Machine Learning’s Promise Beyond Optimization

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From the electrifying environment of high-speed particle collisions to the challenge of sifting signals from heaps of experimental noise, you'll hear how Prof Whiteson and his team are pushing boundaries. They discuss bold new algorithms capable of spotting non-standard tracks—think wild trajectories that defy classical expectations and could reveal surprises nature has kept hidden. Practical questions about detector design, efficiency, and even the mathematics of “smooth” particle paths make for a rich, dynamic dialogue.

If you’re curious about how physicists ask the universe its most challenging questions, the frustrations and breakthroughs of innovation, and the fascinating interplay between theory and experiment, this episode will take you to the front lines of discovery. Plus, hear how machine learning might help us find not just the next weird particle, but perhaps the next Nobel-worthy revelation. Get ready for a fascinating journey into the impossible!

Daniel Whiteson is a physicist whose research spans a wide range of topics at UC Irvine. By day, he works on the ATLAS experiment, one of the major physics collaborations at the Large Hadron Collider, where he contributes to Higgs boson precision measurements and develops advanced techniques in machine learning, data acquisition, and trigger systems. His research group is known for applying machine learning innovations to physics problems, including projects beyond ATLAS—like using approximate symmetries or jet pattern matching. Recently, his team has been focused on machine learning projects to identify unusual particle tracks, always pushing the frontier between physics and data science.

Timestamps:

00:00 Revisiting Discovery with New Tools

04:43 Particle Tracking Constraints Explained

06:56 Challenges in Non-Helical Track Detection

10:29 Non-Helical Tracks and Dark QCD

14:38 "Track Reconstruction and Efficiency"

18:43 Quirk Detection and Reconstruction"

23:27 Testing Generalization Beyond Memorization

25:23 Quirk Tracks and Overlap Analysis

30:36 "Smooth Paths and Signal Control"

31:17 "Training Pipeline on Weird Tracks"

35:55 Filtering Standard Model Tracks

38:24 "Challenges in Parameter Optimization"

43:15 "Neural Networks Learn Complex Mappings"

44:38 "Machine Learning for Track Detection"

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