[Theory-seminars] Theory Seminar Monday (remote) - Yi Chen

[Zheng-Yang Li]

Dear all,

On Monday, May 12th, 2025 at 1:00PM EDT, we will have a virtual seminar given by Yi Chen of Vanderbilt University on the following Zoom link:

https://jlab-org.zoomgov.com/j/1619803255?pwd=AgMEd0JYWBs0MZa4LvbDa5ZecSO6EK.1

Although it’s not an in-person seminar, you can also join your colleagues for a remote streaming of the seminar in CEBAF Center F324-325.

Please see below for the title and abstract.

Theory Seminar: May 12th, 1pm EDT (virtual)

Speaker: Yi Chen (Vanderbilt University)

Title: Systematic studies of complex systems with the Bayesian framework: ultrarelativistic heavy-ion collisions as an example


Abstract: As the complexity of the systems we seek to study increases, extracting their specific properties becomes increasingly challenging. Bayesian parameter extraction is a robust statistical technique that uses Bayes' theorem to estimate model parameters by incorporating both prior information and observed data. This technique not only facilitates parameter estimation but also extends to a variety of advanced applications, including model comparison, model combination, sensitivity analysis, experimental design, and more.

Ultrarelativistic heavy ion collisions are a prime example of such complex systems. These collisions, occurring at nearly the speed of light, generate extreme conditions that resembles the state of the universe microseconds after the big bang, and they produce a wealth of intriguing phenomena. Over the past decades, extensive measurements have been conducted on these collisions, resulting in an ever-growing volume of data. Interpreting this large volume of data cohesively presents a unique challenge. In this seminar, I will use a recent study that employs Bayesian parameter extraction to investigate ultrarelativistic heavy ion collisions as an example. I will discuss the methodology, techniques involved, and the insights gained from this exercise.

Furthermore, I will explore various applications of Bayesian analysis beyond parameter extraction. These applications demonstrate the versatility of Bayesian methods and their potential to contribute to diverse fields of study, from physics to other scientific fields and beyond.

See you on Monday.

Best regards,

Joe, Zheng-Yang, Gloria and Adam
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