Michigan Tech Events Calendar

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Physics Colloquium - Graduate Student Presentations (Kayali, Nguyen)

This is a past event.

Thursday, January 8, 2026, 3:30 pm– 5 pm

This is a past event.

Please join physics graduate students, Ogetay Kayali and Khoa Nguyen for their presentations on Thursday, January 8 at 4 PM - Fisher Hall 139.

Ogetay Kayali (Advisor: Robert Nemiroff)

REINVESTIGATING VAVILOV-CHERENKOV RADIATION (VCR) THROUGH RELATIVISTIC IMAGE DOUBLING (RID)

Vavilov-Cherenkov radiation (VCR, commonly known as Cherenkov radiation) occurs when a charged particle travels through a dielectric medium at a speed exceeding the phase velocity of light in that medium. Since its discovery in the 1930s, its well-defined emission angle and prompt, directional light yield have made it a cornerstone of particle detection, widely used for velocity measurement, particle identification, and high-energy astrophysics. In this work, we reinterpret VCR through the framework of RID, a simple kinematic effect arising from light-travel-time delays when a source appears superluminal to an observer. Although RID has been directly observed in controlled laboratory settings, its implications have not been systematically explored in many
experimental contexts where it naturally arises. Here, in the context of VCR, we show that radiation detected at a single observation time can originate from multiple emission points along the particle's trajectory, producing paired forward and backward apparent images. We discuss how the familiar Cherenkov cone emerges naturally as the caustic envelope of these image solutions, and reinvestigate the coherent and incoherent emission behavior near and within the cone to explore previously unexamined characteristics of VCR.

Khoa Nguyen (Advisor: David Nitz)

IMPROVING PHOTON DETECTION FOR PIERRE-AUGER OBSERVATORY AT 10¹⁶ – 10¹⁸ EV ENERGY RANGE

This project aims to develop advanced photon triggers for the Pierre Auger Observatory's Surface Detector array, targeting the challenging 10¹⁶ – 10¹⁸ eV energy range. Using machine learning discrimination of PMT trace data from water-Cherenkov detectors, we aim to achieve 50% improvement in photon detection efficiency while reducing hadronic background triggers by 30%.

 

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