Events at Physics |
Events on Monday, February 24th, 2025
- Plasma Physics (Physics/ECE/NE 922) Seminar
- "Radiatively-cooled Magnetic Reconnection Experiments on the Z Machine"
- Time: 12:00 pm - 1:15 pm
- Place: 1227 Engineering Hall
- Speaker: Jack Hare, Cornell University
- Abstract: Magnetic reconnection is a fundamental plasma process which explosively dissipates magnetic energy and changes magnetic topology. In many astrophysical plasmas, such as the solar chromosphere, the interstellar medium, and pulsar magnetospheres, the heated plasma rapidly radiates away thermal energy in the form of high energy X-rays, leading to cooling instabilities including the complete radiative collapse of the reconnection layer. Analytical theory by Uzdensky and McKinney suggests this collapse process dramatically accelerates the reconnection rate, and simulations suggest that the plasmoids formed through the tearing instability are the regions of strongest emission within the reconnection layer. In this talk, I will present results from experiments designed to study radiatively cooled magnetic reconnection in the laboratory. Using a suite of diagnostics including X-ray and optical imaging, spectroscopy, magnetic probes, and laser shadowgraphy and interferometry, we demonstrate the formation of these bright plasmoids and their subsequent rapid cooling and radiative collapse.
- Host: Prof.Adelle Wright
- NPAC (Nuclear/Particle/Astro/Cosmo) Forum
- Finding Neutrinos: Advancing Neutrino Detection, Reconstruction, and Analysis
- Time: 4:00 pm - 5:00 pm
- Place: 5280 CH &
- Speaker: Dr. Jessica Micallef, Institute for Artificial Intelligence and Fundamental Interactions
- Abstract: Neutrino oscillation, or flavor changing between the neutral leptons, has indicated that neutrinos do not fit as perfectly into the Standard Model puzzle as they were first predicted. Improving measurements of neutrino oscillation and properties are important to help us better understand the Standard Model, and thus how these fundamental particles influence our universe. To successfully complete their goals, future experiments aiming to make decisive measurements need results from the new technology and methods used by current experiments and prototypes. Machine Learning (ML) is one such tool that particle physics has begun to employ that can tackle new challenges facing neutrino experiments. I will discuss how my work with ML will help the success of one of the largest, future neutrino physics experiments--the Deep Underground Neutrino Experiment.
- Host: Sridhara Dasu