Learning Bayesian Statistics

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What happens inside a black hole? Can we travel back in time? Why is the Universe even here? This is the type of chill questions that we’re all asking ourselves from time to time — you know, when we’re sitting on the beach.

This is also the kind of questions Daniel Whiteson loves to talk about in his podcast, “Daniel and Jorge Explain the Universe”, co-hosted with Jorge Cham, the author of PhD comics. Honestly, it’s one of my favorite shows ever, so I warmly recommend it. Actually, if you’ve ever hung out with me in person, there is a high chance I started nerding out about it…

Daniel is, of course, a professor of physics, at the University of California, Irvine, and also a researcher at CERN, using the Large Hadron Collider to search for exotic new particles — yes, these are particles that put little umbrellas in their drinks and taste like coconut.

On his free time, Daniel loves reading, sailing and baking — I can confirm that he makes a killer Nutella roll!

Oh, I almost forgot: Daniel and Jorge wrote two books — We Have No Idea and FAQ about the Universe — which, again, I strongly recommend. They are among my all-time favorites.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek and Paul Cox.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag 😉

Links from the show:


by Christoph Bamberg

Big questions are tackled in episode 72 of the Learning Bayesian Statistics Podcast: “What is the nature of the universe?”, “What is the role of science?”, “How are findings in physics created and communicated?”, “What is randomness actually?”. This episode’s guest, Daniel Whitesun, is just the right person to address these questions.

He is well-known for his own podcast “Daniel and Jorge Explain the Universe”, wrote several popular science books on physics and works as a particle physicist with data from the particle physics laboratory CERN.

He manages to make sense of Astrology, although he is not much of a star-gazer himself. Daniel prefers to look for weird stuff in the data of colliding particles and ask unexpected questions.

This comes with great statistical challenges that he tackles with Bayesian statistics and machine learning, while he also subscribes to the frequentist philosophy of statistics.

In the episode, Alex and Daniel touch upon many of the great ideas in quantum physics, the Higgs boson, Schrödinger’s cat, John Bell’s quantum entanglement discoveries, true random processes and much more. Mixed in throughout are pieces of advice for anyone scientifically-minded and curious about the universe.


[00:00:00] What happens inside a back home? Can we travel back in time? Why is the universe even here? This is the type of chill questions that we are all asking ourselves from time to. You know, when we are sitting in the beach, this is also the kind of questions that Daniel Whiteson loves to talk about. In his podcast, Daniel and Jorge explained the universe co-hosted with Jorge Chan, the author of PhD comics.

And honestly, it's one of my favorite shows ever. So I'm warmly recommended, and actually, if you've ever hung out with me in person, there is a high chance I started nerding out about it. Daniel is, of course, a professor physics at the University of California Irvine, and also a researcher at CERN using the large HI Collider to search for exotic new particles.

And yes, these are particles that put little umbrellas in their drinks and taste like coconut. [00:01:00] On his free time. Daniel loves reading, sailing, and baking. I can confirm by the way that he makes a killer neutral role. Oh, and I almost forgot, Daniel and Jge wrote two books. We have no Idea and frequently asked Questions about the universe, which again, I strongly recommend.

They are among my old. Favorites. This is Learning Beijing Statistics, episode 72, where Ann gets to interview one of his favorites. Science authors recorded September 6th, 2022.

Welcome to Learning Beijing Statistics, a fortnightly podcast on base inference methods. The project. In the people who make it possible. I'm your host, Alex Andora. You can follow Twitter and Ann Andora like the country. For any info with the podcast, learn base stats.com is lab less to be show notes becoming corporate sponsor supporting [00:02:00] LBS pat and unlocking based merge.

Everything is in there. That's learn based dance.com. If with all that info, a model is still resisting you, or if you find my voice especially smooth and want me to come and teach basic stats in your company, then reach out at Alex dot andora atmc labs dot I or book Core with me@learnbaseddance.com.

Thanks a lot, folks. And. Patient wishes to you all. Let me show you how to be a good BA and change your predictions after taking information. And if you thinking now be less than amazing, let's adjust those expectations. What's a basian is someone who cares about evidence and doesn't jump to assumptions based on intuitions and prejudice.

Abassian makes predictions on the best available info and adjusts the probability cuz every belief is provisional. And when I kick a flow, mostly I'm watching eyes widen. Maybe cuz my likeness lowers [00:03:00] expectations of tight Ryman. How would I know unless I'm Ryman in front of a bunch of blind men dropping placebo controlled science like I'm Richard Feinman.

Hey folks, just a quick editing note. As you can probably hear, I'm having some troubles with my mic, which puts me in the very ironic position of being podcast. Without a mic, so thank you in advance for your patience and understanding. I am already looking into a replacement, so hopefully for the next episode, you'll hear me with a brand new mic with great.

Sound. And also a quick thank you to Tyler Birge, who is a podcast patron and was kind enough to put me in contact with Daniel Whiteson. So officially, thank you so much, Tyler. You made my day. Okay, now let's talk about physics and the universe. Just one last announcement this Thursday, December 8th, 2022.

I'm co-hosting a meet [00:04:00] with p c labs, where Antonian NGO that we work with will tell us how they used p c and hierarchical post stratification to deal with noisy data. So if you're working in environments where uncertainty is high in data are sparse, especially in some straight out of the population.

Well, I'd like to invite you, of course, you're my favorite. Just go to meetup.com/labs online. Meetup and register. This is a mouthful, so the link is in the show notes. Daniel White Song, welcome to Learning Beijing Statistics. Thanks very much for having me. Excited to be here at eight o'clock in the morning.

Yes, I know. So now all the listeners know how cruel I am with all my guests, so thank you very much, . Be warned. Potential guests. Yeah, exactly. No, I have to say, yeah, you only do that with Daniel because I really love what he's doing, so I needed him to wake up for 8:00 AM so that we could record [00:05:00] for about five to six hours

But usually we record in the evening. That's great. And I have actually your voice with me every day because I'm like listening to Daniel and Ho explain the universe all the time. So that's very fun to have you now live with me today. I was listening to the episode, the cruise tour of the exoplanets.

That, that was very fun. I love that one. It's so much fun to talk about those crazy topics in science and to explore them and to take our listeners' minds to other worlds and distant parts of the universe. To me, it's just incredible that sitting here on this tiny little rock, we can actually know something about things that are happening so far away.

It's uh, it's kinda impressive. Yeah, yeah, yeah. It's so weird like to, because it makes you feel both powerless and powerful at the same time. It's super weird, . It's like, damn, this is, So empty outside. But at the same time, there is so many things . It's just like, it feels weird. But [00:06:00] yeah, that's a feeling I love.

Like that's really, and I think you say that a lot in the podcast, like that feeling of like being surprised and understanding afterwards the very weird things that the universe has for us in store. This is an awesome feeling. It is absolutely sort of digesting some crazy new idea about the universe and then thinking about all the people who never even lived to hear it, that lived their entire lives thinking the universe was one way and not realizing it was something different.

And then thinking forwards to humans in the future who will make new, crazy mind bending discoveries and think about us as living in that ignorant age before they understood how things really were. So I like to think of ourselves as sort of in a long line of ignorant, Mrs. Yeah. Yeah, exactly. And I, I think about that also sometimes when I'm like trying to remember that I know some stuff, but there is more stuff that I don't know.

It's. I remember that in the end, it's not being a long time, [00:07:00] since we know that we have to wash our hands, for instance, to be way safer from epi, epi epidemiological standpoint. And I'm always wondering, so like, because we make fun with of those people, you know, it's like you just had to wash your hands and way less people would've died.

And we did that for centuries. So was wondering what will be the, you know, you just had to wash your. From the future, like what will the future generations look at for us and be like, these guys were so ignored. , something about podcasts. You guys just had to have more podcasts. You could save the world with podcasts.

we're getting there. Yeah, yeah. We're try. We're trying. Yeah. So actually this is a very serious podcast, so I have to start very seriously and and ask you, Daniel, what's your zodiac sign and what does it say about the universe? ? Well, I was born in June, so my zodiac sign is Gemini and it [00:08:00] says absolutely nothing about the universe except that humans have been struggling.

Ever to try to make sense of it and have come up with like pretty crazy ideas for how to digest the universe and explain the things that seem to make no sense. You know, if you are a human 20,000 years ago or 10,000 years ago trying to understand why your child has died of disease or why a storm ruined your crops, you're gonna wonder, you know, what's out there causing all these things?

Is it some other intelligent being, you know, the gods? Is it some process out of your control? And so it's uh, you know, it's just part of the rich tapestry of humans desperately trying to understand the universe around us. Yeah. Yeah. And you'll have heard, you heard it here first people, so as strategy is a real science.

That's the first thing. . It's storytelling. It's storytelling. And there is an important part of science, which is [00:09:00] storytelling, right? Science is in the end, telling a kind of story. That doesn't mean that every story is science, but all good science does have a story element to it. Yeah. And a human factor.

That's also what I love in the work you're doing with, and what I try to do with the podcast also is like to make science more human because. I'm a statistician myself, and it's very interesting and I read a lot about physics. I'm, I'm a real nerd about that right now. And it's funny because each time you say that people are really intimidated and they're like, oh yeah, that's very powerful, but like, so dry and, you know, uh, rational.

But for them, rational is not a good word, . And I'm always like, you know, saying the country, I'm like, yeah, well it's because you only see the result, right? But any big discovery in science starts with humans and even continues with humans. Like the, like science was not something that came out of the universe.

It's something like the scientific methods, something we invented to [00:10:00] answer the questions we were really curious about. So in the end, it is actually extremely. Human incarnated. And also I find it quite poetic. You know, like when you look at the night sky and you see like big red dots and you're like, damn, that's a dying star.

Is it that like, yeah. Um, like dying star, but it's actually already dead. You're saying something that already happened, but you're only seeing it right now. And this is always mind blowing to me, and it's kind of poet taken away. I totally agree with you, and I think it's often overlooked that science is not this institution of knowledge.

It's a human activity. It's by people, it's of people, it's for people, right? The questions we ask are the questions we want answers to. And you know, every time you read some paper about like the meeting habits of hummingbirds, because somebody out there has decided that's the most important thing in the world and we have to understand it, and I'm gonna spend six years crouching in a bush, watching hummingbirds do it so that we can understand the [00:11:00] answer.

And so, you know, science only gets pushed forward when somebody is. Desperately curious about that particular question, and I think that's a really interesting, an important thing to keep in mind as we fold in machine learning and AI into our science, that it can help power our science, but it can't replace us entirely because you always need humans to be asking the questions and to be appreciating the answers.

Yeah. That's period of, of curiosity basically. And also that point that you often make. The universe is like actually crazier than whatever you can imagine because our stand, our standpoint is limited by anything we've ever encountered as a species. But like the universe probably has like has billions of planets and we only have eight.

So very probably there are extremely weird things out there that we can't even imagine. I find that awesome. And like in the podcast episode I was talking about that I was listening today from you in Ohio about exoplanet. Like there is actually at least one planet with [00:12:00] two serves. This is incredible. Oh yeah, yeah.

This is amazing. I'd like to just see what's out there, you know, like how is it to live there? And I found that also fascinating that we can know that even. Very far from this. Yeah. And actually it's much more common to have two sons than you might expect because stars tend to form in clusters. You don't have like an individual star form in the middle of space, you have these big gas clouds that tend to collapse and form lots of stars.

So you get binary star systems. You also get tri star systems, which are mostly unstable because the three body problem is pretty tricky. But if you can get two of the stars near each other to form like a little system, a binary system, and then together with another star, they form like a nested binary system, then you can get like an almost stable configuration of three stars.

And then you can just keep adding to that, you know, with this nesting, recursive, uh, star adding procedure. So yeah, the universe is pretty weird. And in the end it's an important statistical question, right? Like our solar system [00:13:00] is one sample of trillion. Right. How typical is it? How unusual is it? How weird are we and how normal are we?

That's like one of the deepest questions in human existence and in the end it's a statistical one. Right? True. Oh my god, you should probably have a, a podcast or something like that because you're very good at making segueways . Yeah, exactly. And like this, it's something I discovered also when reading more about physics is like, actually statistics is extremely important in there, in the reasoning, in the methods and in the reasonings and in the conclusions.

So that was reassuring to me because there is a lot of things I don't understand in physics, but that I was like, okay, that's good. I like that. And I got these whole idea of like also of the multiverse. If everything is possible then everything can happen and will happen. That's awesome. There is a planet.

Only horses or only horses with my face on them. It's amazing. And one with a podcast where two horses with your face on them are talking to each other. [00:14:00] True. Yeah, exactly. And they are visited by Ironman, you know? So does that like, I mean, definitely that means there is a planet with superheroes, right? So then that would be the Avengers

That's cool. I feel like I'm, I'm taking ha's role now talking about Marvel. Actually, I'd like. Speaking about humans and stories and superheroes, I'd like to talk about your origin story. I'm curious about how you came to the physics and stats worlds and how senior of a path it was. Oh, that's a good question.

And you know, I could tell you a podcast appropriate story about being transfixed by the night, sky.dot, dot, but the answer is not really that simple. I did love looking up at the Night sky as a kid and wondering about the universe and all that stuff, and so I tried astronomy and then I discovered I don't really like standing outside.

In the dark, in the cold, staring into a telescope for more than a couple of minutes. And then I find it kind of boring. Like you can't really do much other [00:15:00] than look at stars and see that they still kind of look like points. Or you could look at the moon and like, well, how much can you really look at the moon?

So, I was inspired by astronomy, but I didn't end up becoming an astronomer because I didn't actually like the day to day work of it. And as I went through school, I really wasn't sure what I wanted to do. But I grew up in a town where physics was everywhere. I grew in Los Alamo, New Mexico, and sort of like, you know, physics is in the air.

And it was just sort of like the culture to think that physics was the thing and it was the hardest thing. And so I thought, well, I don't know what I wanna do, so I'll just try the hardest thing, which is physics and I'll keep going until someone tells me to stop. And basically, here I am today. So I just, I went to college and I studied physics and it was pretty good at it.

And it was finally in my junior year in college when I took a class in particle physics and I understood, wow, this is the stuff, man, this is really interesting. I was, you know, years into it before I actually got [00:16:00] inspired and it really touched my like, Interest of the deep questions of physics. I loved taking things apart and trying to understand what the fundamental bits were.

And then I got involved in research and from there just kept going and particle physics. And I also was studying machine learning and computer science and college. I couldn't decide between physics and computer science, so I did both. And so I've been doing machine learning and computer science.

Actually everybody else in my family has a degree in computer science. I'm the only one with a physics degree, . So I'm the oddball. My brother is now a uh, professor at Oxford in machine learning. I see. Damn. Like you picked the two right fields. Definitely. These fields are not like outdated. You could have been unlucky and just like big two fields that just disappeared cuz of technology.

I did not. Were you advised by a superior civilization coming from space or, no, I just tried a bunch of stuff. You know, when I was very young, I [00:17:00] thought I wanted to do plasma physics because I thought fusion is the future. And boy, if we could crack that problem, we could solve everything. And I still think that's true, but I tried spending a summer doing fusion research, and I just found it boring.

There's something I didn't understand, which is really basic, which is that to do research in science, you need to not just be interested in the big questions, and that's not that hard, but you also have to enjoy the day to day work of it, because on a daily basis, we're not making big discoveries and solving huge problems.

Were doing some craft. And in the case of plasma physics, it was like dealing with vacuum chambers and wrenches and pumps, and I didn't really like that. I thought it was boring and annoying. And I spent another summer trying condensed matter physics, which seemed awesome because we were like zapping things with lasers to make new kinds of materials, which, wow, that seems amazing, except that mostly it meant debugging the laser.

And a laser has like 5,000 components, and if each one works [00:18:00] 99% of the time, the laser never works. So I spent a whole summer in a basement trying to make a laser work and got it to work for like 19 seconds. And so I was interested in the questions, but not the craft. And then particle physics. Of course, I was intrigued by the fundamental nature of matter, but it turned out that the day to day work was mostly writing computer programs to analyze data, and that was my jam.

And so I love spending my days building little mental machines, little software algorithms to solve a problem. So finally I found something where I was both interested in the day-to-day work and in the deep questions. And so that's why it was a good fit for. Yeah, definitely. Okay. So I wanna dive actually a bit deeper into like what you actually do, um, because it's something I, I don't really know about because in, in your podcast or books, you talk about the general topics, but not really about your personal work.

So I'm very curious about that. But from a meta standpoint, yeah. I really love that point of, yeah, when you can and when you are. Time in your life, like really [00:19:00] try stuff out and just like it's okay to like try stuff and find that boring and fail at it and then come back to it or go to something else. I think it's extremely important and something that not enough people do, at least in France,

So just do it. Yeah. I think it's one of the hardest things in life is to figure out who are you and what do you like to do. If everybody knew that, you know, at 15 their life path would be so much more satisfying. Right? But sometimes it takes until you're 25 or 35, uh, to figure out like who actually are you and what is it you like to do with your time?

Yeah. In a way it sounds a bit discouraging, right? Because we have to understand the the universe, but we also have to understand ourselves. It's like, when is that gonna stop? You know? I like to understand at least one thing. I know my cat, I know what my cat wants most of the time. That's something. Like at least that works.

So actually, can you define the work that you're doing [00:20:00] nowadays and the topics that you are particularly interested in? Yeah, sure. So I'm a particle physicist. That means that I smash particles together and the hope is out of the debris. From those collisions, we learn something new about the universe.

Maybe we create a particle that hasn't been seen before. Maybe we identify a new force that nobody has identified before. But this is not something I can do myself in my basement laboratory here in California, we work with a $10 billion facility in Geneva. So the large Hay John Collider, where they have a huge ring where we smash protons together at almost the speed of light.

And so I don't have to build my own accelerator or build my own detector. I just have access to this data because I've helped. Be a part of the team that constructed the whole facility. And so to me, I get to ask whatever science questions I want. And to me, the interesting science questions are what's in the data that we haven't expected, that we haven't anticipated.

You know, we do [00:21:00] these collisions, we smash protons together because we think there's something new to discover. We think there might be some new particles we don't know about, or some new forces. And we have some ideas for what those are Theorists have been thinking and drawing of the chalkboard and imagining what new particles might complete the story of the standard model.

And they have specific ideas. And so some folks go and look for those and they say, well, you know, how do we see this thing? How can we identify these things in our collisions? And that's good and that's worthwhile. But to me that's not as interesting as using the data to look for something unexpected. I think it the most interesting discovery, the most like exciting scientific moment would be if you found something and everybody said what?

That's impossible. It can't, what you've discovered is in contradiction with everything we've always understood. That's like my personal scientific fantasy. And so I'm trying to set myself up for that by looking for weird stuff. Looking for stuff that's unexpected. And so that's, uh, how a lot of the statistics comes into play.

It's like, [00:22:00] how do you analyze collisions looking for things if you don't have the alternative hypothesis, if you don't know exactly what it is that you're looking for. Yeah. And in a sense, I really like that because like the motivation is, The same as people who are like convinced that, uh, UFOs are real.

You know, I mean coming from outer space or some conspiracy stuff, like the motivation in a sense is the same. Like it's these willingness to believe that something extraordinary is possible and can happen and is actually happening all the time in the universe. But what differs is the method to go there because then with the scientific methods that you apply and the statistics that you use in your experiments, you can actually be somewhat certain, depends on the topics, but you can actually estimate also your uncertainty around your theories and be like, well, I'd really like [00:23:00] this theory of, well, I dunno, aliens visiting us and actually already having visiting us to be true.

I can't really believe it because of these other alternative theories that are much more probable. And I love that the motivation is the same and is actually something that seems to be in like I our species since the, the beginning of times. Right. There was someone at some point who was like, what's behind this?

Heal where everybody, nobody has ever been. So I, I really love that. But then the method comes into play and then that's how you make discoveries. Yeah. You know, the truth is out there and it's definitely part of being curious is allowing yourself to believe that something crazy could exist. And it's also interesting because it's very subjective.

You know, what you choose to look for. It comes from your curiosity, it comes from a very human place, the kind of thing you'd like to discover or the kind of question you want to ask. It makes me wonder. When we talk [00:24:00] about the universality of physics, some people think that the physics we're uncovering about the universe is deeply true.

You know that the particles are there, whether we're looking at them or not, and that top cos have existed since the beginning of the universe. And if aliens arrived, we could talk to them about these particles. You know, we probably couldn't talk to them about what they like to eat for lunch or how they celebrate their birthdays or these kind of cultural arbitrary things, but that in physics, there's like something universal and deep and true.

But I'm not sure I'm convinced by that. I. That a lot of the questions we ask have human motivations, and the answers we tell have human stories, and so I think there's a lot of humanity there. I think it'll be really eye opening if we ever do get to talk to alien physicists, and there's this subjective nature in all of our science.

You know, the theoretical community in particle physics is excited about one idea, it's called super symmetry, that maybe there are these particles and we know the electron, the muan, the towel, all these tiny little things that make up the [00:25:00] world around us, but maybe there are also other particles. There's a copy of each one.

The Electron has a partner, the Meean has a partner, the towel has a partner. Maybe they're out there and waiting for us to discover them, and they're excited about this idea for specific reasons. They look at the mathematics of their theory and they say, this would be prettier. There were these other particles, this would have a symmetry to it.

It would be elegant. And you know, there's a subjective nature to that idea. And so that tells me that there might be a bias. You know, there are almost certainly a bias. And I would love to explore the universe in a way that's more open minded to things we didn't expect. There's lots of fields of science that are very, very exploratory.

Think about like landing a rover on. We don't know what we're gonna find. You turn over that rock, there could be like critters under there. There could be anything. That's why we do it, right? We do it for the surprise factor. We don't want to go to Mars and discover exactly what we expected. We wanna go to [00:26:00] Mars and find something that blows our minds, right?

And so that's what I wanna do in particle physics, is to look for something weird and unusual. And you know, we have various statistical tools to do that. A lot of which have become much more sophisticated recently because of powerful machine learning that helps us define like what it means to be unexpected, what is different, what is an outlier, but also just so sort of old fashioned creativity.

You know one thing that we do in particle physics is we look for a heavy particle decaying to two other particles. So for example, when we saw the Higgs bow on, we didn't actually see the Higgs bow on. We saw it turn into two other things cuz the Higgs bow on lasts for 10 to the minus 23 seconds. You can't actually observe it.

You can only infer statistically that it existed because of the patterns of the things that leaves behind. Sort of like missing a car accident and reconstructing what happened just from the pattern of debris in the intersection. And so that's the kind of thing we do all the time is look for heavy [00:27:00] things decaying to pairs of light things.

And so I had this idea, well, why don't we look for weird, heavy things decaying to weird pairs? Pairs people hadn't expected? Like, let's not look for heavy particles decaying to an electron and a tron. Let's look for heavy particles decaying to an electron and a muan, or an electron and a Higgs bow on, or an electron and a w, these strange pairings that nobody had expected.

And I proposed this to a theorist friend of mine once at a conference over coffee, and he said, well, that's ridiculous. I have three reasons why those things could never happen. You will never see those things in your collider. And I thought, awesome, because if you're convinced they can't exist. And then I discovered them, boom, Nobel Prize, right?

And then a week later, I saw the same theories at another conference and he was like, actually, I have now two different theories that could explain why you might see those events. And so it made me realize that the reason nobody had predicted this or thought about it was just because nobody had been [00:28:00] interested.

The theoretical community has these trends, these ideas they follow. And that's not a criticism, right? Every community is like that, but it does mean that it's possible to try to push the boundaries of that and think outside of it and think about what else we might see that could surprise us. Yeah, definitely.

And again, Human factor extremely important because in the end it's people doing the research and having interests and trends and like also needing money. and some topics are more bankable. So yeah, for sure. This is definitely important and. I have several directions I could take now, but let's, uh, keep talking a bit about statistics and then have some quantum physics questions for you because this my own personal misunderstandings , so it's my personal crusade.

So, yeah, actually, so this is a podcast with Beijing Statistics, so I'm curious person. Are you familiar with those statistics and are they useful to you [00:29:00] personally in your work or not? Absolutely. I mean, statistics is at the heart of everything we do in particle physics because we don't have direct observations.

It's not like looking for unicorns where it might be hard to find it, but once you spot one, like. Everybody pretty much agrees you found a unicorn. In our case, we never see the particles directly. It's always statistical inference. We say we have this data and it's more likely to have been generated by a universe where the expose exists than a universe where it doesn't exist.

And all we can do in the end is make a statistical statement. Say there's a less than one in 20,000 chance of a fluctuation from a no Higgs on universe to look like a Higgs on universe. So absolutely, statistics is at the core of everything we do and uh, you know, I love that. I think statistics is super fun.

I love the math of it. I love the logic of it and I love the philosophy of it, you know, so we definitely use statistics and I love the sort. Fundamental disagreements about [00:30:00] what the questions are between bas and frequentist. I find myself, and I don't know if this is gonna offend you more often, agreeing philosophically with the frequentist about the nature of the questions that we're asking and how to answer them.

But basian methods are very powerful and I often find myself using them in our papers and in our analysis. Yeah, that's super interesting. And yeah, it's not the first time I hear that point that someone agreeing more philosophically with the idea of what is probability? You know, and these kind of deep kind of axioms in a sense that are at the foundation of the two kinds of statistics.

And in a sense, I would say these axio are kind of like almost a subjective choice, where it's like, well, I prefer my statistics, found it that way. But I prefer, or I prefer found it, founded it that way, but. Then the methods and how you use statistics actually in your work. Uh, it's actually not the first time [00:31:00] that the same person tell me.

I prefer philosophically the frequentist idea, but then the methods when I use them, I prefer much more the the patient way because it's just like much more intuitive to me. And also it's more useful to me because I don't have a lot of data and I have a lot of domain knowledge and prior scientific knowledge that I need to tell the models and so on.

Yeah, and it's not just personal taste. It's not just like, oh, I like cherry vanilla and you like chocolate, you know? Are questions about what are the questions we are asking, right? You're talking about the fundamental nature of probability in the end, statistics always comes down to really carefully specifying what it is that you want to know, because two very similar sounding questions can give very, very different answers.

And you know, English is fuzzy. English is not a great language for communicating specific quantifiable things, which is in the end why we are writing down mathematical thes to describe the universe rather [00:32:00] than paragraphs. But they do come from humans, right? And so it's what question do you want to know the answer to?

Is it interesting to you to say, well, In a thousand parallel universes, you know, would there be a podcast with horse horse spaces on them that's sort of frequentist, right? Or is it more interesting to you to say like, well, we just don't know, and this probability describes our lack of knowledge about that kind of stuff.

And so I think it's important. It's important that these questions are connected in, in the end, down to thing, the things we're asking, the things we want to know the answer to. It's just amazing to me that that you get such different procedures based on asking very similar questions. Yeah, yeah, yeah, exactly.

And then depending on your question in context, one or the other kind of statistic will be more useful. For sure. It's like having different tools at your disposal and then. Using the one that's most appropriate. But that's super interesting to hear that it, it's actually extremely, uh, useful in your, in your work.

And a question I often ask, my guess is do you remember how you [00:33:00] first got introduced to patient methods? Yeah, I think that I took a class in statistics in college and I needed it for particle physics and I didn't understand why. And so like, oh, I'll just take it. And I fell in love with it and I'm learning about bian base theorem and feeling like it was really actually very closely connected to quantum mechanics and linear algebra in a way I hadn't expected.

Thinking about base theorem, you can sort of think about projecting a vector on ba on into a basis space, which is something we do all the time in the neur algebra and is essential component of quantum mechanics. You have a, a vector in hilbert space and you're thinking about, you know, the egen vectors of that space.

Well, it seems to me like. And you know, I'm not a statistician or an expert in this area, but it seems to me like expressing a probability in terms of its conditionals. Using base theorem and using those expansions feels to me sort of similar to expressing something in terms of the basis states or the [00:34:00] agen vectors of the space.

I like that. Yeah, indeed. So actually, do you have an example in mind from your work, ideally that helps listeners well understand your work, but also how patient success can be helpful, uh, there? Yeah, sure. We recently wrote a paper looking for very strange things in the Ka Collider. Uh, one thing that happens a lot in the Collider is that you smash two proteins together, and what comes out are two corks, like an up cork and an antiparticle up cork.

And they fly in opposite directions. But corks, when they're created, you can never see them by themselves. There's so much power in the strong force that they feel that they're pulling particles out of the vacuum. So a particle, like a cork immediately has thousand. Of other corks flying with it. They're never by themselves.

They create these sprays of particles that we call jets, and one idea that people have is, well, maybe sometimes these [00:35:00] jets have dark matter in them. Maybe they don't just pull like normal corks outta the vacuum. They also pull dark matter particles out of the vacuum. And maybe we're creating dark matter inside these.

So we developed some techniques to try to tell these apart, like, well you have a jet. Is it from a normal cork or is it from a dark matter particle, or is there dark matter inside your jet? So we used machine learning to develop a classifier that could try to tell the difference between these two. And that gives you some information, but in order to understand, in order to make a decision like is this jet from a cork or is it from a dark matter cork?

You also have to have some idea about the prior probability of this happening. And so we use bas and statistics in that example to try to fold in the new information we get from the machine learning algorithm that tells us if it looks like a dark matter jet or a normal jet fold that together with our priors about which one is more likely to try to get some sort of [00:36:00] updated information, updated analysis about, you know, whether a particular jet is likely to be from dark matter or likely to be from normal matter.

Okay. That's super cool. Do you have a link to that paper or a paper already that we can put in the Charlotte? Yeah, absolutely. Um, I have a link to a paper and a little tweet thread about it. It was actually a really fun paper because we did more than just try to throw machine learning at it and say, Here's a black box that just tells us whether it thinks it's dark matter or not.

We also try to interpret the network. We try to translate the network into a basis space of sort of human concepts about these jets. We ask theorists to define like, can you define a space of ideas? Can you say like, what are all the possible ways you could think about jets? All the possible sort of operators you could imagine on jets, ways to analyze.

Primitives, you know, what are like the, the primitives of the language to think about jets. And then we took this neural [00:37:00] network and we try to map it into that space. We say, can we express this neural network, which is just in the end, you know, a complex function with lots of weights into the space of sort of human ideas, translated back to the human mind and then look at that and understand what is it doing.

And so that was a really fun project. Cause we got a little bit of insight into not just whether we could tell them apart, but how the machine learning was distinguishing one kind of jet from another kind of jet. Okay. And actually I'm curious how much of your job. Is trying to kind of translate knowledge that a non-human could understand.

So like, I dunno, computer or some other species that can see and feel more things than us with our limited senses to then the language and senses of humans. You know, like I'm thinking about that black hole image for instance, where it's like, it's not really, we're not really seeing the black hole, but we're seeing something that we can see in.

[00:38:00] Is accurately depicting that black hole, but I'm guessing that it's an important part of the work, right? Because yeah, then you need to like convince not only the scientific community, but then also like the general public about those things. So yeah. I'm curious like how, how does that work for you and how much of that work that means?

For you. Yeah. I think that's the central project of physics, right? Is explain the universe, the unfamiliar in terms of the familiar, you picked a great example of the black hole. You know, what is it that you would actually see with your eyeballs if you were there? That's not what you would see, right? That image is reconstructed from photons of all different wavelengths, or maybe even CRISPR example is like the James Webspace telescope.

Everybody's been like drooling over these pictures on their computer screens, but realize the James Webspace telescope, it sees photons in the infrared. If your eye was there instead of the telescope, you would see something, but you wouldn't see those pictures because those pictures come [00:39:00] in an invisible wavelength, if you like.

Literally, were looking at an image of the photons from the James Webspace telescope. It would just be black. You couldn't see it at all. They've shifted. In frequency so that you can understand it. And that's a literal example, but it's also a metaphor for everything we do in physics. Everything in physics is like that, you know?

What is a photon? Well, we try to describe a photon, which is a very strange thing we've never really experienced before or deeply understood in terms of things that we feel comfortable with. Oh, we, it's like a tiny little ball, so let's call it a particle. No, it's sort of more like the waves we see in water.

So we call it a wave. It's. And then sometimes we say, oh, it's kind of both, or it's a combination of the two. Really, it's neither of those. It's something else, something weird, something different. We can't ever really grasp, but we try to, we try to explain this new thing sort of in the basis set of the ideas we are comfortable with.

And so that's everything we're doing. And it's just depends on like what is the basis set [00:40:00] you need to translate to? Are you talking to another physicist and they have sort of the same toolkit in their mind, so you can just talk about those things. Or are you talking to somebody on the airplane and they don't have any of those ideas and you need to translate it into basic ideas that they are familiar with, that you can talk to them about.

So to me, that's the central project of physics is translate the universe into the human mind, but at various layers. Yeah, yeah, yeah, definitely. It's funny because I started, yesterday, I started finally reading, um, Stephen Ho's book, A Brief History of Time, and the, the first chapter is exactly like about that, like the, the motivation of physics and.

What that means to work, to be a physicist. So I found that differently. Interesting. With and echoing what you're saying right now. Oh man. Stephen Hawking is always ripping me off, you know? Yeah. , I, that must be frustrating, isn't it?

all the credit, man. Yeah. That's hard. I know. I've. [00:41:00] Just because he came up with these ideas before I did. Like he should get credit for them. I mean, is that how these things actually true? I know. Yeah. Well, it's, for me it's slap less like these guy, I have the same ideas as this guy just because it's later.

I don't get any credit. Like life is unfair. Life is unfair. Exactly. In another universe, you are a famous statistician and he has a podcast. Exactly. In that universe it's called Andorian Statistics . And we're talking with Daniel. We Son's Brief history of time. . I'd check out that podcast. Yeah. Yeah. I would check out Dead Lulo and Steven Hawking complaining about the two of us.

Yeah, let's hear that podcast.

Definitely. I really like that. And actually to go back to the, the paper you were talking about, so again, we'll put it in the show notes for uh, people who are interested because I have a very. Cool, nerdy [00:42:00] audience who loves papers and show notes. So definitely we'll put that here for you folks. And I'm wondering if there is one or two main difficulties that you encountered during that project, and most importantly, what you learned from those difficulties.

Hmm. Yeah, that's a good question. It was definitely not an easy project. It's one of these projects that we thought was gonna be pretty quick. We had all the tools, we had all the techniques, we had all the data. We're like, oh, that's gonna be a quick one. And then a year and a half later, we're still struggling to get the paper out.

It always comes down to understanding what your results are. And the results we got were sort of confusing at first. We discovered that. Difference between the dark matter jets and the normal jets depended on particles that were very, very slow moving. It seemed to be mostly not in the fast stuff, not the particles that were zipping around with a lot of energy, but the very slow moving stuff [00:43:00] with things we call soft, low momentum particles that really distinguished the dark matter jets and the normal matter jets.

And so we struggled for a while to try to understand why that was and to think about what that meant and to come up with some ideas. Because in the end, you can't just like do a paper and say, here are the results done. We're moving on. You have to explain them. You have to say why they make sense. You have to try to fit them into the scientific conversation.

Say, look, this is what we've learned. And so we were surprised by the results we saw, and it took us a while to come up with a, an understanding, a theory, at least a sketch of why it might be that the very, very slow moving stuff helped us distinguish the dark matter, normal amount of jets more. And it was also sort of discouraging because, The slow moving stuff is the stuff that we don't understand very well.

Quantum field theory describes how particles fly around and how they're generated, and in principle, a particle flying through the universe is always generating other particles. An [00:44:00] electron is never just by itself, it's surrounded by a cloud of virtual particles. Mostly. We can ignore these because they're very low momentum.

You know, there's an infinite, as you go down to smaller and smaller momentum, the probabilities generate a particle outta the vacuum goes up and up, and up until eventually it goes to infinity. It's zero momentum. And so you have like this infinite sum over very slow moving particles. Mostly you can ignore, but.

Because usually we are interested in the fast moving stuff and so to have your signal sort of buried in the slow moving particles meant that it was gonna be harder to understand because that's sort of like the swamp of quantum field theory. Okay. Yeah, that does sound complicated. , actually, it's so perfect segue because for this episode I asked my nerd crew, so my, my colleagues and my other open source developers friends, and if, if they had any stats physics questions for you and one of them then Ben Vincent is [00:45:00] ask me a bit more about, you know, that cat for quantum physics, like and in particular, like is it that the cat is literally alive and dead?

That you have a 50 50 belief that the cat is alive or dead and you just don't know. And then there was, there were a whole debate among the guys because some of them have some physics background. And so these guys were saying, yeah, actually that's just like, that's not really, um, that was more of a means to demonstrate the absurdity of the, the Copenhagen interpretation at the time.

And that's not really the good experiment to understand that there are. Experiments to understand that concept. So, okay. Can you help us here, tell us what this is supposed to explain in the end and how good experiment would look like to understand that phenomenon. . Wow. Right. The fundamental randomness of quantum mechanics.

Let's get into it. There was this revolution in the early part of last century where people discovered that our explanation of the universe as [00:46:00] mechanistic, that. Every experiment could be predicted by its initial conditions that if you knew enough about the particles in your system, then like Lelos demonn, right?

Or the Andorian Demonn could tell you what was gonna happen. And quantum mechanics says that that's not the case. That there is a fundamental random nature to the universe that you have the same initial conditions, you can get different outcomes. And that's kind of mind boggling, right? Because you expect if you throw a ball, you know, or you kick a soccer ball the same way twice, it will go the same direction.

That's your intuition. But quantum mechanical particles don't follow the same rules. And there's this probability distribution for what's gonna happen to. And so that didn't sit very well with a lot of physicists for a long time, and they thought maybe it's not the case. Maybe it's not really random.

Maybe there's some hidden information. There's something that's secretly determining what's gonna happen. We just don't know of it, right? And so it goes to the heart of [00:47:00] this sort of like basian versus frequentist question. Is it actually determined? But it's not. But it's just that we don't know enough information.

We don't have all the information, but in principle, some super intelligent alien. Could gather the information to predict these experiments. Or is it really actually random? In a thousand hypothetical universes, you do the same experiment, you actually will get different outcomes, right? So there's the Bay University frequentist approach for you.

And so people were wondering about this, and as you say, the folks who thought it was ridiculous to imagine that there was a random element to the universe came up with these examples, these ways to try to illustrate the absurdity of having a random element. And one of the, the hardest parts to grapple with is this idea that the universe can maintain an uncertainty until you've examined it, right?

So you imagine some particle has a probability to go left or to go right in some experiment that you've done. And the question is, is it actually going. [00:48:00] And actually going, right? And then when you look at it, you just learn that, or that the universe has kept those, kept that uncertainty, maintains the possibility of going left and going right until somebody asks the question.

And then it decides like, where is the universe invoking its random number generator. Right? And it's very uncomfortable to say like, hold on a second. Are you saying that it depends on when we ask the question, like universe is not just like operating on its own and we are, you know, peeking into it that we're actually like changing the answer by asking the question or that the question you ask depends the answer you get depends on the question you ask.

And to me that's really resonant with statistics, right? Because the answer you get really. Depend and statistics on exactly the question you're asking. And so, you know, there is this famous experiment, the double slit experiment, where it demonstrates that by asking a question about the particle, you are forcing the universe to [00:49:00] make a decision and you lose some of the quantum mechanical effects.

That can only happen if there is uncertainty. Um, but I think probably the most interesting experiment, the one that really puts a nail in the coffin of this idea that the universe has all this information, that it really is deterministic, is Bell's experiment, which has, which is again very statistical.

It introduces yet one other concept, quantum mechanically, which is entanglement. It says, Create some particle, like some electron and you know, that has a possibility to go to spin up or spin down, for example. And you know, we're talking randomness here. So we say the probability the electron has a 50% chance of being spin up and a 50% chance of being spin down.

But now create it with a pair, create it with a partner, another particle that has to have the opposite spin, you know, so electron number one can be spin up or down, and electron number two can be spin up or down, but they have to be opposite. So if electron one is spin up and electron two is spin down, [00:50:00] then electron two has to be spin down and vice versa.

And the interesting thing about this is take those particles and now separate them over very, very long distances. So this is Einstein's thought experiment says if quantum mechanics is really random, if it's maintaining the randomness of both electrons simultaneously, then something really weird is gonna happen, which is you pull these guys.

You bring them like a light year apart and you look at one of the particles, and if quantum mechanics is really random, then you're telling me that it's uncertain which spin this electron has until I look. But now the other one I know has to have the opposite spin. So if I look at electron one, then electron two, suddenly across light years of space instantaneously goes from being uncertain to having to have the opposite spin of electron one.

And so this weird sort of action at a distance was very strange for Einstein, and he thought it can't possibly work that way. It's one of the great examples of somebody saying, look, [00:51:00] here's something that quantum mechanics predicts, which is bonkers and therefore shows this the quantum mechanics is wrong.

But then people go out and they do the experiment and they confirm the bonkers prediction. They're like, oh yeah. It turns out actually the universe is bonkers. And so all is good with the theory. The problem is just our digestion of it. And so there's a series of experiment bells experiments, which try to tell the difference between the two scenarios.

One is that both particles really are uncertain until you look at either one of them that the universe is like keeping. Its is not determined yet which one is up and which one is down until somebody asks the question and collapses the possibilities. And the other possibility is that they somehow have some information that they've always been spin up or spin down until you look.

And Bell's experiment is a very, very clever way to try to disentangle these two possibilities by rotating one of the particles, a random number of degrees, and using correlations between the spins at those different angles to try to [00:52:00] disentangle it. It's a very subtle experiment, and so to me, that's one of the.

The most interesting experiments in the history of, of quantum mechanics, and one of the most surprising because it shows us that the universe really is fundamentally different from the way that we thought it was. So in the end, the answer is the first option, right? It's definitely undetermined. Until you look inside the box in a way.

Yeah. And so the answer is that it's not determined by some little piece of information. The electron carries with it. It's not like there's some little pocket where the electron has some detail in it that's gonna determine it, and it just waits until you ask a question to tell you. It's consistent with it being random and only collapsing at the last moment.

There's actually, there's a lot of loopholes to this experiment, a lot of wrinkles, and one other alternative. Is that it is deterministic, but that there's some like global universe spanning function called the pilot wave, which is organizing everything [00:53:00] across space and time and global and instantaneous.

So the most accurate way to describe Bell's experiment is to say that it rules out any local hidden information. It doesn't rule out a global hidden information, something which is organizing the whole universe. And Bell himself actually thought that that was the best way to interpret the experiment.

He said the quantum mechanics is non-local, that there's something weird, you know, when you're making a measurement here, it affects things over there instantaneously that convinced him that there's this pilot wave spanning the whole universe. It's called boian mechanics. It's a whole different view of the same experiments.

But yeah, it's also consistent with, uh, the, with quantum mechanics being purely random, uh, being really probabilistic, which is, you know, hard to get your head around. Uh, that being the fundamental nature of the universe. Yeah, definitely. And I can see that also in like the models I work on are probabilistic and it's incredibly hard to explain people who are not at all in the, in the statistics field [00:54:00] that yes, things are probabilistic also, sometimes it's just not that you don't have enough information.

It's like sometimes it's really probabilistic and that's really interesting. Our brain really wants things to be deterministic. Christ. Um, like even Einstein's brain wanted that to be true. Yeah. , it's actually super interesting to hear that he was really not believing that. And actually, when was the Bell experiments?

When were they done? So they were proposed in the sixties by Bell, and the first versions of them were done in the seventies, but they're very hard to do. And so people have developed better and better versions like closing more and more loopholes. They're still doing 'em. I think the latest one I read was from 2015 where they have the two particles.

Really, really far apart because you wanna make sure they're super far apart. So there's no time for like light or any sort of information to communicate between them. You wanna look at them in a way that, uh, communi instantaneous communication would have to be faster than light in order to [00:55:00] somehow coordinate between them.

So it's a whole area study. I'm really interested in what you said a minute ago about people not being familiar with random. I feel like a lot of people think they are familiar with it. Like they think, well, a dye is random, or a coin is random. Right? Or the weather is random. But those are actually not great examples because those are situations where if you did know enough, yeah.

If you knew exactly how somebody flipped the coin or rolled the dye, you could predict it. Right. It's apparently random and in our lives we have almost no experience with true randomness. Right. All these things are pseudo random. Even the random number generator on your computer right, is not actually random.

Exactly. And it's like, what? What does that mean? It's purely random. It's just like, it's just like, I think I always forget, but I think in the statistics world, we call that epi epistemological uncertainty, which basically means, well, it's. There is the knowledge somewhere. It's just that we don't have that knowledge and we have to describe it with degrees of uncertainty, [00:56:00] but the knowledge is out there.

Here. What the experiments show is that no, actually the knowledge is not out there until you look for. Something for that electron or that particle. And that's pretty amazing. It's really strange to try to wrap your head around this idea. You do the same experiment twice. You like prepare it exactly the same way.

Maybe you shoot an electron at a target and you can get different outcomes. It can come off at a different angle every time. And it makes you wonder like why this angle, this time, what is determined that, what process is choosing that is the universe have in it some like source of random numbers it's pulling from like that's crazy.

But it's also really useful if it really is random because we need random numbers in all sorts of applications right? In uh, you know, making codes and in cryptography and stuff. And so I know folks who try to use like cosmic rays, which are quantum mechanical to try to generate real random numbers. Yeah.

So actually do we know more about that? Because I'm guessing it's a question like that's [00:57:00] very important physics from like how do we understand that randomness? Like and because there is still the possibility, as you were saying with, I don't remember the wave or something like the name of the wave there is actually still, it's coming from a knowledge that's out there, but we still don't know.

Or like, is it true randomness? So I'm guessing that it's an active field of research. So what basically do we know about? We don't really know very much. We have these different interpretations of these experiments. One of them is the Copenhagen Interpretation that says things really are random until you look at them.

But that's really problematic because nobody really knows what we mean by and then you look at them. Yeah, because for example, these quantum mechanical particles, they can interact with other quantum mechanical particles and not disturb their probabilities. They can maintain the uncertainty when interacting with other quantum mechanical particles, but they can't maintain that uncertainty when they interact with you or your eyeball.

You can't see something quantum mechanically. You can't say like, oh, this photon hit [00:58:00] my eye, and it might be here and it might be there. You don't like see the different probabilities. You see one photon and what we don't understand about that, Well, your eye is made of quantum mechanical particles.

Everything is, so where is the difference? Where is the threshold? Where does something become classical, where it forces the universe to collapse it? And where does it stay? Quantum, mechanical, it's not well defined, and so that's a big problem. And another theory is this boian mechanics that says, well, it actually is all deterministic.

It's just organized by this crazy global pilot wave. That's not a very popular theory, mostly for historical reasons, because there was a proof by John Von Nouman that ruled it out, but it turned out that proof was wrong. And just because Von Nouman was such a genius and so influential that it sort of buried that whole idea for decades before.

Bell actually is the one who dug it back out of the weeds and tried to make it popular again. And then there are other theories called like many worlds quantum mechanics that says all of those things happen when there's a probability for something to happen. The universe [00:59:00] splits and every possibility happens in a different universe.

Oh, oh my God. It would be so amazing. . Yeah. And in that universe, it's really hard to even define. What do you mean by probability? Because. Everything happened, right? Everything happened. So everything has a unit probability. So those folks have a sort of a hard task to define, like how do you, what do you mean by the probability for this to happen or that to happen when everything is going to happen?

So the answer is that we're, you know, we're at that moment where people are gonna look back at quantum mechanics in a hundred years or 500 years and be like, what were they thinking? They just have to wash their hands. You know, the answer is probably so obvious and right in front of us. We just are not thinking about it the right way.

And somebody out there, maybe one of your clever listeners, is gonna put it together and, uh, help us understand it. Yeah, well please do so and then if it's because of us, then definitely mention that episode in your Nobel Prize acceptance. That'd be the like the least you can do. Yeah. Damn, that's amazing.

So it's like, and that would be incredible that the answer is [01:00:00] obvious in a way, right? Because like this would be a huge answer to big mystery that we still have about how universe works. That would be amazing that the answer would be like, oh yeah, of course. Why didn't I think of that? Yeah. Well, you can often do that in the history of science.

Sometimes people make discoveries based on data that's already out there. Like Einstein won his Nobel Prize for the photo electric effect, explaining that photons really are a little quant of energy based on experiments. He didn't do. He just read. Somebody else could have read about them and made the same ideas and you know, won the Nobel Prize.

And so it's sometimes it really is true that the information is there. You just need somebody to come along with the right idea. Yeah, I mean, that's also a basis of the idea that knowledge is a community that. Personal knowledge is kind of an illusion because we are all participating in that community and building it like, and that actually the ideas of creativity, even from Einstein, you can already see them in some [01:01:00] writings before from , for instance, where you have already some formulas talking about that.

But then I just like, it's an idea of the time and it builds up then, and then someone with, yeah, an amazing intelligence comes and unifies all that, but it's kind of already there. I find that pretty incredible because also it's important, I find one of the most difficult things when you do science education is not to turn some scientists into heroes, because then, and that's also kind of the problem of the Nobel Prizes, right?

Um, because then that turns those people into profits and well, that's not good. And it's something that you often say, right? Um, that if Einstein came back to us and was like, oh, actually Thes is flat. Well, you should not believe him. Even if it's Sunshine . That's right. And I always hear, I often hear Nobel Prize winners talking about things they don't know anything about because people think, oh, you want a Nobel Prize to physics?

Let's [01:02:00] ask you about climate change, or let's ask you about education policy. It's like, well, these people don't know anything about that. It doesn't make them geniuses in everything to win the Nobel Prize. Yeah, yeah, yeah. And even in their old. Yeah, so that's important for sure. I mean, but I wanna be, um, mindful of your time because we could still talk about that.

I have so many more questions, but before closing up the show though, I'm curious about these experiments, these bail experiments. So did Einstein learn about those or was it dead already? Did you have the result? Like, did you discover that actually he was wrong on that and, and the universe was actually much more random than he thought?

No, Einstein died unfortunately, before Bell wrote these papers and saw these experiments. I would be really curious to know what Einstein thought of these experiments. You know, probably he would respond with like, oh, that's interesting. Here's another even crazier set of experiments for you guys to go do.

But no, unfortunately we won't know what Einstein thought about these experiments. I see. It's a cool alternative history. Somebody should [01:03:00] write that science fiction novel. Oh yeah, for sure. Yeah. And so last question about that one. How do you do that? Like how do you see those particles? Right. Tell you, tell us quickly what does that mean to have two particles like that who are bounded and how do you identify them first?

You know, like can you make an error, like thinking, oh, particle A here has to be bounded with particle B. Can you make an error in these bound? Like, and actually you took another one, uh, which was been spinned up, but actually you needed another one because that one was not bounded with the first one.

What does that mean? And so you can feel free to like give a general answer and if you have resources for people to go and read about those experiments. Also, like I know some YouTube videos or things like that. Feel free to also share with the listeners and we'll put that in the short. Yeah, that's a great question.

These correlations between the particles like particle A and particle B, having opposite spins, they come [01:04:00] from a local process like they are created that way. So you take, for example, a photon and you let it decay to an electron and a positron, and you know that the spin of the electron depository together have to equal the spin of the photon.

So you create this entanglement, this condition, this constraint. On the particles because of the initial conditions of your system. So you just not like that. You're going out there looking for particles and saying, I think this one might be connected to that one. You're creating this condition, you're instilling this correlation in them from how you've created them.

And the trick then is to keep them isolated, is to create these particles and not let them interact with anything until you are ready to make your measurement. And the way that you can measure the spin of a particle is that a particle, if it has electric charge, it spin, gives it a tiny little magnetic field and you can pass it to a magnet and see like, does it go left or does it go right?

And that'll tell you whether it's spinning. In one way or spinning the other way. And this whole idea of quantum spin is also very confusing. [01:05:00] You know, this is a topic we talk about. It's a property of a particle that's related to actual spin, but it's not like literally that the particle is a little planet that's spinning the way you probably envision in your mind because they've done the calculation and if that were the case, the surface of it would have to be going faster than the speed of light.

And so that's how you actually measure these particles. You, you see, you pass them to a magnet and you see which direction they. Fascinating. That's awesome. The universe is amazing. Cool. Do, do you have any resources for people that you can point them to? Actually in our podcast, Daniel Jorge explained the universe, we just tackled this and did a whole podcast episode trying to pull this apart and talking about the various interpretations of it in great details.

But there are lots of great explainers out there is if you just type Bell's experiment into YouTube, you can see there's a nice video from PBS Space Time and lots of other folks. Okay, perfect. So definitely I put that in in the show notes. Cool. So before asking you the last two questions [01:06:00] that I always ask people quickly, have you heard of Quantum Bism?

Quantum Bism? No. Me neither. That sounds super fun. . Me neither. And it was like, it's Ben also like from , the, the company we work at, uh, like, and he was like, you need to ask Daniel, but quantum bism, I dunno what that is. And yeah, me neither. So, Currently you don't either, so that's good to know. . Okay, so before letting you go, Daniel, I'm gonna ask you the last questions I ask every guest at the end of the show.

So the first one is, if you had unlimited time and resources, which problem would you try to solve? . Wow, unlimited resources. That's pretty amazing. Unlimited resources and time. I love that. This is actually something I do with my research group to try to teach students to be creative about their experiments.

To say like, what question would you like to answer? No [01:07:00] limits on funding or No practical boundaries. And they come up with some pretty crazy stuff, I think. You know, one of the deepest questions in the universe is about whether what we are discovering, is it something we're revealing about the universe, or is it something that's just part of the human mind?

And to me, I want to answer that question by talking with alien physicists and saying like, Do you guys see particles also? I mean, this h goes on thing. Is that really a thing or is that just part of the way we tell stories about the universe? So infinite time and money launch a fleet of artificial intelligence powered self replicating robots that expand and, and reproduce to explore the entire galaxy, to find alien intelligence that's technological and ask them questions about their theories about the universe so that we can compare them with ours.

And also so we can, you know, jumpstart, like maybe we can [01:08:00] find some species that's been doing physics for a billion years and we can just like fast forward our knowledge or maybe we discover. That, you know, the alien mind asks such different questions that we can't even gro the questions they're asking, not to mention the answers that they have found.

I think we would learn a lot about, uh, sort of the nature of the universe and our relationship with it. If we could visit all those alien species and ask them questions about how they think about the universe, that would be my personal way to spend an infinite amount of money and. Nice. Yeah. I am not surprised that you are answering something related to aliens , that that's perfect consistency on your part.

Thank you. And so, second question, if you could have dinner with any great scientific mind, dead life, or fictional, who would it be? Hmm. That's a really hard question. There's so many good options. I think for me, one of the most interesting things [01:09:00] is discoveries that seem obvious in hindsight. You know, like we look back at history and be like, oh, well I could have done that, or I could have figured that out.

That seems obvious, and it seems obvious to us because we are in the future of it. Not just because somebody had the idea like, oh, I could have had the idea for Spider-Man. It's because the idea has so changed the way that everybody thinks that it's just like now inherent in the way we're thinking. So for example, I'd like to talk to Galileo because Galileo did something really amazing, which is he disproved Aristotle's physics, which had stood for thousands of years.

He did it in an a. With like, you know, a couple of sticks and a ball and he did it just by actually going out there to test it. He just like did the experiment. Aristotle had these ideas about physics and he thought he could derive the way the universe worked just by thinking about things. He didn't have a need to go out and actually try them out.

And Galileo was like, well let's go do the experiment and see if this works. Oh, turns out [01:10:00] Aristotle, you were wrong. So like in an afternoon, topple an idea, which is thousands of years old. Like what is that like to. In that moment of transition where you come up with a new way to explore the universe and to develop ideas.

So, you know, he sort of straddles that boundary. He's like in the old world, grew up with the Aristotelian ideas about physics, but using new ideas now to explore the universe and make shocking discoveries. I'd love to hear about what that's like to sort of be on the boundary, what kind of crazy intellect it takes to come up with the bravery to try something so radical and yet so easy.

Yeah, me too. Make sure to invite me to that dinner. . I'll bring my microphone along. We'll record it. Oh, perfect. And we'll eat some uh, Nutella . Perfect. Yeah. Leo needs to discover Nutella . Sure. Well, it's time to call it a show in France because we don't have a [01:11:00] limited time for. Podcast episode. But thanks a lot, Daniel.

As expected it was, uh, fascinating and passionate. I really hope that listeners learned as much as me. I really encourage them to check out your podcast. Daniel and Jge explain the universe. I personally binge listen to it, so definitely go there. People also your book with, uh, we have no idea. I really loved it.

And um, I still remember when someone introduced it to me, I was in Buenos in a really, really good Paris, so, um, steakhouse, and that guy told me, oh man, Like used to read my favorite book of all time and then he pull up. We had no idea. Oh, that's so nice. Well, if any of your listeners have questions about anything we've talked about, they're very free to email me.

I'm Daniel uc.edu. I answer all my emails, so if you have a question about what we talked about or physics in general or the universe, feel free to reach out. Yeah, [01:12:00] please do. As you heard, Daniel is amazing at explaining physics, so as usual, I put resources and a link to your website in the show notes for those who wanna dig deeper.

And thank you again Daniel, for taking the time and being on this show. Thank you very much for having me. Been a lot of fun. Well, you bet. Well come back anytime. This has been another episode of Earning Patient Statistics. Be sure to rate, review and subscribe to the show on your favorite PGA or chaser and visit learn based stats.com for more resources based on today's topics as well as access to more episodes that will help to reach true patients that of mine.

Let's learn base stats.com. Our theme music is Good. Patient by BBA Bringman at MCRs and mega, check out his awesome work@bbabringman.com. I'm your host Aand Laura. You can follow me on Twitter at Alex Elders and do the country. You can support the show and unlock Stu benefits by visiting [01:13:00] patriot.com/learn.

Astep. Thanks so much for listening and for your support. You're truly. Good ba and change your predictions after taking information. And if you thinking not be less than amazing, let's adjust those expectations. Let me show you how to be a good change calculations after taking fresh data in those predictions that your brain is made.

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