rail cars underground. And then we purify that mineral oil in a distillation stripping purification plant, so that it’s ultra-high purity linear alkylbenzene. And then we filled this detector.

And the detector is surrounded by tonnes of ultrapure water. And so this is water that—we have an ultrapure water treatment plant that, like, de-ionizes it and removes just, like, everything from it. It's clean. It has clean nitrogen injected into it. You shouldn't drink it, as I'm told, because it will take all the minerals out of you. 

REBECCA

Oh, Interesting. 

STEVEN

Yeah, it won’t make you a better athlete by drinking ultrapure water. And so it's just, like, a massive detector. And it's quite beautiful.

REBECCA

You can see photos of the SNO+ experiment on SNOLAB’s website which we will link to in our show notes. And they truly are beautiful to look at. It’s like this rich, deep blue luminous sphere that looks like it’s made out of tubes or wires held inside a huge, glowing, klein-blue orb. 

STEVEN

What is surrounding this acrylic vessel are these things called photomultiplier tubes. And these tubes can detect the faintest amount of photons—of light—in the detector. And I believe the number is 10,000—that is 10,000 of these photomultiplier tubes surrounding this vessel looking for this faintest light that's being produced by interactions of particles. Because when a particle interacts with our laundry detergent, it makes light. 

REBECCA

This Nobel-prize winning experiment at SNOLAB showed that even though neutrinos don’t interact with the electromagnetic spectrum, they still have mass—less mass than anything else in the universe. But they still have a teeny tiny little bit of mass. 

Dark matter detection

SNOLAB is clean enough and deep enough to detect these nearly imperceptible neutrinos. So, why not try to detect something much more massive, but just as imperceptible—like dark matter?

Scientists think that something like 85% of the matter in the universe is dark matter. But even though it seems like most of the universe is made up of this dark matter, no one has been able to detect it yet. Because remember, like neutrinos, it doesn’t interact with electromagnetism. So, what makes us think dark matter is out there to begin with?

STEVEN

They theorize this because gravity and other forces don't account for the kind of behaviors that they're seeing in the universe. So when stars are moving around, or when planets are moving around, the way they move isn't explained fully by gravity and other forces that they can observe.

REBECCA

Mass is what creates gravitational force. But, when we take the total mass of the visible matter in a galaxy and compare it with how fast the galaxy is spinning, we discover that there isn’t enough mass to keep it from flying apart. And yet most of the galaxies are clearly not flying apart. So if it’s not the mass of the visible matter holding things together, then what is it? 

Scientists have been wondering what that missing matter might be since the 1800s. Was it “dark stars,” gas clouds, black holes? Over the past century or so, evidence has been mounting to support the idea that it’s some kind of dark, non-luminous matter.

There’s a great video that goes through the evidence we’ve collected so far for the existence of dark matter by Dr. Becky Smethurst, an author and a Royal Astronomical Society Research Fellow at Oxford. We will link to that video along with other resources in our show notes. 

STEVEN

The issue is, we've never observed dark matter.[AK2]  So all these scientists around the world, and all these experiments, are trying to figure out what is dark matter? What does it look like? And the challenge here is that they theorize that dark matter doesn't want to interact with anything else. It doesn't really want to interact with a lot of particles. Doesn't create light. Doesn't create things. So you have to make these experiments that try and create these ecosystems to have this dark matter interact with something so you can observe it.

REBECCA

Just like SNO+ looks for light coming from neutrinos interacting with its blue sphere of laundry detergent, SNOLAB’s DEAP experiment looks for light that might be created by dark matter particles interacting with an acrylic sphere holding 3,600 kg of liquid argon. 

STEVEN

And then there's some experiments that make bubbles.

REBECCA

The SNOLAB PICO experiment uses a chamber of refrigerant super-heated but held by pressure at the cusp of boiling. The hope is that if a dark matter particle interacts with the refrigerant, it will cause it to boil, making bubbles that the lab can detect with cameras and microphones.

STEVEN

So, we've never seen dark matter. It’s just creating this environment. And to create that environment, you have to be ultra clean. You have to have nothing else happening in that environment so that you can observe it. So this goes back to the pin drop, where if you have too much noise, you can't hear the pin drop. And so they make these ultra clean, highly sensitive experiments so that they can potentially hear a pin drop. 

REBECCA

Gotcha. So just to make sure I'm following what you're saying. Neutrinos, which we have detected, and dark matter which we theorize exists, but we don’t know exactly what it is,  and we haven’t quite detected it yet[AK3] , would put out a signal that's, like, very, very subtle. If we do get one of those interactions, like a bubble or a burst of light, it's quite rare. And so to zero-in on that signal and notice if and when dark matter does interact with something, we have to make sure that there's nothing else interfering. No other radiation from the solar system. No other particulate matter that might be in the area. Everything has to be controlled really precisely. 

STEVEN

Absolutely. It's cool when you're looking at the data that these experiments produce. They can look at a graph of data and see these events. And then they can be like, “Oh, that's that kind of particle. That's that particle going through the detector.” Especially the neutrino detectors, where they see the particles going through, and they're like, “Oh, that's a muon,” or, “that’s a different kind of particle going through it.” And they can tell that based on how it went through the detector and how it interacted with things. But they can only do that, because it's, it's so clean, and nothing else is interacting with it. So if I put my hand on the detector, it is too much radiation for that experiment and that experiment won’t be able to operate. 

OPERATIONS DATA

REBECCA

As we’ve said, everything inside SNOLAB has to be very tightly controlled and monitored so that if and when a neutrino or a bit of dark matter does interact with a particle inside of experiments like SNO+, PICO, DEAP, HALO, SENSEI, or CUTE, physicists will be able to see it in the data. That means that every single piece of equipment has to be operating exactly how it’s supposed to operate. That’s where Steven and his team come in.

And here is where we get into the cool nitty-gritty (or in SNOLAB’s case, absolute opposite of gritty) of how the science actually gets done. So this part is gonna be for all my equipment, data and operations nerds out there. But we will keep it high-level enough so that even if you’re not an operations nerd, you can still follow along. And, if you stick around, you’ll also hear about SNEWS: a network of neutrino detectors set up to give early warning when a supernova event might be visible in the sky.  

[Music interlude]

STEVEN

So at SNOLAB, we have a chiller system to provide chilled water to all of our experiments. We have mine power centers to provide electricity, a liquid nitrogen plant to produce cryogenics. And my team has helped build the systems to be able to provide the services to these experiments. And some of the tasks are a little less exciting than building a liquid nitrogen plant. 

We have the deepest underground flushing toilets in the world. And those need servicing too. So we sometimes get to replace a toilet or something of that nature. But it's all important stuff to keep the lab going. 

But then we also help build these world-leading experiments. And these experiments are not something you can…

[Sound of paging system] 

Sorry. Paging system. World-leading facility requires a paging system for us to be able to talk to each other across the facility. [laughing]

So, yeah, so we help build these world-leading experiments. These things are, like, decades-worth of work to design and develop.

So, the experiments we’re bringing in now underground, they’re iterations of previous experiments that have been in the works for, like, decades. And so, this is where it’s really innovation and creativity from scientists and engineers around the world to develop these, kind of, highly sensitive experiments. And then they come here, to SNOLAB.

And we clean these experiments. We clean all the parts. We bring them all together. And then my team helps them put this all together. Because in a two-kilometers-underground environment, it's a little challenging to put things together. 

There is a bit of an art to being able to put this all together in a way that's, that's clean, it functions how it's supposed to function. And so that's what my team fundamentally does—is to enable this world-leading science. 

REBECCA

And what role does industrial software play? I mean, I assume you're, you're gathering data about these various systems. Can you just paint a picture of what that looks like? 

STEVEN

Yeah, so in my previous iterations at SNOLAB—I’ve been here for ten years—I was a Process Engineer, Design Engineer, Operation Engineer. I've held all the engineering titles here. And data is very critical, because like the scientists trying to discover dark matter and listening to neutrinos, all the systems around it are generating millions and millions of data points, surrounded by 1000s and 1000s of data tags. 

Like our building automation system: that's ensuring that we have power to lab, we have chilled water to lab, liquid nitrogen. And these things are being fed through industrial building automation systems and distributed control systems. And these things really help us to make sure that the lab is running at optimal efficiency. Because a lot of these experiments—if we have a power bump every single month, that experiment can’t be run. So these experiments run at such a cold level—they’re running like a couple of kelvins above absolute zero—that if you have a little power bump every single month, that experiment just can't take data because it's taking forever to cool down. 

And so really, we're using data to monitor that all of our systems are working as they should be, as well as informing preventative maintenance activities on it. So, we're trying to use predictive analytics to look at the data and then inform—to say to my team, “Hey, that bearing needs to have some work on it. It's starting to show signs of higher vibration.” Or we have systems that are saying: “that is out of spec, we should look at that.” 

There's an array of particle counters throughout the whole lab. And these particle counters, like, send off an alert to a team of cleaners to be like, “Hey, there’s dirt in this area.” So, right now, where I am, if I started to make a mess, the cleaning group would get an alert saying, “Steven is making a mess in the bottom access drift!” And I would probably have a swarm of cleaners come and yell at me. 

And so that's how data is very important to us. Because if we allow that dirt to reach the experiment, if we allow those power outages to happen, then this fundamental science just can't happen. And so that's why it's so critical to us. Why data is so important to keep this facility running, 

REBECCA

You mentioned as well, in a previous talk, that you use it for doing things like root-cause analysis. So if something goes wrong, you can look into it. Can you talk about that a little bit as well? 

STEVEN

Yeah, so with all these sources of data in the lab, it's not that we have one global control system. There's data coming from multiple different systems—both from our experiments, from a building automation system, from various processing plants. They’re all generating data. And so when an event happens, such as like a power outage, or a pump failure, we want to be able to go back and learn from that to ensure that we prevent it from occurring again. 

REBECCA

You talked about, previously, the importance of uptime for certain experiments, I mean, particularly detecting neutrinos ahead of a supernova. Can you talk about that a little bit?

STEVEN

Yeah. So a supernova is this really cool event where, in the universe, a star has exploded millions and millions of years ago. And when a star explodes, what it does first is it sends out this wave of neutrinos. 

Neutrinos travel at nearly the speed of light. And then after it sends this pulse of neutrinos, it sends out this pulse of visible light. 

REBECCA

The last supernova visible to the naked eye took place in a galaxy close to our own Milky Way, in 1987. Physicists estimate that, on average, the Milky Way should have one to three of these supernova events every hundred years. But, it’s been 420 years since anyone has detected one there.

STEVEN

And so we're kind of due right now for another supernova to reach our planet. And what scientists have learned from the last event is that we need to be ready to observe a supernova occurring. Because the issue is that they—it happens so quickly that all of the sudden, “oh, this, this event happening in the sky—we should have had some pre-warning of it.”

And so what they came up with is this network called the SNEWS network.

REBECCA

SNEWS stands for SuperNova Early Warning System. 

STEVEN

Which is basically a global alarm clock network, which uses sensitive neutrino detectors to basically send out this email alert. And I think anyone in the world can subscribe to this email alert, saying, “We think that there's a supernova happening in the sky right now.” 

And so, at SNOLAB, we have two experiments, the HALO experiment and the SNO+ experiment, as part of this network. And so they're constantly, 24/7, looking for this kind of pulse of neutrinos coming through. And then if they see this, they send out an alert.

They say, “Hey! We think that there's a supernova happening.” And if a couple detectors around the world say, “Yeah, we see the same thing,” then they can use those detectors and triangulate where in the night sky this event is occurring. 

And that basically sends an alert to all these world-leading telescopes around the world to point to that direction so that we can observe. And so that we can fundamentally understand more: how does our universe work? How do stars work? How do black holes work? How does the universe work?

And so uptime becomes really important, and that is that you don't want to be late to this party, right? You don't want to snooze through this alarm clock. And so if at any time we lose power in the lab, then HALO and SNO+ can't be looking for a supernova. So we're using data to ensure that we are maximizing the uptime of all these experiments. 

So each of these experiments have kind of a unique set of requirements and utilities that they need. And so we're constantly looking at, what's our uptime for power? What's our uptime for chilled water? And then how can we improve that? 

REBECCA

This is where predictive analytics might become useful. So, for example, if you have your water chiller, and you're looking at—you have, like, historical data about how it's been operating. And then you notice, like, “Oh, last time, there was a problem with the chiller, there was, like, this data pattern, like, there was this particular vibration, or we saw this, like, fluctuation in the temperature.”

And with predictive analytics is the idea that, in the future, you could notice that trend before something goes wrong and ideally, like, intervene so that you don't have, you know, a dip in your electricity or the chiller doesn't go offline. 

STEVEN

Yeah, and we're slowly building on that. We're using, say, vibration monitors to be monitoring pumps to be able to tell us when things are starting to trend in the wrong direction before we can visibly detect it using noise or thermal imaging on a system. And to be able to say, “Hey, there's a problem here, we should take a look at that.” 

But where we want to go is be able to have AI tools look at our data and predict and make correlations between data, right? To try and inforrm potential future failure modes, and ideally ways that we can intervene before it becomes an issue, right? 

Where if something is occurring, if there’s an environmental condition change, or a quality change, that we can step in before it starts impacting that equipment. And then as well as using AI to help go through, say, databases of manuals to pull useful information from that database.

We spend a lot of time: if a pump fails, you go to that pump manual and try and understand, “well, how could that pump have failed?” And using AI more— and I think this is where the industry is going—to go through that manual and really point out areas that might be the reason why that pump has died, using the data to inform that.

If the pump had high bearing temperature, being able to then go through the manual, and find that section that talks about high bearing-temperature failure. Because we're a science facility, so we don't have 1000s of people to look at this stuff. So we want to be really smart with our resources that we have available to us. 

REBECCA

Right, okay. So this is in the future that you're talking about. Is the vision something like, for example, you know, feeding a manual for some piece of equipment into an AI model, and then being able to interact with it using natural language processing? So it can look at the data that you have, and you can say, “Hey, can you pull up the maintenance manual for XYZ piece of equipment, and like, given these conditions, give me a recommendation on, like, what you think is going on and what we can do?” To speed up that process of going through and searching? Is that kind of what you're talking about? 

STEVEN

Absolutely, right? Because I mean, at SNOLAB, we have 1000s upon 1000s of pieces of equipment that each have their manuals. And being able to go through and search manuals and find useful sections as well as being able to correlate data between different datasets, and make those insights, we currently spend a fair bit of time.

When you do root-cause analysis, it’s pulling in datasets from different areas and overlaying them and trying to figure out where the correlation is. You say, “Well, my chiller tmperature went up” or “my pump failed.” And then you’re pulling in, like, our mine power center data to understand: Was there a quality issue on the incoming power? Or did the drift temperature go up? And trying to see is there a correlation in other datasets to the failure event? I think the future is using AI to help facilitate those kind of events.

REBECCA

So another thing you talked about previously too that I want to hear a bit about is validating designs, or using the data to inform future designs. Can you expand on that a bit? 

STEVEN

Correct. So when we're designing something, say we're designing a UPS, say, backup power for a system. We want to be able to use data to inform those decisions. So before we had access to industrial data readily available, it was kind of a little bit more of guesswork, using kind of, maybe rules of thumb to design a battery backup system for a critical piece of infrastructure. Which typically meant that people took a more conservative approach. Which meant that you usually got a bigger piece of equipment that you really needed to have. Which also meant it took up more space. 

And so, we're in a 54,000 square foot clean space. Every piece of square footage in this lab is very precious. And by having access and having industrial data, we're able to design our systems to what the actual data is. 

So we're designing a backup power system, we can look at how long it takes for our three megawatt generators to kick in. So we can inform how much bridging power we need to be able to back up that system. So, where before we said, “it might need 30 minutes,” the data might show that it only needs seven minutes. And so using the data, we say, “Well, okay, we're going to add a little bit buffer, and we'll make it 10 minutes.” 

So let's say we've designed this backup power for 10 minutes. Now, once it's installed, we can constantly monitor that and every single time we have a power bump, and the backup power kicks in until the diesel generators take over, we can validate that design. So if on day one, it was seven minutes, but in year three, it started becoming eight minutes or nine minutes, then we can start looking at well what's changing, right? Is it our batteries needing to be replaced? Is there something fundamentally wrong with the equipment? Can we look at that before it exceeds, kind of, our engineering limits? Before it starts impacting the uptime for that experiment, and having data will help inform that.

REBECCA

I think you mentioned as well, in a previous talk: So you have some software that allows you to sort of pull all these data streams together and look at it in certain ways, or visualize trends. And I don't know that you said this explicitly, but sort of alluded to maybe it also enables you to share data? Like, are scientists able to look at data when they're not physically in the lab using the software? 

STEVEN

Yeah. So what's unique about SNOLAB is that we enable world-leading science to happen. And there's over a thousand scientists that come to SNOLAB to do their research. These scientists are coming from places from, like, Princeton, and Yale and Oxford and doing their science. And there's these collaborations that run these experiments that are made up of hundreds of people, some of them. And they're looking at data the same way that I'm looking at data from, kind of, our industrial systems around us.

And so that's important about having software that allows us to bring these data lakes together into a platform that allows us to both structure and pull the insights from that data, but as well as to share that data. And so right now, our operating data—so the electricity or chilled water—all that data is available to all of our scientists. Scientists who I know in Oxford, England, is able to look at that data, and then make decisions about that data that impacts their experiment.

[music]

A lot of people ask: Why do you do this work? Why does it matter? That you are studying, trying to find dark matter? Why are you trying to look at neutrinos more and understand neutrinos more? 

Our executive director, Jodi Cooley, describes it well. She describes it as like an onion. The outside layer are the things that we buy every single day—we don't even think twice about it. And then, as you go deeper in the onion, you kind of get the industrial manufacturers, the mining environments, people who are making the goods or are extracting the minerals from the ground to be able to make these things that we're buying every single day. Then you have the researcher who's trying to innovate in R&D, and make new products. But the fundamental core of it is that you need to understand how things work in the universe. And a good comparison to this is gravity. If you want to make an airplane, you have to understand how gravity works. 

And same thing goes with dark matter. We don't know today the useful applications of dark matter. But we know if you don't know what it is, then we'll never be able to use it in applications going forward. And we're starting to see this in neutrinos. One of the key areas in neutrinos is nuclear non-proliferation. So using neutrino detectors to ensure that nuclear plants around the world are behaving how we want them to behave.

Here at SNOLAB, we can see neutrinos coming from the nuclear detectors that are nuclear power plants in Ontario. And we can kind of see neutrinos coming from them. And that's an application that didn't exist several decades ago. We're hoping that by discovering dark matter, we can then eventually use that to drive industry, drive innovation, drive, kind of, these creative moments to come up with products that we don't even know exist today.

REBECCA

I can understand why people would ask, like, “Well, what's the application?” or, you know, “What's the benefit, or the commercial benefit, to society down the line?” But personally, I think there's also just a value in itself of learning more about how the world and the universe work around us, even if it never had any application.

To me—and maybe not everybody agrees with this—but to me, it seems like, wouldn't you want to know? Like, if there's this thing out there that is impacting how, you know, the structures in the universe move and interact with each other, don't you want to know what that is and how it functions?

STEVEN

Yeah, yeah. 100% right. It’s like, some things are just worth knowing. Knowing when you look up at the sky, that that's dark matter that you're looking at.

REBECCA

That’s our show for today. If you want to hear more about what kind of data and software Steven and his team are using to make sure these critical experiments keep running, you can find a link to Steven’s full presentation in our show notes.

[Production team credits]