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SUMMARY:Machine Learning - the future of particle physics?  - Nadya Cherny
 avskaya (CERN)
DTSTART:20221101T110000Z
DTEND:20221101T120000Z
UID:TALK184964@talks.cam.ac.uk
CONTACT:William Fawcett
DESCRIPTION:This year marks the 10-year anniversary of the Higgs boson dis
 covery. Since the start of data-taking at the LHC\, it has been a long and
  complex journey\, delivering a further triumph to the Standard Model. Des
 pite numerous searches for new physics\, it remains elusive. With the futu
 re high-energy physics experiments planned for decades ahead\, we need to 
 ask ourselves a question - where can we further innovate\, and what might 
 we have missed? In this talk\, I will show the tremendous challenges that 
 lie ahead of us at the High-Luminosity LHC\, and I will argue that machine
  learning (ML) can help us solve them\, while furthermore freeing resource
 s for new ideas. With examples from state-of-the-art research\, I will dem
 onstrate how deep learning can improve\, speed up\, and optimise each stag
 e of the data collection and analysis workflows at the LHC while extending
  the experimental sensitivity. Finally\, by showing the physics impact of 
 the ML solutions\, I hope to convince you that machine learning is not onl
 y the past and present of particle physics\, but it has to be the future a
 s well.
LOCATION:Ryle Seminar Room
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