**SPEAKER_1** (0:00)
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**SPEAKER_2** (0:31)
This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome? That's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50-page restoration block, or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses set up required compatibility and availability varies 18 plus.
**Scott Dodelson** (0:56)
What we've done in order to make the story of the universe work is introduce dark matter, introduce inflation, introduce dark energy, and none of this has been found in the lab. How many free passes do we get? Exploring the tension, killing the model is my dream.
**Brian Keating** (1:11)
That's Scott Dodelson. He runs the Cosmic Physics Division at Fermilab. He teaches at the University of Chicago, and if you've ever taken a graduate cosmology class, probably took it from Scott. He also spent 10 years running the sharpest test anyone has ever made of the standard model of cosmology, the model he helped build. The answer came back, two and a half signal off. Close enough to call it a triumph, not close enough to stop staring at it in disbelief. I'm Brian Keating.
**SPEAKER_2** (1:36)
This is Into the Impossible.
**Brian Keating** (1:38)
What is new in the field of dark energy? Yeah. Where we go any further?
**Scott Dodelson** (1:41)
Well, since you've been in the field, we've had this fiducial model of cosmology. I guess you helped establish it really. So it's called Lambda-CDM. I've heard you talk about it.
Now, you've had Kyle and other people on saying that it's under stress.
So the fundamental question that I'm interested in is not, or one of the questions I'm interested in is, how will we change our mind if we will? That is, we have this pretty simple model, and then there are these data points which are saying, oh, this doesn't work, it doesn't work. Maybe it doesn't work. So we have to digest that, and everyone digests it in their own way. And then how are we going to collectively land on another model? And so I know people have written books about this, Tom and Keaton's Structure of Scientific Revolutions, but we're living through that time now. So I'm kind of-
**Brian Keating** (2:29)
It may be in many ways, with AI we'll cut to that later.
**Scott Dodelson** (2:31)
Yeah, right. So it's not, and of course it's not just in cosmology. So cosmology is the lens through which I can understand stuff. But as you say, in society in general, we're losing faith in institutions, so we don't know which institutions we're gonna land on, which to believe, which to trust. So I think it's kind of an important question. And so I've been trying to explore it in this little corner of our world, cosmology, which is in some ways the simplest thing we do.
It's very hard to be a parent, to be a spouse, to be a friend, but cosmology is really easy because there's no people and it's an easier thing. So that's what I've been trying to explore in that context. For about 10 years, I was heavily involved in this project called DES, The Dark Energy Survey, and that was started taking data in 2012 And as you know, it takes an enourous amount of time to process and analyze this data. So we only put out our final results a few months ago. So that's what's been occupying me for the last 10 years or so.
**Brian Keating** (3:25)
Look about the connection between the type of science that I do, which is the first light in the universe, the cosmic and the gray background. I've talked a lot about that. I've talked less about the kind of science that DES does though, with the exception of conversations of people like Kyle and others. But talk about what is DES, again, it's somewhat strange. Not only are you not using particle detectors and whatnot, but you're using optical telescopes, right?
So what does DES do? What is it comprised of? You mentioned how long it took, but what really went into that? What's the portfolio diversification between theory, which is what you do, experimental hardware, observations, big data, machine learning? What are the different ingredients in DES? First of all, what does it stand for?
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