Jessie Muir on the mystery of dark energy | Conversations at the Perimeter

foreign [Music] [Music] hello Jesse 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 going to tell us about and dark matter all things dark because we haven't really talked uh to to any experts about what these things really are or what they aren't um can you 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 so the the yeah I think the main thing that relates them is that they have dark in the name and their labels that we give to um 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 um but we fundamentally don't know what they are but these are two different unknown things um and I think we can get into this in more detail but sort of the simplistic uh description I give of what makes them different is uh Dark Matter seems to be a probably type of some type of particle but it clumps up under the influence of gravity okay uh so it's not uniform in space uh 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's an ordinary matter does that well as well right it clumps in areas of high gravity that's yeah yeah so the the thing that gravity does is it causes Mass to want to fall towards other mass or to attract other Mass and it seems as far as we can tell that dark matter and ordinary matter so things that make up stars and galaxies and planets and US yes um 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 something that 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 in Dark Energy we we learn about and we've detected based on its influence of the very large scale universe so uh you know large and small scales kind of have somewhat different meanings depending on what what field you're in in uh in cosmology we tend to refer to as small scales as anything under about 30 million light years tiny so it's you know maybe a little bit different than uh the scales of like colleagues over doing Quantum Quantum stuff here at Pi if that's small what is large bigger than that a large number of light years so yeah I mean generally we work in a little bit of different units in cosmology but like 30 million light years is kind of want the Benchmark for once you're looking above that um the universe isn't necessarily uniform but in a statistical sense um it becomes uniform so if you're trying to describe I guess you can maybe picture like looking at like uh 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's 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 oh the This Grass tends to grow in like little clumps or this or is it more spread out or you know do we think it was grown there wildly or right using sod I don't know maybe this is getting a little bit 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 you know these different characteristics different way ways it grows and um you mentioned dark energy as a uh in comparison sort of being an element of space time is that right it's it's something intrinsic to it yeah so for for this maybe it's kind of useful to like tell a little bit of the story of how we learned about Dark Energy so up 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 uh 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 a theory of general relativity in early 1900s yeah um 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 the what you'd expect gravity be 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 right and so 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 what several teams of scientists and since many have confirmed in the late 90s was that the universe is 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 is like I know 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 got to be some other source of energy there and the the simplest description that we can come up with the dark energy could be that would give us the sort of observable properties that we're seeing is that if uh 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 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 the the reason they it's causing it's the 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 I love cosmology scales it makes my scale seem so small and so insignificant yeah 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 uh and that that matter gets diluted so as the universe is progresses through its history the matter density will drop and at a certain point the average matter density of matter in the universe drops below that vacuum energy that cosmological constant the dark energy density and that's when the universe starts accelerating so these different components have different influences on the behavior of the space-time and the universe and this is something we can get out of Einstein's general relativity um we can relate the behavior of space-time to the stuff in it and so when the the relative contribution to the total energy um density of the universe switches from being matter dominated to Dark Energy dominated or cosmological constant dominated depending on which model you want to want to use um the 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 you know 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 you think the how that should affect the expansion history how it'll affect how the matter is clumping up to form galaxies and things um and we can kind of test and constrain those um and that's a lot of the motivation behind what I and a lot of other cosmologists do it's fascinating it's 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 yeah I mean I think it's probably the case for a lot of my colleagues like I've never taken a statistics class it's been very much a learning on the Fly it's a lot of computer science along the way too as part of being a scientist for me um but yeah we can so it comes in a couple different ways one is you know if we're trying to describe the large-scale universe um 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 we'll be able to look and each Galaxy is made up of hundreds of or billions of stars yeah yeah but yeah Universe out there um and so we want to be able to to be able to test our theories or to constrain you know does the question of whether dark energies density like varies in time or not which is sort of one of the you know straightforward questions you can ask about that model um you want to find things about those measurements are making that you can actually predict with your theory and with our theory of the universe it's we're not able to say I think I'm going to 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 you know there's a whole story of you know where those tiny density fluctuations came with which I'll gloss over for now but um and then over time given our understanding of like what types of matter are contributing to those fluctuations and how gravity Works how they go 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 of fluctuations in density so like how how do you compare sort of the highest densities in the universe the lowest densities um you can make predictions about that and you can also in the same way that you know are Maybe tortured lawn of grass analogy like you might be able to tie like how you put the seeds down on the ground uh to how like clustered the grasses are you seeing grass and a bunch of little Tufts or is it pretty spread out uniformly we can make predictions for like do we expect to see Galaxy's distributed at random or do we expect to see them clumped together and we can make predictions for um basically the probability of finding galaxies separated by a given distance in the universe uh compared to an average distribution and so it's those so we're describing statistical properties of uh the distribution of matter in the universe and then statistics comes in another way is 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 so 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 you know to be able to do these calculations you need to make some assumptions but a lot of my my day and a lot of the work of my 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 want to make sure that we don't mistake some like complication in like how Supernova blow gas out of galaxies or something generally small scale cosmology link speaking where you have like galaxies and gas and stars things are much harder to model because there's more stuff going on there's more interactions and so we need to like one of our big challenges in cosmology is trying to make sure uncertainties about the detailed calculations of that you know smaller scale astrophysics or just Galaxy skills uh doesn't influence the the inferences that we're making from the larger scales or want to 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 you know maybe not understanding our modeling predictions so we we do a ton of tests we use a ton of simulations to really make sure that we do that rigorously and then trying like translating um you know these comparisons of model predictions data into information about parameters of a model or which model is better than another one is is a whole sort of subfield of study in cosmology itself yeah 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 I guess the sort of so until the answer is like both are great so like I there's a lot of value in um looking at what data we have on hand and looking for new ways to extract information out of it so like an example of that that is sort of a very active area of research which is something I have not worked on myself but I'm interested in learning more about is 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 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 skills and um and there's a whole field of research which these calculations tend to be a bit harder and the measurements to be harder of like understanding like or 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 more information out of that um but then yeah going and Gathering more data because the like 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 which having more galaxies is good for that um you can know that like 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 um and I think most if not all cosmologists are kind of engaged a bit in both of these things um so they're we're consistently planning like working on the current generation of experiments Gathering data and sort of looking to the the next generation of experiments which we'll be turning on and also there's sort of a lot of complementarity there because so the the experiment that I work on is a Galaxy survey called the 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 the biggest uh data set of of its type so it's the most statistically powerful um Galaxy survey of its type which we can maybe touch on in a bit uh and so the 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 um workshop for developing techniques will need when we go to the Next Generation experiment um which will get even more precise constraints and like you know I mentioned we have to spend a lot of time accounting for like are the approximations where you're using for 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 balance on that every time our constraints get or our data get more precise you mentioned the the dark energy survey the experiment that you're you're working on can you tell us sort of the the goals and uh motivations of that and how it actually works is is this uh a telescope out in space or on a mountain or is it something else entirely yeah so so yes maybe as a like basic definition a Galaxy survey is some experiment usually run by a 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 Galaxy 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 this guy so we can make these statistical measurements um the dark energy survey is what is known as an Imaging survey which means another tell us what 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 um but yeah I guess as we take we take images of the sky um and it that giant digital camera is called the the dark energy camera which we're very creative with names clearly um and it is on a four meter telescope called The Blanco telescope in ceratololo in Chile so it's on the top of a mountain um you put telescopes on tops of mountains because there's water in the atmosphere and like turbulence and the atmosphere can make images of space look blurry and so you want to go to where there's not much water in the atmosphere and there's not much atmosphere so generally observatories are on in deserts and on tops of mountains um Chile is a good exact choice 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 yeah sure so so Des or dark energy survey has uh I think about 400 people in it um it's it's been going for over a decade so I think that the camera was installed on the telescope in 2011. so this camera was built specifically for the survey um and specialized to be more sensitive to to Red Light than your average like chip that would be in a digital camera um and like the the CCD chips of the little chip that would be in your digital camera for the telescope is like three feet across that's big um so this collaboration worked something from planning the survey to building the camera to installing it to running the shift so we did something like 760 nights of observing between I think 2013 and 2019 wow um and so and then there's a whole team of people that go from sort of raw images from those digital the big digital camera and turn that into catalogs of where do we see galaxies what are their colors what are their shapes and then there's I mean 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 kind of at the sort of end of that is trying to go from those statistical measurements to inferences about the physics right um and I so I I've been talking specifically about measurements of Galaxy clustering um the the images we have also lets us map the distribution of structure in the universe using um 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 the gravity of what it's passing by yeah yeah so that's um yeah like a beam of light will get a bit abducted by a gravitational potential and you know if we're looking out over you know millions or billions of light years in the universe they're sort of structures in the universe I mean 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 technical term is large-scale structure 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 in the collaboration there's two teams that focuses on Galaxy clusters so like large groups of galaxies um 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 cosmology but the fact that you know we have 760-ish 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 five colors um it also is really good to see things moving so there's a whole group which I'm very impressed by what I am not part of but because he's finding like things like dwarf planets or comets in the solar system all from the same essential piece of equipment and exact experiment and I maybe this is a silly question but why 700 plus nights why so much observation and how much of this guy are you actually looking at yeah so the survey area it's uh covers about 1 8 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 because it's hard to see stuff behind it when you're looking through the the disk of our galaxy so are you looking perpendicular to the disc yeah sort of looking down and there's some some other patches added onto the survey footprint to increase overlap with other kinds of measurements so there are other experiments that map um the large-scale universe using light from the very early universe that was emitted when uh like in the first couple hundred thousand years of the universe when Adams first formed is this the cosmic microwave background exactly yeah and so there's a lot of information gained by analyzing those data sets together and so that's a whole working group or 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 the duration between that what's pictured in those Maps do you compare one to the other to show how things evolve and change over time yeah so so 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 this 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 the Galaxy shapes with weak this we call it weak gravitational lensing because it's like tiny distortions um that same Distortion affects the CMB lights you can use across correlation or like look at the relationship between distortions and the the cosmic microwave background light in the galaxies to be extra sure that the Distortion you see 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 to really make sure our maps are more certain I want to go back to some terms you've said a few times um which are Galaxy clusters and Galaxy clums because when I was reading about this dark energy survey I found this really interesting the 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 yeah so yeah I guess when I'm saying clumpiness 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 intensity 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 how like if you if you turn up the amount of dark energy in the universe expands faster that kind of acts as 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 um and so I guess I'm using clumpiness or clumping as like a shorthand for these statistical measurements we can make for how matter is distributed in the universe and you know sort of a key piece of information is it is just like how big are the density fluctuates and by that I don't mean like if I hold up a ruler to them how far apart are there I mean I mean like if you how much density deviates from the average density and how that varies when you look in space you can kind of make a statistical measurement which is like it's this statistical term would be you'd measure the variance of the density um but it just like that variants 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 a extreme difference then this variance of the density will be higher and sort of the the universe is less uniform or clumpy so that's what I mean when I say that um I remember the first time I heard someone use the term clumpiness when it was describing galaxies and I sort of chuckled and said clumpiness that's a funny word and they said oh believe it's a word we use all the time I didn't realize it was sort of a scientific term or at least it's it's used maybe also because I'm too afraid to say it try to say in homogeneity on a podcast oh so that's what clumpiness is in the uh more formal yeah um or we might say like amplitude of density fluctuations is what would more commonly come up right right I still prefer clumpiness but that's just me you mentioned um what some of the other teams there's numerous teams that are part of the Des the dark energy survey and what some of them are working on can you go a little bit more in depth about what you specifically are trying to do with this work yeah yeah sure so yeah I I work on the team so the working group within Des that I'm part of is called Theory and combined probes which you know so I help work on putting the pieces together that we need to make use to be able to make the model predictions that we we compared 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 um and then combined probes is kind of like I was mentioning when you have the two maps from say the CMB and Galaxy like re-cleansing in the galaxies having those two measurements of the universe that you can put together you sort of the them together it's greater than the sum of the parts because you can get extra information by combining these measurements are they considered probes those different Maps there yes yeah we use probe just to refer to like different kinds of measurements right it's like a fairly yep we use it fairly generically yeah like clumpy yeah um I've been mainly working on the last couple years on analyzes of a combined analysis of Galaxy clustering so like you know do galaxies tend to be close together or far apart and how are they distributed and the weak lensings of the distortions to the distant Galaxy shapes and so you can kind of do 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 or the the shapes that we see of just 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 different galaxies to look more aligned um whereas if the universe is fairly uniform the shapes 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 so we've got three kinds of measurements we make from two kinds of maps and all of that together is you know it's they're combined probes uh yeah 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 position cosmology how has your research sought to allevue potential sources of bias in cosmological analysis that's I think that's a question that I spend most of my time worrying about so it's a good question um so 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 analyzes and trying to test for them and make analysis choices uh 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 analyzes like what length scales are we going to use in comparing our model to measurements is like a very key one um 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 going to measure and so we pick an input set of cosmological parameters 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 the reason why this is nice to do is because you know what the truth is you know what cosmology that you computed it with and so you can make sure if like if that were the data you measured and you were to go analyze it using your parameter fitting methods and what like length scales you're comparing models data on um do you get out what you put in and then 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 it's 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 wow and then we can sort of take that a step further and say all right we know that our model prediction has some approximation then we had to make some choices over you know which software to use what settings to use and generally the the more accurate want to do or the more detailed physics you want to put in the slower your calculation is and like in practice we can't do the really slow versions for every single comparison of 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 on like cosmological small skills that's very uncertain modeling and sort of figuring out sort of what size those can the you know 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 a 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 as stars and matter and and animals and trees you're like oh that's messiness that's getting in the world so I was going to say like one thing that we can do with these simulated analysis is we can go get like what 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 because we can't model it well and we can look at if that was real what sort of scales like so we 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 model is doing something or the simulation is done in with a more complicated model than what we're fitting with and we can make sure that like we're not going to falsely detect that dark energy is varying with time when it's just that galaxies are hard to model right so that that's one form of bias like we're trying to find estimate the the true value or the range of values where the True Value may live for our cosmological model and we want to make sure those estimates have the true number in our inner error bars so one way that we talk about bias and cosmology is like some effects that you're not modeling correctly pushes your your met your inferred parameter values around enough that like you you might try to measure like the a parameter describing 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 um we also and I think this is some we also try to account for and protect against uh something that we call unconscious experimental bias so um you know as scientists we try as hard as we can to make all the decisions that goes into this analysis which what points to measure what choices to make for our model as objectively and responsibly simulated analyzes as possible um 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 want to make sure if at all possible that even subconsciously are decisions on how to do the analysis are aren't influenced by whether the results agree with our expectations interesting um and so we do our analysis and this is fairly common in cosmology it's been done in particle physics since the 90s and it's sort of been a growing thing and big cosmological experiments where we use a we call it a blind analysis framework where you 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 the decisions to get there and you hope that nothing unexpected shows up after you like reveal the results um 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 um so it's kind of a similar motivation to like if you hear about in like medical Fields like double blind trials where you test a new medication against the placebo yeah um you do that because you or you don't like in those experiments neither the patient or 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 going to feel better or worse to like influence your interpretation of some very complicated phenomenon I guess I just assumed that that that kind of uh blinding was done in medicine and the more I don't know human scale uh sciences and that when you're dealing with the the universe at these enormous scales and galaxies my assumption was that you know that's objective data and it's observables and and you don't need to do that but clearly this is something you need to be aware of and yeah and there are like even though you know we sort of guidelines and try to be as transparent as possible we'll have choices are made there are choices that need to be made so like for example we say we use these simulations including all these messy Galaxy physics and we want to make sure that our cosmology inference is 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 going to 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 want to motivate things but there's a certain amount of arbitrariness that does come into it um and so we want to 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 and you said that's a big part of what you think about and worry about in your work yeah I mean less the structure of the analysis Within our collaboration and in you know many cosmology analyzes so you know everyone's sort of working with it so I 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 need to check you have our okay to reveal your results or unbline 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 um so in that sense it's definitely active but uh yeah developing 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 um so it's kind of how I got introduced to the the survey of there's varying degrees of technical manipulations you can do to because the trick is you want to hide the results for yourself but you want to 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 so you know it's 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 going to look at plots of these parameters or you know something like that right um which like does work for your purposes but also when you have a big collaboration and like you it can be nice to have something a little bit harder to accidentally peek at um and so we have this the method that I worked with some collaborators to develop and test and implement actually transforms these statistical quantities that we measure from these you know 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 um sounds almost like you're encrypting them from yourselves yeah a bit okay uh and yeah they're like I know there's one other there's a I mean most of the other collaborations that are certainly doing similar analysis have some mechanism for this kind of transformation of data on some level and I know um in one of the other sort of lensing Galaxy cleansing surveys out there they have a much more like technical like encryption double key sort of way of doing this and so it kind of exactly how how this works depends a lot on you know it's a 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 there's a lot of like personal Dynamics in collaborations and getting large groups of people to work together and so it's a challenge within you know any any collaboration and also like looking forward to next Generation Galaxy surveys which are going to 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 or like you know people invest a lot of time and energy and so you it's an organizational challenge as well and I think one perk or one additional benefit of these sort of blind analysis Frameworks in addition to you know helping make sure that you have the most you know 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 it I think it functions very well as sort of a pause for a collaboration to say like all right we've been like sprinting towards the end let's take take some time take a week or two um and I think I mean in the same way as developing like modeling and data analysis techniques were sort of a laboratory for future analyzes they sort of blinding analysis and strategies for how to make decisions and how to organize people I think is a another thing that we we learn a lot from and see what works and what could work better and um I don't know I think that's that's very tied in with with the science of how these large collaboration works and these large collaborations are how we gather enough data and do the work we need to like figure out figure out what the universe can tell us about Dark Energy so it's really crucial that you know people who are interested can contribute and feel like their work is valued and 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 yeah so I think cosmology as a field is really defined by by this interplay um you know you can go back towards like 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 going to be expanding or Contracting so we you know stuck a constant in the equation cosmological constant um and if you tune it to a specific value given the other properties of the universe you can get the universe and not be expanding or Contracting at all um and then just a few years later Edwin Hubble measured the fact that the Universe was accelerating so they threw out that term it's not needed you know we're going to expect to find the universe that's decelerating uh 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 um and you know there's countless stories within the field where you sort of um the data tells you you need some aspect of the theory and then now you know Dark Energy could be a cosmological constant and so far sort of all the observed observations we made of the universe seem to prefer that or there's not evidence or some other property um but we don't think that's the whole story and well I guess why don't we think it's the whole story would be a reasonable question uh so you know this cosmological concept would be some like vacuum energy and we can look to particle physics colleagues down the hall and they they predict there should be some vacuum energy it's difficult to predict but you can't if you kind of make some like 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 dense we find is like very tiny but non-zero um and so you want to know why that's the case and so there's a lot of work being done by theories to think of different models that could explain this or you might ask like could the universe be accelerating that because there's some extra substance but because our description of gravity doesn't we need to extend it on our relativity on large scales um and then see you can say like all right but yeah how would that manifest in the universe those models or predictions for like ways that you could extend general relativity or just 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 experience sort of gives you a set of effects that you can go look for and then like my team within the dark energy survey that I 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 your Galaxy clustering week lensing data and testing those extensions to to the sort of simplest description of the universe and we can you know in a similar way to to when we constrain properties of the simplest model we can vary the the input parameter describing these kinds of modifications of gravity or dark energy properties and place sort of limits on what those parameters are allowed to be or like and thus far it is limits we have not part of this big analysis we just finished was testing a set of six of these kinds of models and uh it seems like the the sort of simplest cosmological model uh lives to fight another day uh so we but we can place limits on like the large given our data what's the largest um amount of like time dependence that dark energy can have in some range um and things like that connection between Theory and experiment is something that you very tangibly had because you uh you've not only worked on the theory side but you actually went to the telescope right yeah yeah so one one benefit of working in a large a Liberation that's trying to do over 700 nights of observing over the course of six years is uh they needed people to do shifts on the telescope um I think 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 um I did yeah to the top of the top of the world yeah so you fly into a little Beach town and then ride a van for three hours into the mountains and you stay in an astronomers dorm with like a little cafeteria and go work on the telescope every night I actually after you told us about it 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 uh Observatory but then there's these Barren Mount there's a few buildings around the top of this mountain but then it's sort of barren it's a desert yeah what's it like to go to the top of a mountain and live in a in an astronomer's dormitory it seems like such a unique experience yeah I think it's probably one of the most like incredible experience in my life and I feel very grateful that I I got to do it especially because you know I usually work with data that's in a very like processed form um 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 yeah so each exposure with the dark energy cameras like well it might be very it was mostly like 30 second exposures and you see like the raw image of like the the different like chips that make up the the CCD that measures the image um but 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 they see a lot of noise you see like streaks from satellites going through them um and I think 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 need could do something about or not so even a Mountaintop is not completely free of distortions and exactly yeah and there is a lot of work that goes into you know 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 um that kind of distortion and also like the Optics of the telescope might be slightly different towards the edge towards the center and you know the the science the dark energy contents we do would not be possible with all that hard work and Technology development and Analysis development of you know my my many colleagues so this is really a team effort and it's not something that's possible to do without a big team of hard-working people and I think getting to go you know sit in the control room and sort of see the early part of the data or the early iteration of the data I think is 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 a mountain yeah so I think one of my favorite thing so generally there's a 4 pm meeting where you you get on Zoom with people at a Fermi lab who are like run or 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 so 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 you everyone goes and watches the sunset over the ocean and you're on a mountain that's somewhat taller than all the other mountains and usually it's very clear or you know hopefully it's very clear out uh and it's just very beautiful and there's also these little rodents called viscatches that kind of they look like rabbits with squirrel tails that also seem to come out and watch the sunset so you're always kind of looking for those um and then yeah during the night you're kind of keeping an eye on the images they come in making sure that nothing's going wrong as a more Theory oriented person I think my main job is to call somebody who knows more about what they're doing if something does go wrong and there are you call the emergency number and there are like dedicated telescope operators who who live and work there uh so they're they're the the experts of the Machinery so um but yeah you also are supposed to like monitor how much like 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 like once every hour so and like you know 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 if the moon isn't bright or if the moon especially when the moon is down you can see the magellanic clouds wow and it's just like you're kind of like alone on a windy mountain top and it's like a little bit spooky but that sounds like a pretty good view to have pretty inspiring yeah makes you feel very small yeah I want to 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 simple relatively simple model pass is another series of tests and you know maybe on the surface this result could seem not so exciting because 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 I think it is both exciting and frustrating because so we have the simplest model so yeah Lambda cidium is sort of the maybe somewhat jargony name that we often refer to this like simplest model is 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 um and 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 made of the universe so the QV exceptions are debated um but as I said earlier it's not the whole story like we don't know what dark energy is and we don't want to dark matter is and together they make up 95 of the stuff in the universe and 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 um but there's for neither of these things there is not a like clear front runner like oh this must be it Theory um 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 and 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 I don't know what metaphor I'm using there places where the the predictions of this simplest model don't match our observations because if we find the mismatch that holds up as our data get more precise maybe holds up if different teams measure it and make differences like there's all these ways that I think if we start seeing Hansel wanna um really make sure what we're seeing is a hint of physics and not of you know some modeling assumption we don't understand well so um 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 and so it's frustrating that the results match that because it's you know 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 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 of mass of the galaxies and dark matter clustering up and so if you 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 the parameters or the properties of that model but if you lose 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 were and like a weak lensing survey like des and particularly we're making both measurements of the lensing and the clustering lets us make that kind of test make the most precise version of the kind of test available and you know general relativity seems to be doing very well yeah it seems to be standing up to a lot of the tests that it's being put under which is pretty amazing for the century old Theory very much so yeah I was uh I was I was looking around your website learning about the dark energy survey and your role and your past and I have to say uh 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 um in a really sort of fun bright engaging way and one I keep thinking of as you're talking is uh there's a person at a desk in a room I'm assuming maybe it's you maybe it's a you know it could be anybody but they're wearing it like VR goggles and you know in the what they see is this this uh beautiful expanse of galaxies and Swirls and stars and things but really they're they're at a desk in a room and there's a cat sleeping on the bed so I wondered a if that's you and B more generally can you tell us about your your artwork and and how you know I think you're the first person uh whose academic website I've gone on and has a tab that says cartoons for all their artwork I have spent a lot of time in the last couple years working from home with a cat sleeping on my bed so that that is an accurate is that a self-portrait the person the video no not necessarily but uh it wasn't spared by my roommate who I shared an apartment with during during the pandemic who would play a lot of VR games in his room so uh so yeah that cartoon was part of a series that I did with some collaborators in Des um so we released sort of the first round of the the cosmology results from the Galaxy clustering and weak lensing measurements from the 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 so there's more to come um but yeah when we were releasing those cosmology results it'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 you know a bit more accessible to the general public even if you know people aren't going to go to open up a PDF of a very technical paper about measuring like Galaxy distances or something I can tell you the comics are more inviting than a large PDF but I was drawn straight to them yeah and so we we iterated off there was sort of a couple years ago my colleague chiwei Chang um 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 to 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 of each of the papers and then we figured out try to figure out ways to illustrate them so that that cartoon that you're mentioning was the one I drew for a paper describing some simulated analysis so the idea that uh we kind of used simulated data analyze it as like a test run for our analysis and so the cartoon was meant to be an analogy partly because 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 yeah my first thought was flight simulators and even earlier in this conversation when you were describing the the simulation process and why you do it I thought well that's it's similar to why Pilots take flight simulators because you don't want to crash the real plane unless you know what you're doing right exact simulations to figure it out there was one other that I have to ask about there's one other cartoon of uh two volleyball players one is setting the ball the other one's about to spike it over the net and I I didn't fully grasp the science behind it but I think you know these things are they're meant to invite people in and learn more so can you tell us what the volleyball players are doing yeah so that that was to illustrate um one of the papers that starts combining these different types of measurements so specifically that one was um so we've got you know these three types of statistical measurements that we got so we've got the map of Galaxy shapes we've got the map of Galaxy positions you can either look at pairs of reality positions pairs of Galaxy shapes or the cross correlation is well pairs where one you have a shape and a a position um these statistical things I'm talking about we call them correlation functions that's the technical term but um and yeah 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 kind of combined probe analysis idea right and so the volleyball thing to say they're like working together it's teamwork to get the ball over the net or to tell us what dark energy is acting like yeah I don't want to ask you to describe your art in words too much because I know everyone should also go look at it but I also have to ask you about the Platypus comic yeah so let's see so yeah that one one of these cartoons is a little like three panel uh comic looking thing uh that has a bulletin board like you'd see in like a detective movie so you've got photos on it with like string and the idea is you're you're trying to sort of the scenario is you're trying to learn about what an animal is by getting like photos of different parts of the animal and so you start out with you know you have a photo of a foot it's like a web 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 uh and 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 um or Imaging different aspects of the animal so the the second panel of the comic is f the detective gets a photo of the animal's tail and instead of looking like a ducktail it looks like uh like a beaver tail and so you note it like 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 and I think especially you know with this set of like 30 papers like you know 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 like all right what is the one or two sentence sort of hopefully accessible description we can come up with so it helps me you know have a clear understanding of like the core concept behind a number of my colleagues papers that are very important for my work but I might might not be like deeply familiar with the details and then four things that are more closely related to what I work on so like this you know model testing by looking for mismatches between model and data or platypus hunting I guess um you know 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 a very good project for learning how to do that um and then yeah as an added benefit I now use a lot of these cartoons when I give talks so and the the style even varies between them you know I mentioned the volleyball players which they were sort of stick figures had playing a game and then there's the Platypus which is an animal and then there's one that's it resembles sort of an old wood cut or an engraving uh there was a term flammarian engraving I learned that word today flemerion yeah so yeah that actually references a a specific image that's often like in the forwards of like cosmology textbooks um so I definitely with some of these images I had a bit of fun with like trying to mimic some other style and that was the one that I did for the the paper on the blinding method so it was my paper so it was a little like I maybe spent a little bit more time on that drawing on some of the other ones you know so it's an image of somebody like exploring the universe and that's why it's often used in like astronomy or cosmology textbooks and so I tried to adapt the style and kind of represent some aspects of our our analysis and sort of what we're trying to find and sort of in that image there's a like a gift wrapped like present in the center and sort of that was meant to be tied towards like this idea of like revealing the results once you've done all this hard work and seeing what what the you know what our measurements are telling us about the universe have you always been artistically inclined have you always expressed yourself through drawing as well I always like to draw I took a lot of art classes in high school um there was a time when maybe I was 14 or 15 that I thought I might want to be an illustrator when I I grew up and um you took a different path yeah yeah uh yeah ultimately did not go that way and but I I always enjoy drawing yeah you are illustrating yeah as part of your professional work yeah and at least at least one of those cartoons is going to be in a textbook so I feel like yeah in terms of my illustration career I think that's I'm pretty pleased with it um you do it largely for fun yeah yeah action to get away from science or to help you conceptualize ideas I mean I I like drawing in general and find it relaxing and enjoy doing it I think I think I struggle with especially you know I think we're all in the past couple years have a little bit of like pandemic related burnout so it's a little hard to like find motivation or ideas during down time and I think particularly this like science cartoon project was very nice because 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 um so it's kind of fun to to bring those things together a bit and to like get to share them with with uh both collaborators and the general public so and at what point did you change your plans from working to be an illustrator to pursuing a career in physics I mean I guess I'd be clear that I I don't know that I was ever like really working to be almost it was like you know early High School like maybe this could be an option um I don't know I I always liked math or I don't know I liked a lot of subjects in school uh and I think what kind of made the difference was sort of towards the end of high school I read some sort of like popular level science book so like um Stephen Hawking's Brief History of Time and Brian Green's elegant universe and just was very curious about like I think I've always had a bit of a quality of like wanting to know how things work and why and I think reading those books kind of at some point struck me with the realization of like this isn't just knowledge that's out there it's something that people are developing and if I you know learn enough I could actually understand what they're doing it still took me several years after that to realize that like I could have a job doing that like I didn't understand like I started under grad I started college I think I hadn't my major was Undeclared until they told me they were going to drop all my classes if I didn't declare a major um but uh I got placed through a program at my undergrad University which is Michigan State University where they'll place you to work in a lab in whatever Department you want for some number of hours a week and I just read those books so I picked physics and I think that was important for keeping me interested long enough to keep in the field like I enjoyed my physics classes for sort of like problem solving puzzly things but like working in a lab meant I got sort of a crash course in the quantum mechanics when in class we were calculating like friction for boxes sliding down ramps right which you know can be interesting from a puzzly point of view but isn't quite as compelling from a like how does the universe fundamentally work what was the focus of that lab so it's very different from what I work on now so that the lab I got placed in was I think largely dictated by the fact I had never done any computer programming before at that time but it was a condensed matter lab so I was working with something called a scanning tunneling microscope which is a type of microscope that lets you image like atomic size features so I spent a lot of time trying to to calibrate a microscope using some like little samples involving like carbon atoms or like tiny pits and platinum that were of a known sized um and I ended up working in like several different labs in undergrades I was kind of learning uh what physics was as a field so I also spent a couple years working for a professor in experimental particle physics so helping with some like analysis software for the atlas experiment for the which is at the the Large Hadron Collider at certain um yeah not at that time but later yeah between my my Junior and senior so third and fourth year of undergrad I did a summer research program at CERN um who had to spend the summer there uh it was the summer before that the LHC turned on so I was mostly analyzing noise data but it was a it felt like a very valuable experience there were lectures that introduced you to a lot of different kinds of particle physics and cosmology and then also I think a thing that was really impressive to me about that experiment was like how social of an environment it was um like because you know I I didn't you know it took me a while to figure out that like grad school was a thing uh and a thing that I could do and so like I didn't have a lot of knowledge about like what it was like to be a scientist and this idea that you could travel and meet with and work with people from all over the world I think was really compelling to me early on and you know it continues through like I get to work with people from all over the world on this project to like you know push the the edges of our knowledge and the universe and it feels pretty special um so 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 home page 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 a PhD student at Perimeter I'm running what barriers have you experienced while trying to make science more accessible and more diverse that's a good question um so the the main way I've engaged with this it's very dependent 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 or you know similar similar numbers um and at that point I don't think I would have identified anything necessarily as a barrier but and the social dynamics I think I mostly experiment that and then a bit during a 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 um so you know like on one hand like I may have been one of the the only couple women in my physics classes while also recognizing that like 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 after class and so my ability to you know so there are some ways that isolation can and crop up and can become barriers definitely have had at least a couple interactions with uh professors assuming I I knew less than I did uh from potentially almost certainly a gendered point of view um 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 I think I think so there are ways I've faced barriers but also ways that I I have not had barriers that other people might have and I think in grad school I had a big learning experience with this and that I I helped organize the society for women in physics at the University of Michigan for most of my grad school career and there I think 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 that learning experience was often we would also that organization would communicate with and work jointly with other student groups on campus and try like 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 um making sure that these kind of Summers programs have enough like financial support that a student who might otherwise need to work a job can like participate um 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 it's 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 uh people who are not white or women or other gender minorities like can feel supported can find Community know who to ask for advice um and you know can feel heard in conversations recognizing that not everyone communicates in the same way right 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 uh I mean some of it is I just I mean kind of selfishly I enjoy it I I think I'm happiest doing science when I'm like chatting with other people about it um and I don't know I also think back to like the number of different professors I worked you know all these Labs that I worked and I also did a little bit of Galaxy cluster cosmology and in undergrad as well and like all of the professors I worked with are more senior undergrads or grad students that like helped me learn how to I don't know do computer Pro you know it's a learning process along the way and like different mentors definitely make a big impact on the trajectory I have made a 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 um feels important that trajectory of your career where do you see or hope it's it's headed next what's you know this is ongoing work with the Des yeah I mean well I'm gonna be on the job market for faculty jobs in the next couple years so um so you're listening hire me uh yeah I would like to keep doing cosmology research um I would like to be able to teach as well and keep mentoring students uh yeah so this analysis team that I've been co-leading with adnes Verte who's another postdoc um in Des uh so we we've LED this analysis extending the year three analysis which we call it two extended cosmological models models beyond the simplest one uh the the analysis of the full sort of Legacy data set for Des the year six analysis is ramping up I'm going to be taking a little bit more of a back seat like I'm still going to be contributing to different pieces of validation for like the Lambda CDM analysis as well as the extended models but uh some some members 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 so I'm going to be trying to take a little bit more of a support role and also use some of that free time like part part of this analysis there's 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 kind of have to find ways to work around that and so there are a couple of these things that you know were not workable on the time scale of that analysis but with a little bit more work um I think our gaps we can fill that will be to let us do a more precise analysis of the data we already have and also get it ready for next analysis so I was just at a meeting where I was discussing plans with a grad student about sort of extending one of these other analyzes so they're sort of more direct spin-off projects and then you know I also want to get a little bit more involved in sort of the the sort of Next Generation uh survey which is called the the Rubin lsst Rivera Ruben Observatory lsst and that's that's sort of the next evolution in in Precision or and yeah so it's going to be turning on I think in the next year or so it's like on the mount next Mountain over from where the dark energy camera is uh and it's gonna like whereas Des surveyed 1 8 of the sky I mean many times but over the course of six years the the lsst so that stands for legacy survey of space and time the acronym has changed what it stood for a couple times um but I think that's the current one uh is going to image like the whole well as much of the sky as you can image without the Milky Way getting too in the way uh and it's also on the ground so the half of this guy it has access to right like basically every every night or every two nights and so it'll be able to like it has an even bigger field of view on des and will be able to get more precise data looking at fainter galaxies and making more precise measurements of shapes and other other things so um I've been sort of loosely involved in that but I I will be hopefully you know helping with with the year six the final Des analysis and sort of figuring out ways that I can you know take the expertise that I've developed with des and try and get involved in lsst and you know maybe outside of survey science as well you know if I'm over counting my uh free time look for you know do more theoretical projects looking for like what are the ways we can use this data or like I think the fact that I'm interested in theory and have this experiment or experience working with data I think compared to your to your average theorist I think I have a better sense of the way or I have a good sense maybe not better 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 want to have to care about you might have to care about and um so I'll probably continue working at the interface of that you're 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 work doing it carefully and robustly yeah well thank you so much for for taking us on this journey there's so many things I didn't 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 the next stages of this experiment and the ones after that would be great it was great talking to you [Music]

Channel: Perimeter Institute for Theoretical Physics

Views: 57,751

Keywords: physics, theoretical, perimeter, institute, canada, ontario, science, stem

Id: QkozRHsbwbA

Length: 83min 26sec (5006 seconds)

Published: Thu Aug 03 2023