Topic of the Week

Sunrise - WH11NE (Fermilab)

Sunrise - WH11NE


Jamie Antonelli (The Ohio State University (US))
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Jamie Antonelli
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    • 15:00 16:00
      How much information is in a jet? 1h

      Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. In this talk, I will review machine learning at the LHC and discuss a systematic and constructive approach to extracting the information necessary for discrimination. By measuring observables on jets that completely and minimally span N-body phase space, we are able to reduce the problem in a controlled and theoretically well-defined way. For the application of discrimination of QCD jets versus boosted hadronically-decaying Z bosons, we show that discrimination power is saturated by only considering observables that are sensitive to 4-body (8 dimensional) phase space.

      Speakers: Andrew Larkoski (Massachusetts Institute of Technology) , Andrew Larkoski (Reed Collge) , Andrew Larkoski (Harvard University) , Andrew Larkoski (SLAC)
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