(bright music) - Hello, and welcome back to Conversations at the Perimeter. Today, Lauren and I are really excited to share this conversation that we had with Jessie Muir who's a postdoc here at Perimeter Institute, and she studies the mysterious phenomena of dark energy, which is believed to drive the acceleration of our universe's expansion.
- I learned so many things from this conversation that I didn't know before about dark matter, dark energy, gravitational lensing, and I even learned a new scientific term that I really love called galaxy clumpiness. It was just fascinating to hear about how Jessie's work really relies on an interplay between theory and experiment. And she told us about her work as part of the Dark Energy Survey collaboration and how her team works to process and analyze mind boggling amounts of data.
- What I found also fascinating was not only does she do all of this computational work, but she actually went to the telescope in Chile on top of a mountain where this Dark Energy Survey is doing its observations, so she got a real hands-on experience of what it's like to be an astronomer. - She also told us about a series of cartoons that she works on to help communicate her science and make it more accessible for everyone.
I know you're gonna enjoy this conversation, so let's step inside the perimeter with Jessie. (bright music continues) - Hello, Jessie, and thank you for being here at Conversations at The Perimeter. - Hey, thanks for having me. - We're really excited to chat with you today.
In particular, I'm excited to learn about dark energy, which is related to some work that you're gonna tell us about, and dark matter, all things dark, because we haven't really talked to any experts about what these things really are or what they aren't. Can you start us off by telling us, what do we know about dark energy and dark matter? Are they even related aside from both having dark in the name?
- I think the main thing that relates them is that they have dark in the name, and they're labels that we give to components of the matter and energy in the universe that we are fairly sure are there based on how they influence visible matter that we can see and measure and detect and study, but we fundamentally don't know what they are. But these are two different unknown things.
We can get into this in more detail, but sort of the simplistic description I give of what makes them different is dark matter seems to be some type of particle, but it clumps up under the influence of gravity, so it's not uniform in space. It behaves in some ways like ordinary matter that we're familiar with. It just doesn't seem to interact through light or through other forces or if it- - Because ordinary matter does that as well, right? It clumps in areas of high gravity?
- Yeah, yeah, so the thing that gravity does is it causes mass to wanna fall towards other mass. And it seems, as far as we can tell, that both dark matter and ordinary matter seem to feel gravity in the same way, whereas dark energy seems like we're not sure what it is, but it seems to be more like some property of space itself. So dark matter clumps up under the influence of gravity and we can see how it influences the formation of galaxies and how stars move in galaxies among other things.
And dark energy, we learn about and we've detected based on its influence of the very large-scale universe. So large and small scales kind of have somewhat different meanings depending on what field you're in. In cosmology, we tend to refer to small scales as anything under about 30 million light years. - (laughs) Just tiny. - So it's, you know, maybe a little bit different than the scales of, like, colleagues over doing quantum stuff here at PI. - So if that's small, what is large?
- Generally, we work in a little bit of different units in cosmology, but like 30 million light years is kind of the benchmark for once you're looking above that, the universe isn't necessarily uniform but in a statistical sense, it becomes uniform. So I guess you can maybe picture looking at, like, a zoomed-in or zoomed-out picture of, like, a lawn of grass.
If you're looking on small scales that are sort of comparable to the size of, like, little clumps of grass, you might be concerned with, like, oh, how is this blade of grass growing and how is it interacting with its neighbors? And so that would be like individual galaxies forming and observing.
And when you zoom out, you know, you can still see that, you know, the ground isn't completely uniform, there's still blades of grass there, but you can sort of get a sense of, like, the global properties of, like, this grass tends to grow in little clumps or is it more spread out or, you know, do we think it was grown there wildly or using sod, or I don't know, maybe this is getting a little bit obtuse- - No, I actually like that.
You know, it made me think of a golf course where it's all grass, but you look from above and there's different characteristics, different ways it grows. And you mentioned dark energy in comparison sort of being an element of space-time, is that right? It's something intrinsic to it? - For this, maybe it's kind of useful to, like, tell a little bit of the story of how we learned about dark energy. Up until the '90s, we knew that there's matter in the universe.
We've known there's dark matter, sort of first hints showed up in, like, the '30s, and then Vera Rubin made some measurements of the motion of stars and galaxies in, I believe, the '60s, maybe '70s. So we've kind of known about dark matter. We've had a good understanding of how gravity works since Einstein published his theory of general relativity.
And given those things, we know that mass attracts mass through gravity, we know there's matter in the universe, and so your expectation is even if everything is sort of thrown out by the Big Bang in the early universe, what you'd expect gravity to be doing is that all that matter is being thrown out, the universe is expanding, gravity should be acting sort of as a friction. It should be slowing that down.
Given a universe that contains matter and that has gravity, you expect to see that the expansion of the universe is decelerating. And what we found, or what several teams of scientists and since many have confirmed in the late '90s, was that the universe's expansion is not slowing down, it's actually accelerating.
And so previous to that, people were kind of looking at, like, all right, we can measure the rate at which it's decelerating to learn about how much matter there is and some stuff about the geometry of the large-scale universe. And this finding that the universe is accelerating, like, it's like if you threw a baseball up in the air and instead of coming back down, it, like, zips off in some other direction.
So there's gotta be some other sorts of energy there, and the simplest description that we can come up with that dark energy could be that would give us the sort of observable properties that we're seeing is that if empty space just had some intrinsic energy to it.
So sometimes people will call this vacuum energy, sometimes people will call it the cosmological constant, and so what that means is it's some energy density associated with empty space that's both constant in space, so same everywhere in the universe, and constant in time, so the same energy density throughout the history of the universe. And it seems to have been causing acceleration of the expansion of the universe just in the relatively recent past.
Here, recent being on cosmologist scales of the last couple billion years. And so the picture you can have there is the universe is expanding and it has some matter density, but as the universe expands, the same number of particles are around roughly, and that matter gets diluted.
So as the universe progresses through its history, the matter density will drop, and at a certain point, the average density of matter in the universe drops below that vacuum energy, that cosmological constant, and that's when the universe starts accelerating. So these different components have different influences on the behavior of the space-time in the universe, and this is something we can get out of Einstein's general relativity, we can relate the behavior of space-time to the stuff in it.
And so when the relative contribution to the total energy density of the universe switches from being matter-dominated to dark energy-dominated or cosmological constant-dominated depending on which model you wanna use, the expansion starts getting faster and faster. So we don't know what dark energy is, but we can sort of place constraints and say, is it a constant? Does it have some time evolution? Is it something that maybe interacts with matter?
And given one of these assumptions, you can go through and do your calculations for how that should affect the expansion history, how it'll affect how the matter is clumping up to form galaxies and things, and we can kind of test and constrain those. That's a lot of the motivation behind what I and a lot of other cosmologists do.
- It seems like a lot of the work that you specifically do is trying to look at the role of statistics in understanding some of these properties, so can you tell us in general how statistics comes in? - So it comes in in a couple different ways. One is, you know, if we're trying to describe the large-scale universe, you know, we look out in the universe and we see millions and millions of galaxies, like the experiment I work on, which I think we'll touch on later.
Like, we're working with a data set with a couple hundred million galaxies imaged, and that's only, like, one part of the sky, and it's not looking out as far as, like, future telescopes will be able to look. We want to be able to test our theories or to constrain the question of whether dark energy's density, like, varies in time or not, which is sort of one of the straightforward questions you can ask about that model.
You wanna find things about those measurements we're making that you can actually predict with your theory. And with our theory of the universe, we're not able to say, "I think I'm gonna see a galaxy at this location in space or this coordinate on the sky."
What we can say is we have some picture or some description of how a universe that started out very uniform, so the density being basically almost the same everywhere but with tiny density fluctuations, and then over time, given our understanding of, like, what types of matter are contributing to those fluctuations and how gravity works, how they grow over time.
So what you get is not a description of, okay, I expect to see a galaxy in spot A and a galaxy in spot B, but you can say I expect that the sort of size and fluctuations in density, so, like, how do you compare sort of the highest densities in the universe to the lowest densities, and you can make predictions about that.
And you can also, in the same way that, you know, our tortured lawn of grass analogy, like, you might be able to tie, like, how you put the seeds down on the ground to how, like, clustered the grass is. Are you seeing grass in a bunch of little tufts, or is it pretty spread out uniformly? We can make predictions for, like, do we expect to see galaxies distributed at random, or do we expect to see them clumped together?
And we can make predictions for basically the probability of finding galaxies separated by a given distance in the universe compared to an average distribution. So we're describing statistical properties of the distribution of matter in the universe. And then statistics comes in in another way as, like, all right, given these measurements of statistical properties in the universe, how can we use that to tell us about the physics of our model?
We have these measurements of, like, how close or far away we expect to see galaxies to one another. We can predict that with our model, but we know our model has some assumptions in it and we need to be able to do these calculations, we need to make some assumptions.
But a lot of my day and a lot of the work I do with my close colleagues is making sure that, all right, we're trying to use these measurements to say something very fundamental about physics in the universe of, like, does dark energy vary with time or not? And we wanna make sure that we don't mistake some complication in, like, how supernova blow gas out of galaxies or something.
Like, one of our big challenges in cosmology is trying to make sure uncertainties about the detailed calculations of that smaller-scale astrophysics, so just galaxy scales, doesn't influence the inferences that we're making from the larger scales, or wanna get as much information out as possible without biasing ourselves and tricking ourselves into thinking we discovered something about dark energy when, really, we're not understanding our modeling predictions.
So we do a ton of tests, we use a ton of simulations to really make sure that we do that rigorously, and then translating these comparisons of model predictions data into information about parameters of a model or which model is better than another one is the whole sort of subfield of study in cosmology itself. - Yeah, I would assume this must be a really challenging problem when you have so much data.
And I'm just curious, like, when you have all this data, how do you go about approaching the problem of when you need to look at the observations you already have versus when you need to go and collect more data in a new way? - There's a lot of value in looking at what data we have on hand and looking for new ways to extract information out of it.
So a lot of the measurements we make of these statistical properties of galaxies are looking at, like, the distances between pairs of galaxies, and you can go to sort of, we say higher order statistics, so that's, you know, statistics based on pairs of galaxies. You can look at triplets of galaxies and see, like, what kind of triangles you expect to see of different sizes and length scales.
And there's a whole field of research which these calculations tend to be a bit harder and the measurements tend to be a bit harder of understanding, like, what kinds of physics, either new or what we know about, can you get more information from, like, taking these maps we already have and, like, pushing them harder to get more and more information out of that.
But then going and gathering more data 'cause the more galaxies you make these measurements for, the smaller the error bars on those measurements are, so, like, when you make a comparison of your model prediction to the data, if your data are more precise, like, they're measured well, having more galaxies is good for that.
You can know that if you see a little bit of a deviation between your prediction and the data, you can be more confident that it's real and not some, like, statistical fluctuation or noise. And I think most, if not all, cosmologists are kind of engaged a bit in both of these things. We're consistently planning, like, working on the current generation of experiments gathering data, and sort of looking to the next generation of experiments which we'll be turning on.
And also, there's sort of a lot of complementarity there. So the experiment that I work on is a galaxy survey called the Dark Energy Survey, which is a survey that's mapped the distribution of matter in a patch of the sky measuring a couple hundred million galaxies, and we have the biggest data set of its type, so it's the most statistically powerful galaxy survey of its type, which we can maybe touch on it in a bit.
And so the constraints we can get from studying that map of the universe is really exciting and, you know, sort of pushing the bounds of what we can do in cosmology. It's also crucial as sort of a workshop for developing techniques we'll need when we go to the next generation experiment, which we'll get even more precise constraints, and, you know, I mentioned we have to spend a lot of time accounting for, like, are the approximations we're using for our calculations accurate enough?
And as your measurements get more precise, that answer can very easily turn from yes to no, and so we have to, like, push the bounds on that every time our data get more precise. - You mentioned the Dark Energy Survey, the experiment that you're working on. Can you tell us sort of the goals and motivations of that and how it actually works? Is this a telescope out in space or on a mountain, or is it something else entirely?
- I guess maybe as a, like, basic definition, a galaxy survey is some experiment usually run by, I think always run by a large collaboration which you try to systematically, like, observe a patch of the sky and make a really uniform map of the distribution of galaxies. So instead of, like, pointing a telescope at an individual galaxy or a group of galaxies and taking detailed pictures, we're trying to just map the sky so we can make these statistical measurements.
The Dark Energy Survey is what is known as an imaging survey, which means on our telescope, we basically have a giant digital camera, and we can, like, take pictures of the sky as opposed to, like, measuring the colors very precisely. That giant digital camera is called the Dark Energy Camera, which we're very creative with names clearly. - That's a good name for it. - And it is on a four-meter telescope called the Blanco Telescope in Cerro Tololo in Chile. So it's on a top of a mountain.
You put telescopes on tops of mountains because there's water in the atmosphere and, like, turbulence in the atmosphere can make images of space look blurry, and so you wanna go to where there's not much water in the atmosphere and there's not much atmosphere, so, generally, observatories are in deserts and on tops of mountains. - You've said this is a really big collaboration. Can you give us a sense of how big and how the different teams in this collaboration are organized?
- Dark Energy Survey has, I think, about 400 people in it. It's been going for over a decade so I think the camera was installed on the telescope in 2011. So this camera was built specifically for this survey. It's specialized to be more sensitive to red light than your average chip that would be in a digital camera. The CCD chips, or the little chip that would be in your digital camera, for the telescope is like three feet across so it's big.
So this collaboration worked on things from planning the survey to building the camera to installing it to running the shifts, so we did something like 760 nights of observing between, I think, 2013 and 2019. And then there's a whole team of people that go from sort of raw images from the big digital camera and turn that into catalogs of where do we see galaxies, what are their colors, what are their shapes?
These teams all overlap and people move between them, but then there's going from those catalogs to making these statistical measurements. And then where I kind of live within the collaboration at the sort of end of that is trying to go from those statistical measurements to inferences about the physics. So I've been talking specifically about measurements of galaxy clustering.
The image we have also lets us map the distribution of structure in the universe using how the shapes of distant galaxies get a little bit distorted by gravitational lensing when their light passes through clumps of matter along the line of sight. - And then the light is actually bent a little bit by the gravity of what is passing by? - Like a beam of light will get a bit deflected by a gravitational potential.
And, you know, if we're looking out over millions or billions of light years in the universe, there's sort of structures in the universe, these structures, I mean, like, galaxies and groups of galaxies and they kind of end up being aligned in this kind of filamentary structure. So light from more distant galaxies is going through the large-scale structure between us and them and getting deflected.
So we can both look at the fact that galaxies tend to live in high-density regions of the universe and that those high-density regions also cause the most deflection and therefore distortion to background galaxy shapes. Those are both tools we have to map the distribution of matter in the universe. There are other teams in the collaboration. There's a team that focuses on galaxy clusters, so, like, large groups of galaxies.
There's a team that looks for supernova and uses those measurements to learn about the expansion of the universe. But this data set is really rich and lets you do a lot of things not just in cosmology, and I'm sure I'm leaving out something in cosmology, but the fact that we have 760ish nights of observation over the course of six years, imaging each patch of the sky I think something like 50 times, so like 10 times in each of 5 colors. It also is really good to see things moving.
So there's a whole group, which I'm very impressed by but I am not part of, but finding, like, things like dwarf planets or comets in the solar system. - Wow, all from the same essential piece of equipment and experiment? - Exactly. - Maybe this is a silly question, but why so much observation? And how much of the sky are you actually looking at? - The survey area covers about one-eighth of the total sky, so it's kind of looking out the south pole of our galaxy.
So it turns out if you're trying to look at distant galaxies, the Milky Way is kind of a hindrance 'cause it's hard to see stuff behind it when you're looking through the disc of our galaxy. - So are you looking perpendicular to the disc? - Yeah, sort of looking down, and there's some other patches added onto the survey footprint to increase overlap with other kinds of measurements.
So there are other experiments that map the large-scale universe using light from the very early universe that was emitted in the first couple hundred thousand years of the universe when atoms first formed. - Is this the cosmic microwave background? - Exactly, yeah. - Okay.
- And so there's a lot of information gained by analyzing those data sets together, and so that's a whole team that's using the overlap where the DES map overlaps with the cosmic microwave background map from something called the South Pole Telescope. - Even though there's billions of years duration between what's pictured in those maps, do you compare one to the other to show how things evolve and change over time?
- There's that element, so you can analyze the cosmic microwave maps and see what inferences that would give you about cosmology, and then say, given our model, what do we expect to see in the late universe? If the maps are actually on the same patch of sky, you get something additional. Whereas, like, we kind of know the statistical properties of the CMB, cosmic microwave background map, and that light is also traveling through the same structures as the galaxies.
So the same structures that are distorting the galaxy shapes with, we call it, weak gravitational lensing 'cause it's, like, tiny distortions, and that same distortion affects the CMB light, so you can use a cross correlation or, like, look at the relationship between distortions in the cosmic microwave background light and the galaxies to be extra sure that the distortion you're seeing in the galaxies is from lensing and not through some other properties of galaxies.
So it's kind of an additional piece of data you can throw at it to really make sure our maps are more certain. - I wanna go back to some terms you've said a few times, which are galaxy clusters and galaxy clumps, because when I was reading about this Dark Energy Survey, I found this really interesting that galaxy clumpiness is something that people actually say in a lot of this work. Can you tell us why these are useful terms to look into and define?
- Saying clumpiness, and as you say, a lot of people use it, is when we're describing structure in the universe, you know, we've got this story of the universe of, like, once upon a time, the universe was denser and much more uniform, and over time, those small fluctuations in density grow to form structures, and the properties of those structures and how fast they grow depend on the physics of gravity, it depends on how much matter you have.
If you turn up the amount of dark energy and the universe expands faster, that kind of acts against the pull of gravity, so, like, the rate that structure forms in the universe depends on the properties of dark energy because it influences the expansion. And so I guess I'm using clumpiness or clumping as like a shorthand for the statistical measurements we can make for how matter is distributed in the universe.
You know, sort of a key piece of information is just, like, how big are the density fluctuations. And by that, I don't mean like if I hold up a ruler to them, how far apart are they? I mean, like, how much density deviates from the average density and how that varies when you look at it in space, you can kind of make a statistical measurement, which is, like, a statistical term would be you'd measure the variance of the density.
That variance will be small if the universe is very uniform where the density is close to average everywhere, but if you have a big clump in one spot and a void in another spot and there's an extreme difference, then this variance of the density will be higher and sort of the universe is less uniform or clumpy. - There's numerous teams that are part of the DES, the Dark Energy Survey. Can you go a little bit more in depth about what you specifically are trying to do with this work?
- The working group within DES that I'm part of is called Theory and Combined Probes, which I help work on putting the pieces together that we need to use to be able to make the model predictions that we compare to data, and then, you know, doing that comparison and doing the fits and making all the plots and trying to make the plots pretty and all these kind of things.
Like I was mentioning, when we have the two maps from, say, the CMB and weak lensing in the galaxies, having those two measurements of the universe that you can put together, use them together, it's greater than the sum of the parts 'cause you can get extra information by combining these measurements. - Are they considered probes, those different maps? - We use probe just to refer to, like, different kinds of measurements.
And I've been mainly working on, the last couple years, combined analysis of galaxy clustering, so, like, do galaxies tend to be close together or far apart and how are they distributed, and the weak lensing, so the distortions to the distant galaxy shapes. You know, I was talking about those paired measurements where you look at the distances between pairs of galaxies.
You can do an analogous thing by looking at how aligned are the shapes that we see of distant galaxies as a function of how far apart they're on the sky. So if you have much more clumpy matter along the line of sight, you'll get more of this weak lensing, and that'll cause the shapes of distant galaxies to look more aligned. Whereas if the universe is fairly uniform, you won't have much lensing and the shapes will look pretty randomized on the sky.
So those are sort of two different of these kind of measurements we can make using pairs of things, and then there's a third one where you can say, all right, I've got these positions of galaxies that are in the clumps of matter that are doing the lensing and then the shapes of galaxies behind them, and so putting those things together gives you some extra information. We've got three kinds of measurements we make from two kinds of maps, and all of that together is combined probes.
- And I know you've said that in the analysis you do, bias is something you have to be careful about in different forms, and we had a question about this that was sent in from Estefania, who's a student in Texas. - I've noticed your emphasis on the refinement of position cosmology. How has your research sought to alleviate potential sources of bias in cosmological analysis? - I think that's a question that I spend most of my time worrying about, so it's a good question.
There are a lot of ways that we approach this, and so there's not one panacea. It's a lot of trying to think of all the possible ways that bias could enter our analyses and trying to test for them and make analysis choices to help protect us against them. So one of the key things that we do is we try to make as many choices about our analyses, like what length scales are we gonna use in comparing our model to measurements is, like, a very key one.
We try to make a lot of those choices based on simulated data. So the sort of simplest way we approach that is, you know, we've got our machinery to do a model prediction for the observables we're gonna measure, so we pick an input set of cosmological parameters, an input model, we make our model prediction, and then we treat that model prediction as if it's data and analyze it using our planned analysis.
And the reason why this is nice to do is 'cause you know what the truth is, you know what cosmology that you computed it with. And so you can make sure, like, if that were the data you measured and you were to go analyze it using your parameter fitting methods and what length scales you're comparing model to data on, you get out what you put in.
- So you're essentially creating a simulation for yourselves to make sure that what you get out corresponds to what you've created, even though that's not the actual data that you're working with. - Exactly. - You're making sure that you can trust the data when you get it?
- Exactly, and then we can sort of take that a step further and say, all right, we know that our model prediction has some approximations and we had to make some choices over, you know, which software to use and what settings to use. Generally, the more accurate you wanna do or the more detailed physics you wanna put in, the slower your calculation is.
And, like, in practice, we can't do the really slow versions for every single comparison to model to data, or, you know, there might be some physics we just know that we don't know how to model. So I was talking earlier about the effects of, like, galaxies and supernova pushing gas out.
On, like, cosmological small scales, that's very uncertain modeling and sort of figuring out feedback, we call it baryonic feedback, so supernova gas, stars, dust, galaxy messiness can have a feedback effect on the large-scale structure that we don't know how to model. Characterizing that is like cutting-edge cosmology that people are debating and figuring out actively.
- I like that what most people, I think, consider the real stuff of the world, you know, stars and matter and animals and trees, you're, like, eh, it's messiness, that's getting in the way. - Exactly.
So I was gonna say, like, one thing that we can do with these simulated analysis is we can go get what's sort of a large-ish but plausible amount of this, like, baryonic, this supernova feedback stuff that could influence our data that we know we're not modeling so we can't model it well, and we can look at if that was real, so we'd throw out a lot of our small-scale data points to, like, make sure we're not sensitive to that.
So we use these simulations where the simulation is done with a more complicated model than what we're fitting with, and we can make sure that we're not gonna falsely detect that dark energy is varying with time when it's just that galaxies are hard to model. So that's one form of bias. Like, we're trying to find the true value or a range of values where the true value may live for our cosmological model, and we wanna make sure those estimates have the true number in our error bars.
One way that we talk about bias in cosmology is, like, some effect that you're not modeling correctly pushes your inferred parameter values around enough that you might try to measure, like, a parameter described as dark energy time dependence and it might move away from what the true value is because you haven't accounted for something in your model. We also try to account for and protect against something that we call unconscious experimenter bias.
As scientists, we try as hard as we can to make all the decision that goes into this analysis, what points to measure, what choices to make for our model as objectively and in response to these simulated analyses as possible, but, ultimately, you know, science is done by people and people are subject to all kinds of pressures and assumptions and we might be interested in seeing how our measurements are relating to previous measurements or, like, there are special values in the parameter space,
like detecting if dark energy varies in time, it's a very different result than if it's constant in time. And so you wanna make sure, if at all possible, that, even subconsciously, our decisions on how to do the analysis aren't influenced by whether the results agree with our expectation. And so we use a, we call it a blind analysis framework.
Exactly what that means depends a lot on the experiment, but, like, the main thing in principle is you make sure that you're not looking at your main results until you've frozen in all the decisions to get there, and you hope that nothing unexpected shows up after you, like, reveal the results.
In practice, things are not always that tidy, but generally, part of this is if something does change or you find something afterwards, we really try to be rigorous about, like, documenting it and being clear of, like, what decisions were made before versus after unblinding. So it's kind of a similar motivation to if you hear about in, like, medical fields, like double-blind trials where you test a new medication against a placebo.
Like, in those experiments, neither the patient nor the doctor knows which is the real pill and which is the placebo. And you do that because you don't want sort of expectations of whether somebody's gonna feel better or worse to, like, influence your interpretation of some very complicated phenomena then.
- I guess I just assume that that kind of blinding was done in medicine and the more, I don't know, human-scale sciences, and that when you're dealing with the universe at these enormous scales and galaxies, my assumption was that, you know, that's objective data and it's observables and you don't need to do that, but clearly this is something you need to be aware of.
- Even though, you know, we sort of guideline and try to be as transparent as possible about how choices are made, there are choices that need to be made. So, like, for example, we use these simulations including all these messy galaxy physics, and we wanna make sure that our cosmology inferences about dark energy aren't biased by that. But, like, how do you quantify that? What amount of bias is little versus enough?
And, like, you have to set a threshold and decide exactly what numbers you're gonna look at to assess that, and, you know, there's sort of things that are better choices than others in sort of a broad sense but when you get down to the specifics, you wanna motivate things, but there's a certain amount of arbitrariness that does come into it, and so we wanna make sure that, yeah, if we're making that choice, it's not informed in any way by, like, what the science
coming out the end of the pipeline is. It's part of the structure of the whole analysis within our collaboration and in, you know, many cosmology analyses. So, recently finished a big analysis, and sort of one of the dramatic stages at the end is you write up everything you did and all the tests you do and have some collaborators who are experts but not directly involved in the project look that over and say, "All right, I think you've checked everything you needed to check.
You have our okay to reveal your results or unblind them." And so it always feels like a bit of an event, kind of a nerve-wracking event when you, like, look at the results for the first time. So in that sense, it's definitely active. But, yeah, helping develop the sort of technical method for hiding the results from ourselves was my first project in the Dark Energy Survey as a graduate student.
There's varying degrees of technical manipulations you can do, 'cause the trick is you wanna hide the results for yourself, but you wanna give yourself enough access to the data that you can test for all the things you need to test for.
And that ends up being a pretty tricky question sort of on one extreme end of, like, not doing very much technically for this is just you all agree as a collaboration, like, we're not gonna look at plots of these parameters or something like that, which, like, does work for your purposes, but also, when you have a big collaboration and, like, it can be nice to have something a little bit harder to accidentally peek at.
The method that I worked with some collaborators to develop and test and implement actually transforms these statistical quantities that we measure from these three kinds of statistical measurements, and we figured out a way that you can transform them that, like, still keep them all consistent with one another so they look like they came from some valid universe, but it looks like they came from a different set of cosmology parameters. So we have these, like, transformed statistic measurements.
Most of the other collaborations that are sort doing similar analyses, they have some mechanism for this kind of transformation of data on some level. And I know in one of the other sort of weak lensing surveys out there, they have a much more technical, like, encryption double key sort of way of doing this.
It's the technical aspect of how can we transform the data and make sure we preserve the access we need to preserve, and then there's also, like, how does your collaboration work as a group, and, you know, how do you decide when to reveal the results, and what do you do if something unexpected comes up? And, you know, this maybe also ties into other ways that bias comes up in conversation of, like, personal dynamics in collaborations and getting large groups of people to work together.
And so it's a challenge within any collaboration and also, like, looking forward to next-generation galaxy surveys, which are gonna be even bigger, of, like, how do you make sure everyone has enough information to understand what tests are done? How can you make sure everyone's voice gets heard when you're having these conversations? Often, when people are kind of stressed out and pushing for results, it's an organizational challenge as well.
And I think one additional benefit of these sort of blind analysis frameworks, in addition to, you know, helping make sure that you have the most robust and accurate science as possible, is it's kind of a little bit of a sociological break. It's like if you all need to decide that you've checked all the things you need to check to look at the results, I think it functions very well as sort of a pause for a collaboration to say, like, we've been sprinting towards the end, let's take some time,
take a week or two. - Take a breather. (laughs) - In the same way as developing, like, modeling and data analysis techniques, we're sort of a laboratory for future analyses, these sort of blinding analysis and strategies for how to make decisions and how to organize people I think is another thing that we learn a lot from and see what works and what could work better. And that's very tied in with the science of how these large collaboration works.
And these large collaborations are hard, we gather enough data and do the work we need to, like, figure out what the universe can tell us about dark energy, so it's really crucial that people who are interested can contribute and feel like their work
is valued and important. - It seems that a lot of your work also pretty fundamentally relies on understanding this interplay between experiment and theory, so I'm wondering if you can tell us a little bit more about that and how experiments can help us improve theory and theory can help us improve experiments. - So I think cosmology as a field is really defined by this interplay.
You can go back towards sort of early days of cosmology where, you know, Einstein developed general relativity and had this assumption that the universe should be static. And when you look at what the equations tell you about the universe, it tells you it's gonna be expanding or contracting, so we, you know, stuck a constant in the equation, and if you tune it to a specific value, given the other properties of the universe, you can get the universe to not be expanding or contracting at all.
And then just a few years later, Edwin Hubble measured the fact that the universe was accelerating, so they throw out that term, it's not needed, you know, we're gonna expect to find the universe that's decelerating. And then, you know, you get to the '90s when people go and measure that, and you realize, oh, it's actually accelerating, which brings the constant back but tells you it needs a different value.
And there's countless stories within the field where the data tells you you need some aspect of the theory, and then now, dark energy could be a cosmological constant, and so far, sort of all the observations we've made of the universe seem to prefer that or there's not evidence for some other property, but we don't think that's the whole story. And why don't we think it's the whole story would be a reasonable question.
So, you know, this cosmological constant would be some, like, vacuum energy, and we can look to particle physics colleagues down the hall and they predict that there should be some vacuum energy. It's difficult to predict, but if you kind of make some estimates based on our knowledge of particle physics of what the value of that energy density should be, you get a number that's, like, absurdly larger than the number we measure.
So given, like, particle physics energy scales, the value of this energy density we find is, like, very tiny but nonzero. And so you want to know why that's the case, and so there's a lot of work being done by theorists to think of different models that could explain this. Or you might ask, like, could the universe be accelerating not because there's some extra substance but because we need to extend general relativity on large scales?
And then you can say like, all right, but how would that manifest in the universe? Those models are predictions for, like, ways that you could extend your description of gravity beyond general relativity while still respecting all the very tight constraints we have on gravity from, like, measurements of the solar system and lab experiments, sort of gives you a set of effects that you can go look for.
My team within the Dark Energy Survey that I co-lead with another postdoc who works at the Jet Propulsion Laboratory for NASA in particular focus on taking these different proposed models for, you know, maybe different ways you could model dark energy or modifications of your theory of gravity and going and taking our galaxy cluster and weak lensing data and testing those extensions to the sort of simplest description of the universe.
In a similar way to when we constrain properties of the simplest model, we can vary the input parameters describing these kinds of modifications of gravity or dark energy properties and place sort of limits on what those parameters are allowed to be.
Part of this big analysis we just finished was testing a set of six of these kinds of models, and seems like the sort of simplest cosmological model lives to fight another day, given our data, what's the largest amount of, like, time dependence that dark energy can have in some range. - That connection between theory and experiment is something that you very tangibly had because you've not only worked on the theory side but you actually went to the telescope, right?
- One benefit of working in a large collaboration that's trying to do over 700 nights of observing over the course of six years is they needed people to do shifts on the telescope. Some observatories, I think, in next-generation survey, they're doing a lot more, like, remote observing, but it can be helpful to have people in the room. So I did two observing shifts for DES.
You fly into a little beach town and then ride a van for three hours into the mountains, and you stay in a astronomers' dorm with, like, a little cafeteria and go work on the telescope every night. - After you told us about it at first, I looked it up. I wanted to see what it looked like, and it looks so much like what I pictured, you know, this classic dome-shaped observatory, but then there's these barren, there's a few buildings around at the top of this mountain,
but then it's sort of barren. - Yeah, it's a desert. - Yeah, what's it like to go to the top of a mountain and live in an astronomers' dormitory? It seems like such a unique experience. - I think it's probably one of the most, like, incredible experiences of my life, and I feel very grateful that I got to do it, especially because, you know, I usually work with data that's in a very, like, processed form, and so this is a very different way of interacting with the experiment.
- That's data as it's pouring in in real time from the universe, right? - Yeah, so each exposure with the Dark Energy Camera is like 30-second exposures, and you see, like, the raw image of the different, like, chips that make up the CCD that measures the image. And so they pop up on the screen as they come in, and the thing that I find really striking is just how messy they look. So you see a lot of noise, you see, like, streaks from satellites going through them.
One of the shifts I was on, there was a bit of dust on one of them so we spent a lot of time trying to figure out if a little squiggle was something we could do something about or not. - So even a mountaintop is not completely free of distortions and issues to deal with. - Exactly. And there is a lot of work that goes into combining multiple images to beat down the noise.
There's ways of correcting, you know, so you can look at the shapes of, like, stars, which are, like, in principle, from our point of view, like point objects, and people look at how their shapes get distorted, and there's a lot of complicated modeling to correct for that kind of distortion. And also, like, the optics of the telescope might be slightly different towards the edge, towards the center.
The science, the dark energy constraints we do, would not be possible with all that hard work and technology development and analysis development of my many colleagues. So this is really a team effort and is not something that's possible to do without a big team of hardworking people, and I think getting to go, you know, sit in the control room and sort of see the early iteration of the data I think felt very valuable to me in that sense.
- I'm fascinated just by that idea of going to work at this telescope in this remote location. Aside from looking at the data as it comes in, what do you do when you're on top of the mountain?
- So generally, there's a 4:00 PM meeting where you get on Zoom with people at Fermilab who, like, manage a lot of the telescope operations, and you check in about, like, what the plan is for the day, get everything set up, you go eat dinner in the astronomers' cafeteria, you come back, you get, like, the various scripts queued up that you're gonna run, and then you just have to wait for the sun to go down.
And so, like, kind of part of your job is to go, like, "Well, there's nothing we can do in the control room, we're gonna go..." Everyone goes and watches the sun set over the ocean, and you're on a mountain that's somewhat taller than all the other mountains, and usually it's very clear and it's just very beautiful. And there's also these little rodents called viscachas.
They look like rabbits with squirrel tails that also seem to come out and watch the sunset so you're always kinda looking for those. (Colin laughs) And then, yeah, during the night, you're kind of keeping an eye on the images as they come in, making sure that nothing's going wrong. You also are supposed to monitor how much cloud cover there is, and it can be detected to some extent with instruments.
But, like, part of your job that you do sort of a little report is you're supposed to step outside and let your eyes adjust to the dark once every hour. So as you would expect from somewhere where you put a telescope, like, that's some of the most stars I've ever seen in my life. So you can see the Milky Way super clearly, especially when the moon is down, you can see the Magellanic Clouds, and it's just like you're kind of like alone on a windy mountaintop, it makes you feel very small.
- I wanna go back to asking you about the way you summarize this result that has recently come out of this Dark Energy Survey collaboration. You said this, I think you said the Lambda-CDM model survives another day, or maybe another way to say that is some relatively simple model passes another series of tests. And, you know, maybe on the surface, this result could seem not so exciting 'cause we're not announcing something big and new that we couldn't expect.
But I think it must be pretty incredible to think that all of this observation time, all of this noise and dust and clouds that you had to account for with so many people over so much time, all of that was done and, in the end, something pretty simple can describe all of that, and I'm just curious to get your perspective on that. Do you find that simplicity exciting? Or do you find yourself wanting to find something new?
- It is both exciting and frustrating because, so we have the simplest model, so, yeah, Lambda-CDM is sort of the maybe somewhat jargony name that we often refer to this, like, simplest model as. So Lambda is the symbol that we usually use to represent the cosmological constant, so this simplest description of dark energy. CDM stands for cold dark matter, which is, you know, this matter that doesn't interact with light but clumps up under the influence of gravity.
It is a real achievement of the field that we have this model that we can use to describe pretty accurately basically all of the observations we've made of the universe. There's a few exceptions that are debated, but as I said earlier, it's not the whole story. Like, we don't know what dark energy is and we don't what dark matter is, and together, they make up 95% of the stuff in the universe.
There are a lot of different models or descriptions that people consider that, you know, could dark energy be like this or that, or might dark matter have a little bit of interaction, or what kind of particle makes it up. For neither of these things, there is not a, like, clear front-runner, like, oh, this must be it.
And so there's a lot of, like, very important work being done on the theory and to think of different possibilities, But, ultimately, on the data end, what we're looking at is trying to make more and more precise measurements of this simplest model Lambda-CDM and kind of look for, like, cracks in the facade or places where the predictions of the simplest model don't match our observations because if we find a mismatch that holds up as our data get more precise,
maybe holds up if different teams measure it and make different, like, there's all these ways that, I think if we start seeing hints, we'll wanna really make sure what we're seeing is a hint of physics and not of some modeling assumption we don't understand well. But ultimately, we're looking for mismatches that will give us a clue for how to build a more fundamental understanding of 95% of the universe.
So it's frustrating that the results match that because it'd be very exciting if we found, like, a clear hint for something, but, you know, it's all part of the process. Like, we can narrow in on, like, what kinds of models are allowed or not allowed or at least, like, what are the ranges of the size of effects that deviations from general relativity on large scales might have.
In my mind, a concrete example is, like, one of the common things you can sort of study if you're looking for deviations from the prediction of general relativity is that theory will give you a specific relationship between the way that light interacts with the gravitational potential, so causing that gravitational lensing, and the way that gravity affects matter, like particles with mass, so the galaxies and dark matter clustering up.
If you're assuming general relativity is part of your model as you are in Lambda-CDM, putting those different kinds of measurements together lets you really get precise constraints on the parameters or the properties of that model.
But if you relax that assumption a little bit, you can say, all right, we're looking at the same sort of structures in the universe and we're seeing how they affect light and how they affect matter, and we can use that to test whether or not they have the expected relationship. And like a weak lensing survey like DES, and particularly, we're making both measurements of the lensing and the clustering, lets us make the most precise version of that kind of test available.
General relativity seems to be doing very well. (Jessie laughs) (Colin laughs) - Yeah, it seems to be standing up to a lot of the tests that it's being put under, which is pretty amazing for a century-old theory. - Very much so, yeah.
- I was looking around your website, learning about the Dark Energy Survey and your role and your past, and I have to say I enjoy, on your website there's a tab that just says Cartoons, and you click Cartoons and there's these illustrations that you've made of some pretty cool scientific concepts in a really sort of fun, bright, engaging way.
And one I keep thinking of as you're talking is there's a person at a desk in a room, I'm assuming maybe it's you, maybe it's, you know, it could be anybody, but they're wearing, like, VR goggles. What they see is this beautiful expanse of galaxies and swirls and stars and things, but, really, they're at a desk in a room and there's a cat sleeping on the bed nearby.
And so I wondered, A, if that's you, and B, more generally, can you tell us about your artwork and how, you know, I think you're the first person whose academic website I've gone on and it has a tab that says Cartoons for all their artwork. How did that come to be? - I have spent a lot of time in the last couple years working from home with a cat sleeping on my bed so that is an accurate representation. - OK, so that one's accurate. Is that a self portrait, the person in the VR helmet?
- No, not necessarily, but it was inspired by my roommate who I shared an apartment with during the pandemic who would play a lot of VR games in his room. So, yeah, that cartoon was part of a series that I did with some collaborators in DES. We released sort of the first round of the cosmology results from the galaxy clustering and weak lensing measurements from the first three years of DES data. So I guess that's something I didn't mention when talking about the project before.
We've analyzed the first three of six years of observations, and we're just getting started on the next round now. Yeah, when we were releasing those cosmology results, there's the main cosmology paper, but there's also like 30 other papers documenting all the work and tests and things that go into making that measurement possible.
And we were talking about how, you know, we've got the Dark Energy Survey, like, Twitter account and things, like it'd be fun to try and, like, highlight these works and try and figure out a way to make them a bit more accessible to the general public even if, you know, people aren't gonna go open up a PDF of a very technical paper about measuring, like, galaxy distances or something.
A couple of years ago, my colleague Chihway Chang, who's now a professor at Chicago, she had done this series of, like, one cartoon a week about different science concepts, and so we decided it'd be fun to revive that to illustrate these like 30 different papers. So we kind of split them up and got the authors to help us write sort of a little, like, blurb description of each of the papers, and then we tried to figure out ways to illustrate them.
So that cartoon that you're mentioning was the one I drew for a paper describing some simulated analyses, so the idea that we kind of used simulated data, analyzed it as, like, a test run for our analysis. And so partly 'cause my roommate during the pandemic was doing a lot of flight simulators on VR in his room during the pandemic, and so that was kind of the inspiration there. I'm just kinda trying
to think of fun things. - Yeah, my first thought was flight simulators, and even earlier in this conversation when you were describing the simulation process and why you do it, I thought, well, it's similar to why pilots take flight simulators 'cause you don't wanna crash the real plane unless you know what you're doing, right? - Exactly. - You do the simulations to figure it out. There was one other that I have to ask about. There's one other cartoon of two volleyball players.
One is setting the ball, the other one's about to spike it over the net. And I didn't fully grasp the science behind it, but I think, you know, these things, they're meant to invite people in and and learn more, so can you tell us what the volleyball players are doing? - That was to illustrate one of the papers that starts combining these different types of measurements. So we've got the map of galaxy shapes, we've got the map of galaxy positions.
You can either look at pairs of galaxy positions, pairs of galaxy shapes, or the cross-correlation, pairs where you have a shape and a position. These statistical things I'm talking about, we call them correlation functions, that's the technical term. That was meant to illustrate that analyzing these types of measurements together gives you information that you wouldn't get by analyzing them separately, so it's this kind of combined probe analysis idea.
- Team sport. - And so the volleyball thing is to say they're working together, it's teamwork to get the ball over the net or to tell us what dark energy is acting like. - I don't wanna ask you to describe your art in words too much 'cause I know everyone should also go look at it, but I also have to ask you about the platypus comic.
(Lauren laughs) (Colin laughs) - One of these cartoons is a little, like, three-panel comic-looking thing that has a bulletin board like you'd see in, like, a detective movie, so you've got photos on it with, like, string. So the scenario is you're trying to learn about what an animal is by getting, like, photos of different parts of the animal.
You know, you have a photo of a foot that's like a webbed foot, and you have a photo of a nose, which is a beak, and so the working model, sort of the simplest model, Lambda-CDM, is that it's a duck. Then you go, and a lot of what we're doing in cosmology is going and making either more precise measurements, which I guess would be like a less blurry picture of your duck or imaging different aspects of the animal.
So the second panel of the comic is the detective gets a photo of the animal's tail, and instead of looking like a duck tail, it looks like a beaver tail. If the new data doesn't match your expectations of the model given your previous data, that might be a hint that you need to develop a new model for your description of the universe or, like, what animal you're looking at.
And so in this case, the new model is a platypus, which has a duck-like beak and webbed feet and a tail that looks like a beaver tail. So that's sort of the analogy for kind of what we're doing and trying to test Lambda-CDM by looking for sort of mismatches between its predictions and our measurements. - Has it been useful to you as a researcher to take these long papers and try to condense them into these short comics? - Yeah, I think so. It's definitely a fun brainstorming process.
You know, with this set of like 30 papers, like, everyone's working together, but there's definitely some that I contribute more directly to than others. And so for doing illustrations for all of these, it was kind of fun to navigate the project and try and help authors come up with, all right, what is the one- or two-sentence sort of hopefully accessible description we can come up with?
So it helps me have a clearer understanding of, like, the core concept behind a number of my colleagues' papers that are very important for my work but I might not be, like, deeply familiar with the details. And then for things that are more closely related to what I work on, so, like, model testing by looking for mismatches between model and data, or platypus hunting, I guess, it's just kind of fun to think through and, like, come up with analogies like that.
And, I mean, it was also, like, one of my goals over the last couple years was to learn how to do digital art on an iPad, and this was was a very good project for learning how to do that. As an added benefit, I now use a lot of these cartoons when I give talks. - Have you always been artistically inclined? Have you always expressed yourself through drawing as well? - I've definitely had it more as a habit at some times in my life than others, but, yeah, I always liked to draw.
I mean, I like drawing in general and find it relaxing and enjoy doing it. I think a thing I struggle with especially, I think we all, in the past couple of years, have a little bit of, like, pandemic-related burnout so it's a little hard to, like, find motivation or ideas during downtime. And I think particularly this, like, science cartoon project was very nice 'cause it was a little bit collaborative and then it sort of seeds a bunch of ideas.
And, like, once I have an idea, like, the sort of type of mental energy used to, like, plan and figure out a drawing, it's like a form of problem solving, but it's a different kind of problem solving than, you know, working on a scientific analysis or a calculation. So it's kind of fun to bring those things together a bit and to, like, get to share them with both collaborators and the general public. - Colin talked about how unique this Cartoons tab is on your website.
I wanted to tell you something else that stood out to me on your website, which is that right on your homepage, you start by giving, you know, a brief description of your research, and then right after that, you write, "I'm also interested in science outreach and in making STEM fields more accessible and welcoming to everyone." And we actually had a question sent in about this sentence on your website. - Matt Duschenes, a PhD student at Perimeter.
I'm wondering what barriers have you experienced while trying to make science more accessible and more diverse? - So the main way I have engaged with this, it's varied depending on different stages of my career, and sort of recognizing the existence of barriers and the ways that those can manifest was definitely a progression. Like, you know, I look back at being an undergrad student, and I had several classes where I was, like, one of two women in the room.
And at that point, I don't think I would've identified anything necessarily as a barrier. The social dynamics, I think I mostly experienced that, and then a bit during my master's is just being a little bit of like an isolation. There are more concrete and more abstract ways that that can manifest, and, you know, they impact different people differently.
Like on one hand, I may have been one of the only couple women in my physics classes while also recognizing that I was being supported partially by my parents in undergrad and so I could go work in a physics lab and not have to, you know, work other jobs after class. You know, so there are some ways that isolation can crop up and can become barriers. Definitely have had at least a couple interactions with professors assuming I knew less than I did, almost certainly a gendered point of view.
But, you know, there are other ways in which I, you know, was privileged and had this access to, say, this research program and had the support to, like, go to Europe for a summer and do physics research. So there are ways I've faced barriers, but also ways that I have not had barriers that other people might have.
And I think in grad school, I had a big learning experience with this in that I helped organize the Society for Women in Physics at the University of Michigan for most of my grad school career. A big focus of that was, you know, just building sort of a community within the department for support and mentoring, which, honestly, I think can benefit everyone in academia, but especially people who might feel a bit isolated or face some challenges.
And I think a big part of that learning experience was, often, we would also communicate with and work jointly with other student groups on campus. For me, it's an ongoing learning experience of recognizing ways in which, you know, I might have faced barriers or ways which people might face barriers that aren't me.
So, like, things like making sure that these kind of summer programs have enough, like, financial support that a student who might otherwise need to work a job can, like, participate or trying to set up programs where, you know, you don't have to be in the know to go seek out a research experience that, like, might change the trajectory of your career. So I think that kind of thing is important.
And, you know, also thinking through these collaboration dynamics of, like, if you have a bunch of stressed out people who are trying to pay attention to too many things at once, that's, like, a prime environment for well-intentioned people to make others feel excluded, which I know I have been guilty of and, you know, I think we're all trying to work on it, and so there's a lot of discussion within, you know, Dark Energy Survey and other collaborations of, like, how can we make sure
people who are new to the experiment or people who are not white or women or other gender minorities, like, can feel supported, can find community, know who to ask for advice and, you know, can feel heard in conversations, recognizing that not everyone communicates in the same way. - And I know here at Perimeter, you've become pretty involved in outreach and in mentoring and supervising students at more junior stages. What motivates you to be involved in that kind of work?
- I mean, kind of selfishly, I enjoy it. I think I'm happiest doing science when I'm, like, chatting with other people about it. You know, all these labs that I worked in, I also did a little bit of galaxy cluster cosmology in undergrad as well. And, like, all of the professors I worked with or more senior undergrads or grad students that, like, helped me learn how to do computer...
You know, it's a learning process along the way, and different mentors have made an impact on the trajectory of my career, and so the idea of being able to, like, support and introduce other people and help them feel supported feels important. - That trajectory of your career, where do you see or hope it's headed next? You know, this is ongoing work with the DES. - Well I'm gonna be on the job market for faculty jobs in the next couple years so.
(Jessie laughs) (Colin laughs) Yeah, I would like to keep doing cosmology research. I would like to be able to teach as well and keep mentoring students. This analysis team that I've been co-leading with Agnes Ferte, who's another postdoc in DES, we've led this analysis extending the year three analysis, which we call it, to extended cosmological models, models beyond the simplest one. The analysis of the full sort of legacy data set for DES, the year six analysis is ramping up.
I'm gonna be taking a little bit more of a backseat. Like, I'm still gonna be contributing to different pieces of validation for, like, the Lambda-CDM analysis as well as the extended models. Some people who are on our team during this year three analysis are stepping up and are gonna have a chance to lead the group as well now.
And part of this analysis, there'd been a lot of patches where we realized, like, oh, this modeling tool that we would need to do this just doesn't exist, and so we, you know, kinda have to find ways to work around that, and so there are a couple of these things that were not workable on the time scale of that analysis, but with a little bit more work, I think, are gaps we can fill to let us do a more precise analysis of the data we already have and also get it ready for our next analysis.
So one of the students I'm supervising here at PI as a summer student, we're working on one of these projects. And I was just at a meeting where I was discussing plans with a grad student about sort of extending one of these other analyses, so there's sort of more direct spinoff projects and then I also want to get a little bit more involved in sort of the next-generation survey, which is called the Vera Rubin Observatory LSST. - That's sort of the next evolution in precision or in power?
- Yeah, so it's gonna be turning on I think in the next year or so. It's, like, on the next mountain over from where the Dark Energy Camera is. Many times we've heard, over the course of six years, that the LSST is gonna image as much of the sky as you can image without the Milky Way getting too in the way. It's also on the ground, so the half of the sky it has access to, like, basically every night or every two nights.
Like, it has an even bigger field of view than DES and will be able to get more precise data looking at fainter galaxies and making more precise measurements of shapes and other things.
You know, maybe outside of survey science as well, you know, if I'm overcounting my free time, look for more theoretical projects looking for, like, what are other ways we can use this data or, like, the fact that I'm interested in theory and have this experience working with data, compared to your average theorist, I have a good sense of the ways that which data is messy and tough and so, like, when you try to bring those things together, things that you don't wanna have to care about,
you might have to care about. So I'll probably continue working at the interface of that, both, you know, looking for ways we can get more information out of data we already have and also making sure that when we do that, we're doing it carefully and robustly. - Well, thank you so much for taking us on this journey. There's so many things I didn't know about and so many things that I just find fascinating and at scales that are just mind boggling.
And I hope you'll keep us posted on the next stages of this experiment and the ones after. - Yeah, that would be great. It was great talking to you. (bright music) - Thanks so much for listening. Be sure to subscribe so you don't miss any of our conversations. We've interviewed so many brilliant scientists whose research spans from the quantum to the cosmos, and we can't wait for you to hear more. And if you like what you hear, please rate and review our show on your preferred podcast platform.
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