Investigación y desarrollo · Seminarios
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.