Investigación y desarrollo · Seminarios

A Review on Machine Learning:

For Neutrino Experiments

Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds, elusive signals, and small statistics. In this seminar I will discuss how state-of-the-art machine learning tools have been used to solve analysis tasks, having major impacts to these challenges in neutrino experiments. I will also present a number of the roadblocks, both human and computational, in applying these techniques. Finally, I will discuss the challenges that still exist in the application of these techniques and how they are critical to their proper and beneficial utilisation for physics applications.



Karl Thomas Warburton


12 de noviembre de 2020

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A Review on Machine Learning

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