AI-powered
podcast player
Listen to all your favourite podcasts with AI-powered features
The podcast episode takes place at CERN, the heart of Particle Physics research. The host visits Kivinkive, a doctoral candidate in Particle Physics, to explore topics such as Particle Physics, Dark Matter, Dark Energy, and Machine Learning. They discuss the purpose of Atlas, one of the experiments at CERN, and its role in understanding the properties of particles. The episode also delves into the challenges of dealing with pileup interactions and the statistical models used to mitigate their effects. Overall, the episode provides an in-depth look at the research and discoveries in the field of Particle Physics.
In order to study the remnants of collisions and understand what happens beyond the Standard Model, it is crucial to reconstruct particles. This involves measuring the properties of electrons, photons, muons, and quarks that are produced in collisions. By analyzing the properties of these particles, including their energy, momentum, and direction, researchers can infer what occurred in the collision and gain insights into the fundamental workings of the universe.
Pileup interactions, which occur when multiple proton-proton collisions happen at once, present a significant challenge in experimental research. With a higher pileup rate, there is a greater chance to observe interesting physics but also an increased amount of uninteresting pileup interactions. Research efforts are focused on mitigating the effects of pileup interactions through statistical modeling and improving the accuracy of reconstructing jets, which are complex signatures involving quarks. As pileup rates are expected to increase in future runs of the Large Hadron Collider, upgrades to particle detectors and data processing systems are being pursued.
One of the major mysteries in physics is the elusive nature of dark matter. Although dark matter does not interact with electromagnetic radiation and cannot be detected directly, its presence is inferred through its gravitational effects on visible matter. The computations of galaxy rotations and other gravitational interactions indicate that dark matter constitutes a significant portion of the universe's mass. Dark matter remains an open question in physics, and researchers are eagerly seeking to uncover its composition and properties.
One of the main focuses of the podcast is the search for dark matter. The speaker discusses the challenges in detecting and studying dark matter, as well as various methods being used, such as collider experiments and direct detection. The hope is that by measuring properties of particles like the Higgs boson with higher precision, we may find deviations that could indicate interactions between dark matter and regular matter. The ultimate goal is to gain a better understanding of the nature of dark matter and its role in the universe.
The podcast episode explores the issue of whether antimatter interacts with gravity differently than regular matter. While antimatter is produced in the same ratio as matter in collider experiments like the LHC, it is difficult to study due to its short lifespan. Experiments in the anti-proton decelerator are aimed at measuring how antimatter interacts with gravity compared to matter. The hope is to find any deviations that could provide insights into the asymmetry between the abundance of matter and antimatter in the universe.
In a hypothetical scenario with unlimited time and resources, the speaker expresses the desire to further advance the search for dark matter. This would involve funding projects for designing next-generation colliders and detectors, as well as supporting accelerator physicists and computing resources for simulations and data analysis. The ultimate goal would be to improve precision in measurements and potentially discover deviations from the standard model, leading to a better understanding of the universe and potentially solving the mystery of dark matter.
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
This is a very special episode. It is the first-ever LBS video episode, and it takes place in the heart of particle physics research -- the CERN 🍾
I went onsite in Geneva, to visit Kevin Greif, a doctoral candidate in particle physics at UC Irvine, and we walked around the CERN campus, talking about particle physics, dark matter, dark energy, machine learning -- and a lot more!
I still released the audio form of this episode, but I really thought and made it as a video-first episode, so I strongly recommend watching this one, as you’ll get a cool tour of the CERN campus and some of its experiments ;) I put the YouTube link in the show notes.
I hope you'll enjoy this deep dive into all things physics. If you have any recommendations for other cool scientific places I should do a documentary about, please get in touch on Twitter @LearnBayesStats, or by email.
This was literally a one-person endeavor — you may have noticed that I edited the video myself. So, if you liked it, please send this episode to your friends and colleagues -- and tell them to support the show on Patreon 😉
With enough support, that means I'll be able to continue with such in-depth content, and maybe, maybe, even pay for a professional video editor next time 🙈
Enjoy, my dear Bayesians, and best Bayesian wishes 🖖
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, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, 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, 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, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau and Luis Fonseca.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
Some physics (and physics adjacent) books Kevin enjoys:
Listen to all your favourite podcasts with AI-powered features
Listen to the best highlights from the podcasts you love and dive into the full episode
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
Listen to all your favourite podcasts with AI-powered features
Listen to the best highlights from the podcasts you love and dive into the full episode