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SUMMARY:Machine Learning - the future of particle physics?  - Nadya Cherny
 avskaya (CERN)
DTSTART:20220927T150000Z
DTEND:20220927T160000Z
UID:TALK172004@talks.cam.ac.uk
CONTACT:William Fawcett
DESCRIPTION: This year marks the 10-year anniversary of the Higgs boson di
 scovery. Since the start of data-taking at the LHC\, it has been a long an
 d complex journey\, delivering further triumph to the Standard Model. Desp
 ite numerous searches for new physics\, it remains elusive. With the futur
 e high energy physics experiments planned for decades ahead\, we need to a
 sk ourselves a question - where can we further innovate\, and what might w
 e have missed? In this talk\, I will show the tremendous challenges that l
 ie ahead of us at the High-Luminosity LHC\, and I will argue that machine 
 learning (ML) can help us solve them\, while furthermore freeing resources
  for new ideas. With examples from state-of-the-art research\, I will demo
 nstrate how deep learning can improve\, speed up\, and optimise each stage
  of the data collection and analysis workflows at the LHC while extending 
 the experimental sensitivity. Finally\, by showing the physics impact of t
 he ML solutions\, I hope to convince you that machine learning is not only
  the past and present of particle physics\, but it has to be the future as
  well.
LOCATION:Ryle Seminar Room
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